If you are thinking about applying to graduate school in the future, it is essential that you prepare yourself for some standardized tests. These assessments assess your skills and knowledge across various fields and can help strengthen your application package.
The logic-based exam for graduate students tests your comprehension of logical principles. Additionally, it evaluates your analytical writing and verbal reasoning abilities.
The Graduate Record Examination (GRE) is the most widely administered graduate school admission test. It measures your capacity for critical thinking, analytical writing, verbal reasoning and quantitative reasoning skills as well as critical thinking and analytical writing capabilities.
The GRE exam consists of two sections – the General Test and Analytical Writing Section – designed to test your logical reasoning skills. Each section tests specific topics and concepts through multiple-choice questions.
Each test has a distinct format, and the General Test includes four sections: Verbal Reasoning, Quantitative Reasoning, Analytical Writing and Integrated Reasoning. Additionally it includes Data Sufficiency and Problem Solving questions which assess your ability to analyze and resolve an issue.
Before you begin studying for the GRE, be sure to understand logic’s fundamental principles and how it applies to mathematics. Doing so will enable you to get the most out of this exam.
Logic requires an intense amount of study, and graduate students often take multiple courses before taking the logic qualifying exam. Among other topics, they learn about model theory, proof theory, and recursion theory.
An important component of logic is set theory, which describes how formal proofs interact with natural language structure and behavior. This relationship is particularly crucial in computer science and philosophy contexts.
Graduate students wishing to gain proficiency in set theory typically enroll in Math 220ABC, a one-year sequence of courses that presents the fundamental principles and their practical application. Furthermore, they attend colloquia hosted by faculty members of the Logic Group to stay abreast of new research directions within logic.
In addition to logic courses, UCLA graduate students take a variety of other classes as well. These include various math and physics classes as well as some other graduate-level offerings from other departments.
Many grad schools require students to submit the Free Application for Federal Student Aid, or FAFSA, in order to be eligible for financial aid. The earlier you submit your application for aid, the greater your chances are of being able to afford school.
Sets are one of the fundamental concepts in mathematics and have long been a focus for research by mathematicians. Without an understanding of sets, other mathematical concepts such as relations, functions and sequences would become incomprehensible.
Set theory is the study of the properties of well-defined collections of objects. These can range from mathematical objects such as numbers or functions to non-mathematical ones like rivers or colors in a rainbow.
Set theory is an axiomatically defined subject, where sets are given their basic properties by applying certain axioms. As these concepts are so general, virtually all mathematical objects can be construed as sets – making it a highly relevant area in modern mathematics.
Set theory is an incredibly sophisticated field, though seemingly straightforward at first glance. It has allowed us to formalize almost every aspect of mathematical thought into a formal set theoretic language – an incredible accomplishment and now the standard foundation for modern mathematics.
Set theory was first axiomatized in 1904 and has since been utilized to derive most fundamental results in pure mathematics. Additionally, it has contributed significantly to other fields of mathematics like probability theory and geometry.
Grad students taking a logic-based exam must possess an in-depth knowledge of set theory. This subject requires extensive study and practice to gain the required level of comprehension.
At Berkeley, a comprehensive understanding of set theory is an absolute prerequisite to passing the logic-based exams. This course provides a thorough introduction to sets, membership rules, and their expressive power.
Recursion theory is a branch of mathematical logic and computer science that began in the 1930s with the study of computable functions and Turing degrees. It has since broadened to encompass generalized computability and definability, as well as overlaps with proof theory and effective descriptive set theory.
Recursion theory states that a function f is considered to be recursive if its computation can only be accomplished using recursion. This concept is similar to how loops in computer programs may be considered recursive; the main distinction being that the former must be capable of calling itself when its input arguments match up against one single base case value.
Similar to for and while loop statements in programming languages, recursive functions require at least one base case to prevent them from repeatedly calling themselves.
Let us use an example to demonstrate this point: suppose you want to write a function that calls itself until its output equals five. To accomplish this, pass in input arguments x, y and z until each has been matched against a single base case.
Once this is complete, you can recursively call the function until its final result equals five. However, if an initial value of zero (0 0) isn’t passed to it when called, then failure will occur and an error message will display.
Recursive functions can be divided into two categories: b-recursive and a-recursive. Generally speaking, b-recursive functions are weakly admissible while a-recursive ones are strongly admissible.
The latter category is the strongest and most significant. It is cartesian closed, satisfying several other categories-theoretic properties.
B-recursion theory is intimately connected to Peano arithmetic’s formal logic, and many researchers have worked on it with this in mind. Kleene’s work on arithmetical hierarchy is particularly pertinent here, while Jensen’s ordinal recursion theory serves as the driving force behind this area of research.
Incompleteness in logic refers to the fact that certain mathematical systems may not be complete. This discovery, attributed to Kurt Godel in 1931, has become one of the cornerstones of modern logic.
Incompleteness is the property that some mathematical statements cannot be proved or disproven within an axiomatic system, although they may still hold true elsewhere. This does not undermine logic nor lead to any speculative nonsense as some claim; on the contrary, it promotes creative problem-solving and constructive debate.
Some have argued that this poses a problem for the program of logicism proposed by Gottlob Frege and Bertrand Russell. However, this argument ignores the incompleteness theorems which affect both arithmetic and first order logic.
Godel defined a valid logical expression as an ordered first order formula without identity, and an expression is refutable (false) if its negation can be proved true, or satisfying (true) if some interpretation of it holds.
Furthermore, an incomplete arithmetic statement is one that is neither provable nor refutable in Peano arithmetic but true according to the standard model of arithmetic. Thus, even though true arithmetic contains all such statements, it does not satisfy the hypotheses posed by incompleteness theorems.
Since their publication in the 1930s, incompleteness theorems have been an influential part of mathematic philosophy and set theory, as well as arithmetic formalism.
Proofs of the first incompleteness theorem have been achieved using computer verification methods; Natarajan Shankar in 1994, Russell O’Connor in 2003 and John Harrison in 2009. Lawrence Paulson announced a computer-verified proof of the second incompleteness theorem with HOL Light in 2013.
Graduate logic students must take a course in logic and pass a foundations exam for credit. Logic I provides proof procedure and semantics for first-order predicate logic with identity, as well as basic familiarity with standard metalogical results concerning completeness, incompleteness and decidability. This should typically be completed during the first year of graduate study prior to taking the foundations exam; exceptions may be granted by either the chair of the department or graduate adviser.


We present a numerical model for the evolution of pathogens organised in discrete antigenic clusters, where individuals in the same clusters have the same fitness. The fitness of each cluster is a decreasing function of the total number of cluster members appeared in the population. Cluster transition is modelled with inclusion and exclusion of dynamical epistatic effects. In both cases we observe a continuous transition, driven by the mutation rate, from a dynamics with single clusters alternating in time to the coexistence of many clusters in the population. The transition between the two regimes is investigated in terms of the key parameters of the model. We find that the location and the scaling of this transition can be explained in terms of the time of first appearance of a new cluster in the population. The presence of dynamical epistatic effects results in a shift of the value of the mutation rate where the transition occurs.


Hello! I have a very silly question, but I can’t find the answer in the documentation, nor in the forum.
I am currently using WebODM, which has the feature of downloading the Surface Model in TIF format.
I can open it and visualize it in Matlab to process it, which is great. The TIF file appears to be a matrix of floating point values, where the Matrix position represents the X,Y position of the image, and the elements of the matrix are the Z. So far, so good.
The question is: What is the unit for these Z? They are doubles, and they are around 100-130. Are they in meters, feet? Relative height or absolute height?
Theories and advice for where to look for the answer are welcome . Thank you very much in advance


About Invention Convention
Invention Convention envisions a day when EVERY child in Ohio has the chance to follow their natural curiosity, invent creative solutions to problems, and be inspired to achieve their highest level of personal accomplishment. Through these efforts, Ohio’s future will be brighter and its workforce more capable and productive.
Invention Convention began in 1993 as the brainchild of the Columbus Dispatch Printing Company in Columbus, Ohio. The Dispatch wanted to reach out to the Sandusky community to play a more significant role in the future success of the community’s youth. They decided to focus on sharing good news and they built the concept of “invention convention” – focusing on bringing creative problem-solving skills and unique math and science curriculum to area schools. What they didn’t realize, was that this program had such an amazing educational value to it, that educators in Columbus said, “We have real value here, we must have this program in our schools too!” The Dispatch then formed a 501c3 entitled, “Just Think, Inc.” and in 2014, The Ohio Academy of Science took Invention Convention under its wing where it thrives today! During the past 21 years, The Invention Convention has been a tremendous success in Ohio, and as a free program, has awarded more than $700,000 in college scholarships, taught invaluable problem-solving skills, built self-esteems, rewarded creativity, and has helped shape the future workforce of Ohio.
The mission of The Ohio Academy of Science and the Invention Convention program is to foster curiosity, discovery and innovation and unite all who value education, science, engineering and technology, or their application, for the benefit of society.
Our goals are to enhance student performance in Ohio’s Learning Standards in math, science, and language arts through administration of the Invention Convention program and to increase the interest of Ohio’s students in STEM (Science, Technology, Engineering and Mathematics) and STEAM (Science, Technology, Engineering, Art and Mathematics) careers.
Our objectives are to reinforce Ohio’s educational standards in math and science through critical thinking and problem-solving activities associated with the Invention Convention curriculum; to reinforce Ohio standards in language arts through communication activities associated with the Invention Convention curriculum; and to expose Ohio children to STEM- and STEAM-related activities through participation in the Invention Convention program.


How to calculate the weight of stone products?
Author:apexpwu Date:4/17/2006 6:40:14 PM Read:2613
The density of most of the stone ranges from 2800 to 3000 kilogram per cube meter. Tiles in 1cm (3/8") thickness will be around 28 kilogram (62LB) per square meter. Or 5.8LB per square foot. Slabs, countertops, vanity tops, tiles in 2cm (3/4") will be around 56KGS per SQM. Most of the carved fireplace mantels will be around 400KGS each unit including package.


My program to identify the primer is dependent on repeated digits at the crib location. Thus it depends on knowing the crib location. The process is to increment every 5-digit primer from 1 to 99999, extend the full primer until the digits cover the crib location, then take the segment of the primer where the crib is located and call that primer2. In the preceding con, the crib is 14 letters long. I test each primer2 first for three-digit triplets and search to see if the corresponding crib letters appear as a sequence in the ct. For example, if the primer2 were 19692055125063, my program would first test the three 5’s. The corresponding crib letters for the 5’s triplet would be MOT for the crip “TOPOFAMOUNTAIN”. If MOT does not appear in the ct, then that primer cannot be correct. If it does, I give the primer a score of 5 points. Then I do the same with 2-digit doubles, like the 1, 6, 9 and 0. Those corresponding crib letters form digraphs. If any of those digraphs don’t appear in the ct, again the primer is rejected. If they do, 1 point is given for each such pair. Once all primers have been tested and a score given to each one that isn’t rejected, those are then ordered by score and decryption attempted with the highest-scoring primers first. The process could be extended to 4- or 5-digit groups, but the crib would have to be quite long in most cases.
In the preceding con, there was only one 4-primer group that outscored the correct group. I tested this method on the same con with a shorter crib (12 letters) without a triplet in the corresponding primer2 and the correct primer appeared in the highest-scoring 200 primers. If the crib were to occur over a sequence without many triplets or doubles, this method would not be effective. If the crib were long enough, one would not need to know its location in advance because there would probably be only one 4-primer group and crib location that would work.


(For those of you who are undecided about whether to buy a petrol or diesel car, here are some quick calculations I did today. You can use this as a starting point and decide which one to buy based on your driving pattern)
Petrol 1 litre – Rs. 82.86 (Average Mileage: 14 km/l)
Diesel 1 litre – Rs. 56.10 (Average Mileage: 17 km/l)
For 1000 km,
Petrol – 1000/14 * 82.86 = Rs. 5919
Diesel – 1000/17 * 56.10 = Rs. 3300
(Difference = Rs. 2619 per 1000 km)
Diesel cars cost ~Rs. 1.5 lakh more than petrol vehicles
Therefore, to break even,
you have to drive 150000/2619 = 58,000 km in a diesel car.
After that, you start seeing the benefit of lower diesel prices.
I’m at 63,000 km now. Yay!!


The probability subject is a very difficult subject to me. This is because it involves estimation of all the possible events. Therefore, it involves the combination and permutation. And there is no exact formula for different situations. It also involves statistics.
Gambler’s fallacy, is a very good notion. To simplify it, gambler’s fallacy is a belief that the next outcome will be different if the observed outcome is repeated consecutively, where these events are actually independent. The best example is tossing the coin, which has the probability of 0.5 for head and 0.5 for tail. Because tossing the coin first time will not affect the second time, the probability to get the head or tail is always same.
For example, first tossing the coin to get the head is 0.5, then 2nd for head is 0.5*0.5 = 0.25, then 3rd for head is 0.125. As for the gambler’s fallacy, the person will think that the probability to get another head is 0.0625, which the chance is very small. Thus, the person will assume that the next one is tail. However, in the actual sense, because of the events are independent, thus, to get the 4th time as tail, it is also 0.25 * 0.5 = 0.0625. That means, whenever we toss the coin, the probability to get head or tail is always 0.5.
However, recently I think about the probability again in the empirical way. Firstly, we need to know, the probability 0.5 means that, if we toss the coin 1000 times, the result of head is approximately 500 times. The greater the number of tossing, the results will be more close to 0.5. However, if the total number of tossing decreases, the deviation of the empirical result becomes higher. For example, if we toss the coins only 2 times, we might get 2 tails for both tossing, where the empirical result of head is 0.
So, that is why gambler’s false assumption happened. If a person is going to toss the coin 500 times, and this results 250 tails successively, that means the next 250 toss must be heads, so that the empirical result will be 0.5. This is interesting part. This kind of belief normally connected to the fate or luck. That is why some people believe that if we are too lucky successively, we might finish using our good luck for our whole life, then we will left only bad luck until the end of the day.
As a conclusion, the gambler’s fallacy is true (refers to 2nd and 3rd paragraphs). But sometimes we cannot accept it, for example tossing the coins and get the head 10 times successively, then the next 10 toss are probably tails, so that the probability will equal to 0.5, this is what we normally believe. Therefore, if asking me to guess the next outcome after 10 successive heads, I will also guess tail, even I know that gambler’s fallacy is true.


NumPy, Matplotlib, SciPy
NumPy, Matplotlib, and SciPy are packages that are fundamental to many applications of Python, particularly in numerically-oriented programming. They are not part of the set of base Python packages, but are included in distributions such as Anaconda and can be easily added to other Python installations.
The NumPy package adds many features important or useful to scientific and numeric computing. These include
- True multidimensional arrays
- Linear algebra functions
- Fast Fourer Transform (FFT) Functions
- Random number generators
- Tools for integrating Fortran, C, and C++ libraries.
Matplotlib is a Python package that can be used to produce high-quality plots similar to those of MATLABTM. Its homepage and documentation can be found at matplotlib.org. A full complement of plot types is available, including
- line plots
- scatter plots
- bar charts
- pie charts
- contour plots
NumPy and SciPy (Scientific Python) are closely linked and frequently are used together. Both provide a large selection of built-in functions.
SciPy builds on NumPy to provide a set of modules and packages that add functions for data analysis and numerical computations. These include
- special functions
- linear algebra
- quadrature (numerical integration)
- signal processing
- basic statistics


In Maths there’s a number. This number’s called PI. But why name a number? I’ll demonstrate why! And when I have finished you may be astounded, your maths skills will be just that little more … ’rounded’ (*ahem*) Find any old circle: a pan or a pot, a frisbee, a saucer, whatever you’ve got. Get one ball of string, grab some scissors and glue. You’ll need them for what we are going to do.
We’re going to measure a distance with string, in order to witness a curious thing – no matter the size of the circle selected, there’s something about every one that’s connected.
First measure the length of your circle’s inside. You’ll start at one edge and you’ll go side-to-side. (Go straight through the part where the circle’s most wide, and try to be accurate. Measure with pride!)
Then snip at the string with your scissors SNIP SNIP! And look at the string that you have in your grip, admiring its length as you hold it out straight. That’s called the DIAMETER. Isn’t it great!!!
“But what use is that?” I hear some of you squeal. “A length of old string. Tell me, what’s the big deal?” (Well, that’s just the thing that I plan to reveal!)
We’ve one bit of string, but we need to have four. So, please make 3 more, just the same as before. And when you have finished, you’ll want to ensure all lengths are the same. Yes I know – what a bore!
Now this is the fun part, so here’s what you do: you cover the edge of your circle with glue, and stick on the string (pieces one, two & three). You’ll end up with one tiny gap, just like me!
That forth bit of string is too large is it not? Chop off what you need – just the tiniest dot. Then fill up that gap so the gap is no more. You used 3 whole pieces … and 0.14 <—- [nought point one four]
Now pick a new circle. You know what to do … you measure the string and you slap on the glue. You stick on that string and then just like before, you find you need 3 and a bit out of 4!
But if you are shouting “That’s NOT my result!” your circle is wonky or *you* are at fault! I know this for certain. Of that I am sure. You see, I know PI, which is 3.14!
This value is constant. It always applies. Each circle obeys it, no matter the size. And rather than always say “three point one four”, we just call it PI, as it’s less of a chore.
To find the circumference (around the outside), we measure our circle (to find out how wide), and multiply that by our figure called PI. I hope that explains it. I’m off now. Bye bye.


To repoint a curve: Select one or more curves. Default is 0. ParFit can simultaneously handle several molecular-mechanics. Polynomial terms are independent variables that you raise to a power, such as squared or cubed terms. Where A is the amplitude of the curve, c is the offset from zero and k is the rate constant. Nevertheless, for a given set of data, the fitting curves of a given type are generally NOT unique. Learn Python programming language as a creative medium for design, architecture, art and beyond. optimize curve_fit function. If False, sigma denotes relative weights of the data points. In other words, it can be used to interpolate or ex. Pat This shows that minimizing the original curve fitting function is equivalent to minimizing. Fitting data with python. How to fit data to a normal distribution using MLE and Python MLE, distribution fittings and model calibrating are for sure fascinating topics. Curve Fitting using Polynomial Terms in Linear Regression. That's what curve fitting is about. 1D Examples and Exercise¶. Multiple curve fitting python. Data analysis with Python¶. Linear regression is a statistical model that examines the linear relationship between two (Simple Linear Regression ) or more (Multiple Linear Regression) variables — a dependent variable and independent variable(s.
Steps for Implementing VIF. What if you wish to test for differences in best-fit parameters between three or more data sets, on the basis of a single experiment? You don't just want to know if all the curves are the same. ROC Curves and AUC in Python. The preview window in the NLFit dialog is capable of plotting only one quantity versus another. How to fit data to a normal distribution using MLE and Python MLE, distribution fittings and model calibrating are for sure fascinating topics. So you need to convert the sets of data and models (or the value returned by the objective function) to be one dimensional. fit-extract (Data Extraction for EC-LAB) ===== Extracts parameter (R2, R3, Q1, etc. As an example, here is a residual. diag(pcov)). I suggest you to start with simple polynomial fit, scipy. A 1-d sigma should contain values of standard deviations of errors in ydata. # curve fit [with only y-error] popt, pcov = curve_fit(func, x, y) You still get an estimate for the uncertainty of the fit parameters, although it is less reliable. CurveExpert Professional is a cross-platform solution for curve fitting and data analysis. optimize import curve_fit. As always the entire code freely available at this github repo. Built-in Fitting Models in the models module¶. This binning only influences how the scatterplot is drawn; the regression is still fit to the original data. I'm analysing some Raman spectra and have written the basis of a program to use Scipy curve_fit to fit multiple lorentzians to the peaks on my data.
Fitting Lorentzian curve to noisy data. Specifically, the powerful and robust Python Rhinoscript library will be introduced and explored in detail. The Relationship Between Precision-Recall and ROC Curves Jesse Davis [email protected]
GH Python - Intersect List of Curves and get a List of Point3D. Setting the Initial Conditions. The code written in Python can be edited and tested multiple times by running it in the interpreter. It is especially designed to fit spectroscopic data but should be suited for any other fitting task. A visual examination of the fitted curve displayed in the Curve Fitting Tool should be your first step. xlsx' in the same folder. This best-fitting curve can be obtained by the method of least squares. Can anyone. Curve Ensemble is a free C++ open-source project for fitting, editing, and painting curves. Scipy will sometimes warn you if the degree is too high. Moreover, Python is an excellent environment to develop your own fitting routines for more advanced problems. This example shows a code to generate a fake dataset and then fit with a gaussian, returning the covariance matrix for parameter uncertainties. By Anders Andreasen. Fitting multiple gaussian curves to a single set of data in Python 2. To help the routine find the best fit it is hence a good idea to give it a good starting point.
Grasshopper. With Python, we need to use the statsmodels package, which enables many statistical methods to be used in Python. I am trying to use Python's 'len' to gather the length of the curves selected to offset. For a refresher, here is a Python program using regular expressions to munge the Ch3observations. Comparing AUC values is also really useful when comparing different models, as we can select the model with the high AUC value, rather than just look at the curves. We can get a single line using curve-fit() function. While reasonable. fit a sigmoid curve, python, scipy. MCS320 IntroductiontoSymbolicComputation Spring2007 MATLAB Lecture 3. An operator may plot the ROC curve for the final model and choose a threshold that gives a desirable balance between the false positives and false negatives. A 2-d sigma should contain the covariance matrix of errors in ydata. There are some relationships that a researcher will hypothesize is curvilinear. I have data sets that describe the relation between a dependent variable of interest and three different independent variables, where the relationship is linear in two of the cases and enxponential in the third. I've discussed linear regression on this blog before, but quite often a straight line is not the best way to represent your data. Posted on Tue 27 November 2018 in python • Tagged with lmfit, python, curve fitting. After creating a fit, you can add an additional fit using any of these methods:. Choose a model that is complex and likely represents an underlying mechanism of something at work. Legends can be placed in various positions: A legend can be placed inside or outside the chart and the position can be moved. Ask Question 1 $\begingroup$ I have this 7 quasi-lorentzian curves which are fitted to my data.
56 KB py", line 84, in test_hazardcurve_plot_generation_multiple_sites_multiple_curves. A three parameter (a,b,c) model y = a + b/x + c ln(x) is fit to a set of data with the Python APMonitor package. A 1-d sigma should contain values of standard deviations of errors in ydata. The formulas for multiple regression and curve fitting are a little different from the formulas for linear regression, but the ideas are the same. I'm analysing some Raman spectra and have written the basis of a program to use Scipy curve_fit to fit multiple lorentzians to the peaks on my data. SciPy's curve_fit() function allows us to fit a curve defined by an arbitrary Python function to the data:. leastsq that overcomes its poor usability. How can I fit multiple data sets?¶ As above, the fitting routines accept data arrays that are one dimensional and double precision. To determine an appropriate model for a data distribution, you should consider curves from several distribution families. Like leastsq, curve_fit internally uses a Levenburg-Marquardt gradient method (greedy algorithm) to minimise the objective function. To repoint a curve: Select one or more curves. This is a simple 3 degree polynomial fit using numpy. Interpolate and Extrapolate for a set of points and generate the curve of best fit that intersects all the points. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases. fit-extract (Data Extraction for EC-LAB) ===== Extracts parameter (R2, R3, Q1, etc. It is calculated by taking the the ratio of the variance of all a given model's betas divide by the variane of a single beta if it were fit alone.
Pat This shows that minimizing the original curve fitting function is equivalent to minimizing. Beyond that, the toolbox provides these goodness of fit measures for both linear and nonlinear parametric fits: Residuals. 1 Fitting Multiple Curves Independently. Curve Fit: A Pixel Level Raster Regression Tool. Once I have this array of fit uncertainties, I plot the best fit curve, the fit curve, the fit curve, and use the matplotlib plot. Okay so I appreciate this will require a bit of patience but bear with me. After fitting data with one or more models, you should evaluate the goodness of fit. Introduction. You will push design and architecture limits for new product development. import numpy as np np. Task 7 : Fit a power model and intercept. I'm investigating Brewster's angle in the diffraction of polarised light and I've been trying to produce a line of best fit for my data. Setting the Initial Conditions. In this case, the optimized function is chisq = sum((r / sigma) ** 2). Nevertheless, for a given set of data, the fitting curves of a given type are generally NOT unique. The objective function to be minimised is directly provided and does not need to be of a least-squares form. Hi all, How to plot a Stress vs Strain curve by using the governing equations and input values in Matrices form using PYTHON? I also need to show the strain maximum and minimum points, Yield. One can try with the following data (luckily, we know that this sequence has a limit):. See related question on stackoverflow.
The * before the list is key to unpack it into multiple parts; those multiple parts are then packed into the *arg. The main purpose of this video is to show case the convenience of having a custom program. Use Web applets to manipulate curves of best fit on an interactive whiteboard. Fit Plot window contains the list of. Each value along the x-axis represents that many standard deviations from the mean. So in the context of an ROC curve, the more "up and left" it looks, the larger the AUC will be and thus, the better your classifier is. More details in and. The generalization to any family of curves, and to any family of surfaces, or things in higher dimensions, is straightforward. Image source: Pexels. 2 Fitting to a functional form The more general way to use nls is to de ne a function for the right-hand side of the non-linear equation. TrimCurve will trim a curve by removing portions of the curve outside of a specified interval. set # Load the iris dataset iris = sns. The sigmoid function, also called logistic function gives an ‘S’ shaped curve that can take any real-valued number and map it into a value between 0 and 1. , of spectra with a few mouse clicks. Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy. Part 1: Introduction. SciPy curve fitting. If you have one, then it is easy to do that.
Let me see if i can explain it simply using an example Lets say you have a customer base of 1000 customers spread out in various areas of equal sizes Area 1 to Area 10, of which around 400 customers are likely the target of a new product you are l. This article describes a method how to fit a circle to the cluster of points in 3D space. pandas python PyQGIS qgis DataFrame precipitation datetime Excel numpy timeseries Clipboard idf regression Chart PyQt4 accumulated curve fit manning's formula polyfit rain read scipy text files Line Open File Open folder PLotting Charts String Time series exponential fitting idf curves flow formula geometry groupby hydrology install list. Here are some examples. Create a linear fit / regression in Python and add a line of best fit to your chart. py Find file Copy path Ffisegydd Added a curve_fit example to scipy 53dc2cd Mar 27, 2014. pairwise_survdiff(): Multiple comparisons of survival curves. optimize and a wrapper for scipy. Recently I have a friend asking me how to fit a function to some observational data using python. But even you don't know the form of the function you want to fit, you can still do it fairly easy. You can select each pair of curves by individual mouse click or together with a window. For these specific situations, we can take advantage of some of the tools available to perform nonlinear regression or curve fitting in Excel. The code above works fine if I only have the first plot, similar to what the 'Generate Code' feature in the curve fit toolbox creates. More details in and. Polynomial terms are independent variables that you raise to a power, such as squared or cubed terms. We went quite deep into the formal setting for it (projective space ), and we spent a lot of time talking about the right way to define the “zero” object in our elliptic curve so that our issues with vertical. The area under the curve (AUC) of ROC curve is an aggregate measure of performance across all possible classification thresholds. What you’ll be doing:You have developed complex software products that include scale, algorithmic complexity and huge amount of data. If the user wants to ﬁx a particular variable (not vary it in the ﬁt), the residual function has to be altered to have fewer variables, and have the corresponding constant value passed in some other way.
The rate constant can be estimated as 1/t1/2 (t1/2 = half-life). Yes it gives one curve and not 40 individual curves. Dose response curves are parallel if they are only shifted right or left on the concentration (X) axis. Tag: best fit Linear Regression How does regression relate to machine learning?. Learners will develop, analyze and critique algorithmic approaches to digital drawing, modeling, and projection. After we discover the best fit line, we can use it to make predictions. As with many other things in python and scipy, fitting routines are scattered in many places and not always easy to find or learn to use. 2 Nonlinear Curve Fits Nonlinear curve fitting is accommodated in KaleidaGraph through the General curve fit function. ROC curves and ROC AUC were calculated with ROCR package. All minimization and Model fitting routines in lmfit will use exactly one Parameters object, typically given as the first argument to the objective function. , the dependent variable) of a fictitious economy by using 2 independent/input variables:. The simplest but effective way of multiple peaks fitting of XPS, Raman, Photoluminesence spectroscopic data/graph. 2 we looked at useful mathematical models and formulas that we anticipate seeing repeatedly in the business environment. A 2-d sigma should contain the covariance matrix of errors in ydata. This will compute the 95% and 99% confidence intervals for the curves and for the optimised parameters (popt).
Let’s also solve a curve fitting problem using robust loss function to take care of outliers in the data. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. The diagonals provide the variance of the parameter estimate. Python examples. Curve Fitting & Approximate Functions Built into the Wolfram Language are state-of-the-art constrained nonlinear fitting capabilities, conveniently accessed with models given directly in symbolic form. Non-linear curve fitting (or non-linear parametric regression)is a fundamental part of the quantitative analysis performed in multiple scientific disciplines. Often times you have to try lots of different bounds, starting points, or functions before your fitted curves look reasonable so this usually isn't a simple once-and-done process. Can someone please tell me is there any mathematical equation/formula on ploting a curve with given three coordinates/points such as below image? As hotvette has already said, there are infinitely many curves you can draw through three points. We have a text file that contains data from a gas chromatograph with two peaks that overlap. Data and analysis spreadsheet for a power law curve fit. An operator may plot the ROC curve for the final model and choose a threshold that gives a desirable balance between the false positives and false negatives. 1D Examples and Exercise¶. The red triangles are the standards. The AUC number of the ROC curve is also calculated (using sklearn. It is not as computationally fast as pure compiled languages such as FORTRAN or C++, but it is generally considered easier to learn. Clearly, such type of cases will include a polynomial term. For a surface curve, you can only redistribute its points between two points, but not insert new ones. ROC Curves and AUC in Python.
Using the example dataset. That's what curve fitting is about. I am running a curve fit in python that encountered the error, RuntimeError: Optimal parameters not found: Number of calls to function has reached maxfev = 1000. In order to find the spline representation, there are two different ways to represent a curve and obtain (smoothing) spline coefficients: directly and parametrically. CurveExpert Professional for Linux v. Moreover, Python is an excellent environment to develop your own fitting routines for more advanced problems. Today we examine an approach to fitting curves to overlapping peaks to deconvolute them so we can estimate the area under each curve. Thank you for the help!. It is especially designed to fit spectroscopic data but should be suited for any other fitting task. auc()) and shown in the legend. WE WILL SENT ONE MORE SHAPING TOOL TO YOU About the product Multiple Curves - 3 Curve Sizes/Angles instead of just 1!. The key concept that makes this possible is the fact that a sine wave of arbitrary phase can be represented by the sum of a sin wave and a cosine wave. The above given source codes in C language for curve fitting are applicable for linear and exponential approximation of available data or for fitting linear or exponential curves through easy, accurate and fast way. Note that the training score and the cross-validation score are both not very good at the end. SciPy's curve_fit() function allows us to fit a curve defined by an arbitrary Python function to the data:.
First of all thanks for the code and. We can make density curves like above, but with "hist = False" argument to Seaborn's distplot. Curve fitting. from scipy. Python examples. In this tutorial, you will discover how you can diagnose the fit of your LSTM model on your sequence prediction problem. The computed values of a, b, r 2 for a power curve fit are shown at the bottom of the spreadsheet. New is an exerciser program allowing step by step observation of the curve fitting process. m that reads data from a file and perform regression analysis using polyfit and polyval. Some familiarity with scikit-learn and machine learning theory is assumed. GitHub Gist: instantly share code, notes, and snippets. A polynomial can also be used to fit the data in a quadratic. The exponent can be indicated by preceding it by the character E or e, as you can see in the example. By changing the preview or render resolution of the curve, you reduce the number of vertices generated per control point. All minimization and Model fitting routines in lmfit will use exactly one Parameters object, typically given as the first argument to the objective function. Singular values smaller than this relative to the largest singular value will be ignored. Like the system of ODE's from a disease SIR model.
Astrophysical example: Salpeter mass function. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data. 20 KB import numpy as np. It builds on and extends many of the optimization methods ofscipy. CurveFitData Refer to UF_MODL_curve_fit_data_s for documentation. Let us first review ideas concerning the cubic polynomial of degree 3 passes through the 4 points for and is shown in Figure 1. The graphs below show the characteristic curves of high, intermediate and low temperature thermocouples. By looking at the data, the points appear to approximately follow a sigmoid, so we may want to try to fit such a curve to the points. Select Analysis: Peak and Baseline: Multiple Peak Fit from the main menu. An algorithm to fit multiple measured curves simultaneously was developed. In this case, the optimized function is chisq = sum((r / sigma) ** 2). Python Scripting Command Reference Fit curves to streets settings. DCA is more than a empirical curve fit. This section describes the implementation of Parameter bounds. The n-th derivative of the Gaussian is the Gaussian function itself multiplied by the n-th Hermite polynomial, up to scale. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. In this tutorial, you will discover how you can diagnose the fit of your LSTM model on your sequence prediction problem. by scientists who analyse data from powder diffraction, chromatography, photoluminescence and photoelectron spectroscopy, infrared and Raman spectroscopy, and other experimental techniques,. com, automatically downloads the data, analyses it, and plots the results in a new window. surv_cutpoint(): Determines the optimal cutpoint for one or multiple continuous variables at once.
Given this, there are a lot of problems that are simple to accomplish in R than in Python, and vice versa. In this example we start from a model function and generate artificial data with the help of the Numpy random number generator. edu Mark Goadrich [email protected]
Note that if we optimized the hyperparameters based on a validation score the. The Multiple Peak Fit tool provides an interactive and easy way to pick multiple peaks in a graph and then fit them with a peak function. Brons 1963 and Fetkovitch 1983 applied constant pressure solution to diffusivity equation and demonstrated that exponential decline curve actually reflects single phase, incompressible fluid production from a closed reservoir. The returned covariance matrix pcov is based on estimated errors in the data, and is not affected by the overall magnitude of the values in sigma. Почему scipy. The graph below (Figure 5) shows the best fit curve to this data using the power law function. Can anyone. I am new to Python and statistics. , of spectra with a few mouse clicks. All keys of a Parameters() instance must be strings and valid Python symbol names, so that the name must match [a-z_][a-z0-9_]* and cannot be a Python reserved word. The Simplex is a greedy algorithm, too. I'm searching the coefficients a,n and m for the best fit over all curves. Overview of Matlab Curve Fitting Toolbox Junior Lab Technical Staﬀ MIT Department of Physics Advanced Experimental Physics I & II This quick-start guide contains instructions on how to use Matlab on Athena to ﬁt data sets. SAS/INSIGHT software offers both parametric and nonparametric methods to ﬁt curves.
We then fit the data to the same model function. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. Many calibration curves are linear and can be fit with the basic equation y=mx+b, where m is the slope and b is the y-intercept. ROC Curves and AUC in Python. Mathematically, the derivatives of the Gaussian function can be represented using Hermite functions. With Python, we need to use the statsmodels package, which enables many statistical methods to be used in Python. GH Python - Intersect List of Curves and get a List of Point3D. The NLFit dialog can then be used to fit with such functions. Do anybody knows, how to fit a custome Learn more about curve fitting Curve Fitting Toolbox. How can I fit multiple data sets?¶ As above, the fitting routines accept data arrays that are one dimensional and double precision. If you're an engineer (like I used to be in a previous life), you have probably done your bit of experimenting. Many datasets contain multiple quantitative variables, and the goal of an analysis is often to relate those variables to each other. polyfit and poly1d, the first performs a least squares polynomial fit and the second calculates the new points:. often fit the curve in the range of observed x values with a polynomial function. In other words, it can be used to interpolate or ex. I should have made that clear. The main idea is that we know (or. Data analysis with Python¶.
Polynomial terms are independent variables that you raise to a power, such as squared or cubed terms. You can generate conﬁdence ellipses, ﬁt parametric polynomials with conﬁdence curves, and. That's what curve fitting is about. table = TRUE) ggadjustedcurves Adjusted Survival Curves for Cox Proportional Hazards Model Description This function plots adjusted survival curves for the coxph model. I am an engineering student and relatively inexperienced with matlab. By default commas are considered column separators; in the case you are using them as decimal separators check the option below. Part 2: Lines, Points & Formatting. In particular, when your learning curve has already converged (i. All minimization and Model fitting routines in lmfit will use exactly one Parameters object, typically given as the first argument to the objective function. Graphics #120 and #121 show you how to create a basic line chart and how to apply basic customization. It contains a powerful language for combining simple models into complex expressions that can be fit to the data using a variety of statistics and optimization methods. it uses either interpolation or regression. The psychotools provides an infrastructure for psychometric modeling such as data classes (e. The tokenize module provides a lexical scanner for Python source code, implemented in Python. The algorithm for constructing principal curves starts with some prior summary, such as the usual principal-component line. 3 based on 159 Reviews "Nisha was very helpful in providing. ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. The result of the fitting process is an estimate of the model coefficients. Python Fit Multiple Curves.


Lose 10 Pounds with this easy workout, using the math provided
Today, I’m going to give you a workout plan and meal planning guide. This is a beginner level program that will take 10 minutes each day, every day. No special equipment is required, but we will have to do a few calculations in order to personalize your results. Getting personalized results does require us to calculate a bunch of information about you, which is what I will be detailing.
If you want some help getting through this process, email me at firstname.lastname@example.org or call/text 240-630-0298 to arrange a time to go over it together
Grab a scale and a tape measure if you don’t know your height and weight. Getting height in centimeter and weight in kilograms is best, but we can adjust the numbers if you only have inches and pounds. Our goal is to lose 10 pounds, and we’ll explain this weight loss at the end of the article.
The first thing we will do is calculate your basal metabolic rate, which is the minimum number of calories your body needs to function. We are going to estimate this number using your height in centimeters and your weight in kilograms. If you have your height in inches, then multiply that number by 2.54 to get your height in centimeter. If you have your weight in pounds, then divide that number by 2.2 to get your weight in kilograms. Now we are going to plug in some numbers into an equation to get an estimate of how many calories your body uses in a day for metabolic function, which is all of the those things associated with keeping you alive.
Men use this equation → BMR = (height in centimeters x 6.25) + (weight in kilograms x 9.99) - (age x 4.92) + 5
Women use this equation → BMR = (height in centimeters x 6.25) + (weight in kilograms x 9.99) - (age x 4.92) - 161
Now that we have your BMR, we can build the seven day, 10 minute routine using a brisk walk as our means of exercise. A brisk walk will burn between 4-7 calories, depending on your weight, which is a function of weight (in pounds) multiplied by 0.03. We are also going to estimate that a person engaging in light activity will need thirty percent more calories above their BMR, so we’ll multiply your BMR by 1.3 to get your total daily energy expenditure (TDEE). However, we’ll want to consume about ten percent fewer calories to create a weight loss deficit, so we’ll multiply BMR by 1.2. This number will be Calorie Intake, which is how much you should aim to eat each day
Let’s chart this information:
Height & Weight
BMR x 1.3
BMR x 1.2
Weight lbs. X 0.03 x 10 minutes
Let’s take use a 5 foot, 4 inch 30 year old female who weighs 120 pounds to illustrate how to use this table
Her BMR is (163 cm x 6.25) + (55 kg x 9.99) - (30 years old x 4.92) - 161 which is about 1260 calories. Let’s plug this into our table.
Height & Weight
As promised, here’s the part where we lose 10 pounds. 90 calories burned each day will burn through a pound of fat (which is 3500 calories) every 39 days. This means that it’ll take 390 days, or a little over a year, to burn off 10 pounds of fat. The good news is that, with a disciplined diet and small amounts of exercise, your body can safely maintain your weight all year long while burning through 10 pounds of fat.


Answer Book Prealgebra by Jeffrey A. Cole Download PDF EPUB FB2
It can be used in courses named “Basic Mathematics,” “Introductory Algebra,” “Fundamentals of Algebra,” and so on.
The organization makes it easy to adapt the book to suit a variety of course syllabi. : Pre Algebra (Kumon Math Workbooks) (): Kumon, Kumon: Books The Answer Key for Workbook I (front 1/2 of the book) is in the Middle of the book. The Answer Key for Workbook II (second 1/2 of the book) is the answer key in the very back of the book.
So yes, it is the correct answer key(s)/5(). Text book has solid algebra fundamental materials. Each chapter explains the concepts with easy to understand examples. Perfect summer review for my son who will attend High School this Answer Book Prealgebra book. Strongly recommend to also get the Study Guide Workbook.
Pre-Algebra Go Math!: Student Interactive Worktext Florida GO Math: Advanced Mathematics 1 Florida GO Math: Advanced Mathematics 2 Florida Standards Practice Book HMH GO Math!, Grade K Go Math. StaGrade 7 Interactive Go Math. Common Core, Middle School Go Math. Student Edition Chapter 1 Grade.
The solutions manual contains full solutions to all of the problems, not just answers. This book can serve as a complete Prealgebra course.
Our site includes a free innovative online learning system, Alcumus, and a free collection of videos, both aligned to this textbook. This book is used in both our Prealgebra 1 and Prealgebra 2 courses. This pre-algebra textbook (or ebook) is different from other math texts you have previously used in school where you were taught by a classroom teacher.
This book, for the most part, is your teacher and tutor and as such, it will guide your step-by-step learning. Just as you should pay. The Prealgebra Solutions Manual is a great companion to the Prealgebra book. Since it's always nice to be able to check out the answers, the Prealgebra Solutions Manual is really a must.
While many math books include the solutions at the end of the book, the authors of Prealgebra Reviews: Important facts and powerful problem solving approaches are highlighted throughout the text. In addition to the instructional material, Answer Book Prealgebra book book contains well over problems.
The solutions manual (sold separately) contains full solutions to all of the problems, not just answers. This book can serve as a complete Prealgebra s: Homework Answers. Math Concepts Class Notes.
Quiz Answers. Math Intervention. Grading Policy. Parent Page. Pre-Algebra Book PDF. Selection File type icon File name Description Size Revision Time User Pre-Algebra Contents and Introductions; Selection File type icon File name Description Size Revision Time User; Ċ: Pre-Algebra Text Contents.
The solutions manual contains full solutions to all of the problems, not just answers. This book can serve as a complete Prealgebra course. Our site includes a free innovative online learning system, Alcumus, and a free collection of videos, both aligned to this textbook. Text: s: 6.
Prealgebra prepares students for the rigors of algebra, and also teaches students problem-solving techniques to prepare them for prestigious middle school math contests such as MATHCOUNTS, MOEMS, and the AMC covered in the book include the properties of arithmetic, exponents, primes and divisors, fractions, equations and inequalities, decimals, ratios and proportions, unit.
Pre-Algebra Quiz and Test Book $ Pre-Algebra Quiz and Test Key $ Rapid Calculation Drills C $ Homeschool Arithmetic Charts $ Arithmetic Charts $ Saxon Math has consistently been one of homeschoolers' top choices when it comes to math curriculum.
Saxon's incremental approach to education has been proven to be one of the most effective teaching methods; learning pieces of information in small amounts, and then continually repeating concepts keeps information fresh and active in one's mind, preventing once-learned information from.
Deep, thorough, challenging Prealgebra book. Beast Academy prepared us well for it. I wish it weren’t quite so wordy. The content is good enough to make up for it, though, plus there is just nothing else like this out there if you want a deep understanding of the concepts.
The videos, Alcumus and MathCounts Trainer are a big plus, too!/5(1). Welcome to the study of prealgebra. In this ﬁrst chapter of study, we will Answer: 6, 8 CHAPTER 1. THE WHOLE NUMBERS Year AtmosphericCO2 Table Atmospheric CO2 values (ppmv) derived from in situ air samples collectedatMaunaLoa,Hawaii,USA.
"The book is set up to present the math standards needed in Pre-Algebra learning but does so in a logical manner and emphasizes practice along with critical thinking.
What it steers clear of is teaching new concepts before the student is ready. Instead, this book presents new concepts clearly and logically and in connection with other concepts.".
Prealgebra is based on Bob Prior’s own varied teaching experiences, and is designed to serve the needs of today's developmental math student and classroom. Bob knows that because today's students don't always have a lot of "face time" with their instructors, a usable, thorough, easy.
PREALGEBRA - Prepared EXCUSIVELY for Solano Community College by Lial - Hestwood and a great selection of related books, art and collectibles available now at Prealgebra is a one-semester, college mathematics book that introduces students to negative numbers and equations early on in the course.
Published by XYZ Textbooks. CONTENTS Catalog. Our $45 All-Access Pass allows you to view our online books, worksheets, answer. Algebra 1: Common Core (15th Edition) Charles, Randall I.
Publisher Prentice Hall ISBN The Life of Fred Language Arts Series is designed for the high school years; it is recommended that books be used in order: Australia, Begin Teaching, Classes, and and to the point, each contains 19 daily lessons that are rich in the rules of the English Language.
Covering grammar (not literature and writing) –as well as the many other facts about other topics Fred always. The Pre-Algebra works a little differently than the The academic content is the same as always and includes over hours of multimedia instruction.
However, each course is now delivered online as a month subscription (per student). This obviously means no more discs (and, importantly, no more need for expensive disc replacements).
$\begingroup$ Look for a prealgebra book and an Algebra I book by Saxon. Part of learning math is working sample problems until you understand them and you made need to obtain several used math books to provide good explanations for a given problem and enough examples to work through till you understand them.
About This Book. Prealgebra is a one-semester, college mathematics book that introduces students to negative numbers and equations early on in the course. Negative numbers are in the second chapter, and equations are in the fourth chapter.
2 3. 44 4: 43 + PM PM AM 7" 7" 9 yd I ft 15 yd 2 ft 25 1b 80Z+60z- b 10 OZ+60z- irrational. Prealgebra And Introductory Algebra 4th Edition Solutions Manual is an interesting book. My concepts were clear after reading this book. All fundamentals are deeply explained with examples. I highly recommend this book to all students for step by step textbook solutions.
Algebra and pre-algebra textbooks lay the mathematical foundation for understanding more advanced mathematics.
College algebra textbooks and pre-algebra textbooks discuss basic concepts such as inequalities, graphing linear equations, logarithms, and expressions. Stepping beyond these algebra and pre-algebra textbooks you may continue your studies with geometry, trigonometry, or calculus.
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Students get unlimited access to a library of more t products for $ per term. Pre-Algebra [DRAFT] H. Wu Ap ; revised, Octo c Hung-Hsi Wu, General Introduction (p. 2) oor and trying to put the books on the shelves using a well-understood organizing principle.
Once arranged this way, any book in the room can be easily accessed in the future. Likewise, if we can re-organize mathematical. Prealgebra with Study Skills 2nd Preliminary Edition by Alan and Holly Bass and a great selection of related books, art and collectibles available now at Prealgebra and Introductory Algebra by Cavanah and McKeague fits both the traditional, one-semester prealgebra course, and the one-semester introductory algebra course, but combined into one ts are introduced to negative numbers and equations early in the curriculum.
Students are also introduced to evaluating algebraic expressions and solving algebraic equations early on in the.Algebra 1/2 Home Study Kit includes the hardcover student text, softcover answer key and softcover test booklet. Containing lessons, this text is the culmination of prealgebra mathematics, a full pre-algebra course and an introduction to geometry and discrete mathematics.
Some topics covered include Prime and Composite numbers; fractions & decimals; order of operations, coordinates.


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Description: Lola's Math Train 2 is a game for kids, which is pretty self-evident from the title and its appearance. This is a game that is specifically designed to help children hone their Math skills while having fun. Call it a form of edutainment if you want, but it's an entertaining title that can also offer genuine results. Unlike a lot of other similar titles, Lola's Math Train 2 is not a game that has an educational add-on, instead the developers worked with Stanford University to make sure it was educational and developed a system that scales the difficulty of levels as the child gets through them. There are three difficulty levels, and each one of them adjusts to the child's skill when playing. The easy difficulty features two-digit addition and subtraction, ordering of numbers, with the medium difficulty featuring odd and even numbers, number sequencing. Finally, the hard difficulty tests more difficult subtraction, problem solving tasks and multiplication.
How it Works: As with anything on Android, a quick download from the Play Store will install the game on your smartphone or tablet. After that, you're ready to play.
After a quick introduction from Lola herself, you'll be asked to choose a difficulty – probably something for parents to set.
The difficulties are explained in the description above, and don't forget that you can always work your way up to a higher one. I chose 3, and once you start playing, you'll start to see your first questions.
As you progress, there will be different types of question, that evolve different uses of Math, too.
At the end of each wave of answers, there's a score in percentage, as well as a star rating as well. The percentage is a particularly nice touch as a lot of schools will grade children using percentages.
Opinion: This isn't the first time that I've reviewed a game aimed towards children and learning, and I doubt it will be last. However, it is the first time that I've been impressed with the fit and finish of such an app. Lola's Math Train 2 is well-polished and looks really good on tablets, especially higher-resolution ones. Which is a good thing as it's safe to say that more kids will be playing this on tablets than they will be on smartphones. The difficulty level is interesting, and even as I chose 3, the game got a little tougher as I carried on playing, which is clearly the game adapting to how good a job I was doing. For children though, this effect will be more important and will help to challenge them and push them to do more things with their Math knowledge.
- Speed (4/5) – The game ran well on pretty much everything I tried it on and I had no complaints with performance.
- Features (5/5) – Certainly, there doesn't seem to be much on offer here, but the fact that the developer has worked with Stanford university to make the game as good as they could shows the effort they put in.
- Theme (4/5) – Everything looks good and Lola and her friends will definitely appeal to youngsters.
- Overall (4.5/5) – With sokme good-looking graphics and a high level of fit and finish, Lola's Math Train 2 is an excellent game for little ones to hone their math skills with.
- Colorful and inviting graphics will appeal to youngsters playing the game.
- Ability to customize the characters with different clothes will help to keep children playing.
- Different difficulties to choose from as well as adaptive difficulties make this a game for all skills and all ages.
- High level of polish makes this a game you can trust your kids with.
- Division and more would help widen the net to older children.
- More interaction with Lola and the other animals would be good.
Conclusion: While not perfect, Lola and her friends have come together in a game that many children will find appealing when honing their Math skills. It not only looks good and appeals to children, but there's also been a lot of research gone into making sure this is a game that will garner genuine results. It's easy to pickup and play, there are varying difficulties for most skill levels in young children and it's fun to play. What more could you ask for in a children's educational title?


Oct 31, · Enter the SUM function manually to sum a column In Excel; Use Subtotal in Excel to sum only filtered cells; Convert your data into Excel Table to get total for your .
How to use the SUM Function and AUTOSUM in Microsoft Excel - Tutorial for Absolute Beginners
If you need to sum a column or row of numbers, let Excel do the math for you. Select a cell next to the numbers you want to sum, click AutoSum on the Home. SUM formula is applied in cell “C15”, i.e. =SUM (B15, B16); here, We have to select the cells that contain the values we want to add together. In the argument, the cell references ‘B15’ and .
Using AutoSum to select common functions · Select the cell where the answer will appear (E24, for example). · Click the Home tab. · In the Editing group, click the. Oct 15, · , views Oct 15, Learn how to work with sum and autosum functions in Excel. Knowing how to work with them can help you greatly in your calculations. .
VIDEOHow to using Sum and AutoSum in Excel 2010
Tips: If you want, you can apply the criteria to one range and sum the corresponding values in a different range. For example, the formula =SUMIF (B2:B5, "John", C2:C5) sums only the .: How to use the sum function in excel 2010
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We are committed to making ST Math a seamless addition to any education technology ecosystem. With quick and easy program access, teachers and students can …
Elementary Math Program – ST Math
ST Math’s K-6 school program helps students gain math proficiency through self-paced, language-independent, mastery-based objectives.
School Landing Page Kit & Downloads – ST Math
ST Math Screenshot on iPad/Laptop (PNG); JiJi Image with White Outline (PNG); Student Login Graphic (PNG); Parent Resources Kit (PDF); Photos of Students Using …
Early Learning Math Games
ST Math: Early Learning provides a comprehensive, blended learning program for students offering math instruction before kindergarten.
Teacher Resources – ST Math
We empower teachers to build a greater understanding of how students learn, develop their math content knowledge, and effectively use ST Math to engage students …
How to Get Started with Picture Passwords (visual logins) – ST Math Help
Students sign into ST Math at play.stmath.com using their picture password and begin playing and learning! Ready to get started? First, let’s …
How the Games Work | Online Math Learning
Teachers and students can access the web-based ST Math software anywhere, anytime, and on supported mobile devices. From desktop to tablet, classroom to home, …
What’s New? – ST Math
Play Full Games … These new features are designed to support the deep understanding that makes ST Math so powerful and keep students learning on grade level.
Using Clever to Roster Students in ST Math
Please note that the Clever login is set by the district and is different than the username and password for play.stmath.com. Note: If a student …
Looking for the ST Math app?
ST Math no longer requires an app or flash player. Instead, student … login access for students. Was this article helpful? 0 out of 0 found …
Homeschool Program | ST Math
… students play at home and at school. Digital Manipulatives. Teachers have access to all of the visual models within ST Math to supplement classroom lessons …
Frequently Asked Questions | Visual Math Learning – ST Math
Request a demo here! I am considering using ST Math for my school. How do I get access to ST Math to try out the games? Tour and play …
ST Math – Clever
Instead of asking, “How do we teach math?” we need to ask ourselves, “How do we learn math?” With visual learning, students are better equipped to tackle …
How do students get started on ST Math?
ST Math is browser based. Some students will access through their school SSO/portal, while others will go directly to play.stmath.com.
Play ST Math: Addition and Subtraction – YouTube
Animated immediate feedback offers an intrinsically …
What is the process for setting up a St Math student account?
The process for setting up a St Math student account involves creating a teacher account, adding students to the teacher’s class, and assigning a username and password to each student. Once the student accounts have been created, students can log in and begin using the St Math program.
How can parents access their child’s St Math progress report?
Parents can access their child’s St Math progress report by logging into their child’s St Math account and selecting the “Progress Report” option.


What's a board foot?
A board foot (bd. ft.) is the unit of measurement for hardwood lumber
T" x W" x L" divided by 144 = Bd. Ft.
Here's What To Know:
1 Bd. Ft. = 144 cubic inches
Board footage is a volume measurement
Measure by the board foot when buying random widths and random lengths
How To Plan a Project with Board Feet
or scroll down to use a basic board foot calculator
Try This Board Foot Calculator
- Only use numbers. Fractions slashes ( / ) or periods (. ) for fractional sizes. Calculator assumes your numbers are in inches, so there's no need to include double quotes ( " ).
- Calculator rounds lumber thickness up to the next 1/4" and lumber less than 4/4 is rounded up to 4/4. Thus, a specified thickness of 3/4" will be treated as 4/4" material.
- You can enter fractional wood dimensions as decimals (12.75), or as conventional fractions (12 3/4). If you use fractions, just make sure you leave a space between any leading whole number and the fraction.
The Good Ol' Fashioned Way - The Formula to Calculate Board Feet
When you're looking at a stack of hardwood lumber and and you begin picking out the boards you like and want to buy, you'll notice that each board has a different width (W), and possibly a different length (L).
Let's say you've found two pieces in the 4/4 stack, one is 8" x 96" and the other is 4" x 96". Should you pay the same price for both pieces just because they're the same length?
Of course not. The first board has twice the amount of wood in it (it's twice as wide and the same length).
The unit of measurement for this kind of lumber, then, is the board foot. It's a measure of the volume of wood that's in a board.
Here's what woodworkers are getting excited about - Order Today
$6.99 $4.99 / Board Feet
$5.99 $4.49 / Board Feet
$10.00 $8.00 ea.


Data scientist – sounds cool right? People seem to be fascinated by my role and I constantly get asked about my day-to-day. Truth be told, no two data scientist roles are alike. Want to know more about a typical day of a data scientist at Genetec?
A good place to start is to ask: “What is a data scientist?”
A data scientist is a person who has expertise in the following areas:
- Mathematics, probability, and statistics
- Computer science
- Business/industry knowledge
Now the question is, how do these all relate to the creation of a “data scientist” role? Data scientists need to have expertise in computer science to extract, transform, and load (ETL) data. They also need to be experts in mathematics, probability, and statistics - so when they look at data, they can recognize which probability distribution or algorithm will possibly fit best and to be able to assess the quality of the fitting algorithm. Lastly, nothing can replace the knowledge of one’s domain. Being an expert in security systems, for example, is the only way to build data science-based systems for security.
What does a data scientist do at Genetec?
In my role, I mainly deal with two types of problems - structured vs unstructured.
Structured problems are problems where we know the output of the data. For example, automatic license plate recognition (ALPR) is a structured problem because when we train our algorithms we have a data set of raw ALPR images and the classified output (this is an image of license plate XYZ123 and the plate in at this location in the image). These are the most common problems in machine learning.
Unstructured problems are often related to “automatic” learning problems. When someone says: “I want the algorithm to teach itself” that’s usually a clue that you’re looking at an unstructured problem because we don’t know the ground truth. A big focus in unstructured problems is anomaly detection, identifying when something is outside the norm for the data, without knowing what the norm is in advance.
Structured problem example
Imagine our access control team is working on integrating a card reader. They’ve been noticing that this device occasionally spits out null (all 0’s) or corrupted credential IDs and they’d like to know why. So, what I would ask them for is a sample data set of normal activity with the following parameters:
- Firmware version
- Card type
- Time of day, etc.
Additionally, I’d ask them for another data set of the same parameters for when the reader gives “bad” data. I would then say that the first data set has an output of 1 [success] and the bad data set has an output of 0 [fail] and then combine the data sets together. A big part of data science is extracting and transforming the data then loading it into a new system.
Now I have a new data set with an extra parameter, this is called the response, which is  or . The parameters of the event which we collected are called covariates – this is the data we will use to see what causes a “bad” read. This would look something like:
Let's dig in a little deeper...
Since I’m dealing with a binary response variable, I’ll likely first try and model the problem as a Binomial distribution with a link function that links all the covariates together. The Binomial distribution is a probability distribution for binary [0/1] data. It says that a success occurs with probability p and a failure occurs with probability 1-p. Then I’ll model p as a generalized linear model (GLM) using the link function:
The things we don’t know here are the set of β variables, which we will learn from the sample data sets we received from the access control team. It is fit by a method called iteratively reweighted least squares which as a really neat side-effect tells us which β variables are important predictors for getting an output of 1 versus 0. Essentially it tells us which of the many factors we have asked for are most powerful for guessing if a bad event will occur.
Also, for the ones that are important, we can say how important and to what degree by having something like firmware versions 1 and 2 effects the rate of bad events. We can then, mathematically, show that firmware versions 1 and 2 makes for a 10% rise in the rate of corrupt or null credential reads [as an example]. So, we can report this to the manufacturer and work together to make a better product!
Unstructured problem example
Still reading? Good! As I mentioned earlier, unstructured machine learning is the realm of machine learning which deals with data where we don’t know the correct output. For example, if you’re monitoring a stream of network data, you often don’t know what it normally should look like. Should there be that many camera connections? Does that make sense? Maybe it’s normal and I just want to look when things get weird.
One-way data scientists tackle problems like this is with something called anomaly detection. To do this, I first need to pick a mathematical model which I think represents the data best. For now, let’s assume the data we are dealing with is distributed according to a Guassian (normal) distribution. This is one of the most common models applied to data because it has some very nice mathematical properties and can solve many types of problems.
Next, we need to “fit” the parameters of the model to the data. For a normal distribution, this means computing the mean and variance of the data. One we have estimated what we think are the mean and variance, we can pass each data point through a standard score test (also known as the Z-score or Z-value) or optionally compute something like the p-value. With either of these metrics, we can get a general understanding of if each point in the collection of data appears to be distributed from a normal distribution with the parameters we estimated. We lastly set a threshold on “acceptable” values for either the standard score or p-value and when we get a value outside our normal range we call that point an anomaly!
How do we work on a new problem?
The way in which these problems always start is by having another entity describing what they want the system to do for them. We then work together to (1) manage expectations about what is possible and (2) discuss alternate use cases which they may not even be aware of. After that, the next step is to receive a data set to work with. Even though I might think a problem is feasible, a data scientist never knows this until they see and work with the data. Data can have a multitude of problems ranging from missing attributes/properties to having a variation that is too large. When this happens, we follow the “data science process”.
The data science process
- Plot the data
- Does the plot seem to demonstrate anything?
- If no, return to (1.) and re-plot the data a different way or with different attributes
- If yes, continue to (3.)
- Take a guess at which distribution or mathematical model you want to try applying
- Fit the parameters of the model to the data
- If the model doesn’t fit, try another
- If the model seems to fit well, continue to step 5
- Convert the model (if necessary or possible) to a sequential method for online estimation/detection
- Use the model estimate along with new data to identify data points outside the “normal” range.
- Spend an additional 5000% time creating the support systems for a product (security, UX, services, APIs, etc.)
Data science products
Some products that I worked on at Genetec include:
This is a crime prediction and resource deployment software-as-a-service (SaaS) product which helps cities and law enforcement deploy their physical resources more efficiently based on predicted trends in crime. It utilizes a sequential algorithm to learn crime trends and then predicts forward given recent crime in a city. It was developed using the Chicago open-data portal.
Citigraf Command utilizes a high-tech correlation engine to identify, in high-dimensions, data points which may be related to another (including events). It works in real-time to associate data together to help law enforcement and public safety build a complete picture of an event.
This is a feature which is being developed to be included in our System Availability Monitor (SAM) SaaS product. It will help integrators and IT professionals’ asses the future health of their systems and notify an operator how long they have until the system heads towards a critical failure.
Want to become a data scientist at Genetec?
If you’re still reading this far, then that might mean you have a real interest in a job in the field of data science! Check out our careers page to view our available positions.
About the AuthorFollow on Linkedin More content by Sean Lawlor


Seventh grade students are engaged in active learning and their different learning styles are addressed throughout the year. Seventh grade strengthens computation skills of whole numbers, decimals, fractions and integers and introduces algebra. The text used is McDougal Littell, Math Course 2.
This course focuses on the processes of solving algebraic equations, inequalities, and pre-geometry skills. Our math text is McDougal Littell Math Course 3.
FIRST IN MATH PROGRAM
The First In Math Online Program provides deep practice to rapidly increase students’ computation, problem-solving, critical-thinking, pattern recognition, and other essential math skills. It includes comprehensive content presented in the game format, but engages all students in serious mathematics. Use of FIM for minutes a day, in the classroom or at home, increases math skills exponentially over time.
In 2012-2013 school year, BJKM Junior High students solved 2,012,586 math problems on this program finishing first in the State of Ohio and fourth in the nation.
In the 2013-2014 school year, students solved 2,480,898 problems finishing, once again, first in the State of Ohio and third in the nation out of over 6,000 schools.


Algebra Club? That will never work.
Or so I was told by a few people when I suggested the idea a few years ago. I envisioned a club, not a class, for homeschooled students; a supplement to studying algebra rather than a main source for learning algebra. We wouldn’t have a textbook, homework to grade, nor any quizzes and tests. Instead I envisioned this as a way to make algebra more fun by adding a social element to it, some bits of history trivia and stories, some hands-on activities, manipulatives and games that would work better with a group of children than with just my two teens alone. These were the parts of teaching math that I loved when I was a classroom teacher, but we rarely had time in the curriculum for these as testing became more and more the emphasis of public schools.
I was told that the name alone would scare people off, but I advertised it and opened it up to anyone who was working on at least middle school math. (I thought that those who weren’t actually studying algebra yet could enjoy the club and get a lot out of it as long as they were almost ready, academically, to study algebra.) I charged $5 per child for the school year to help cover the costs of photocopies and other materials I’d put into it, but most of the expensive materials–such as Algebra Tiles, an Algebra Balance, and various books and resources–that I used were ones I already owned. We meet twice a month for about an hour and a half each time.
A lot of kids came to the first meeting. Some never returned, but we ended up with a nice number, about a dozen students, who stuck with it all year and even returned the following year when I ran a somewhat similar club that I called “Geometry Club.”
While we had no graded homework, I did hand out photocopied pages from an out-of-print reproducible source called “Algebra with Pizzazz” that included lots of practice problems whose answers helped solve very lame riddles. They were fun and if they completed a page successfully, I put a sticker on the page. (If they didn’t do them, nothing happened. At least nothing happened in our club, though I can’t guarantee that their parents didn’t get upset about it sometimes.) If they finished several of them successfully, I handed out small prizes such as fun erasers, colorful little rulers with designs in them, etc.
We played bingo to practice working with integers. I used a toy car and a ruler to demonstrate how the slope of a line could be negative, positive, zero, or impossible. I read stories about famous mathematicians or famous math problems. I used brain teasers and group problems to get them to play with math ideas. We acted out some word problems to help students really understand them. We worked through a variety of SAT (the college entrance exam) math problems and learned a few tricks for dealing with them, and they learned to use some of the more advanced features of scientific calculators. We used games such as 24 (The Algebra version), Equations, and a math game that resembled Trivial Pursuit.
We met in the clubhouse of the neighborhood of one of the participating kids. This was great as the place had several tables and chairs available and they charged no rent. There was even a playground outside the clubhouse where younger siblings could play during the club (and the older ones often ran off a bit of steam right after the club meeting, too). The Clubhouse was air-conditioned and heated, came with plenty of parking, and was mostly empty during school days so it worked well for us.
When we moved on to Geometry Club, we used models of geometric solids to explore shapes and their properties. We measured the heights of trees and flag poles indirectly. We played with Tangoes (tangrams), cut shapes from paper to explore their properties, made mobius strips and Platonic solids, etc. while we continued working on SAT problems, brain teasers, and group problem-solving.
I’m convinced that getting a solid grasp of math facts is important in math, but teaching kids to enjoy math, learn to play with math concepts, and find ways to explore math problems with other people are all important ways to create good mathematicians.
Perhaps you don’t have an Algebra Club or other math club near you, but that doesn’t mean that you can’t make an effort to supplement the curriculum occasionally. Take an occasional word problem and go act it out–preferably outside or with objects that will make the situation seem more realistic. Find some fun ways to teach lessons–for example, there are lots of ideas online for using Lego bricks to teach math concepts. Find some fun puzzles to do using math–there are magazines that specialize in math puzzles–or get a board game or two that will put some math into practice in a format that’s more fun. Let the kids see you working on projects that involve math–whether balancing a checkbook, multiplying a recipe, or trying to determine how much paint to buy to paint the entire house, or some other such, these can really make math seem less of a drudge and more of a useful tool that kids will want to know.