| How to add a polynomial term to a model |
using sklearn |
1 (view) |
| How to add a transformed term to a model |
using NumPy and sklearn |
1 (view) |
| How to add details to a plot |
using Matplotlib |
1 (view) |
| How to analyze the sample means of different treatment conditions |
using Matplotlib and Seaborn |
1 (view) |
| How to change axes, ticks, and scale in a plot |
using Matplotlib |
None |
| How to check the assumptions of a linear model |
using NumPy, SciPy, sklearn, Matplotlib and Seaborn |
1 (view) |
| How to choose the sample size in a study with two population means |
using statsmodels |
1 (view) |
| How to compare two nested linear models |
using statsmodels |
1 (view) |
| How to compute a confidence interval for a mean difference (matched pairs) |
using NumPy and SciPy |
1 (view) |
| How to compute a confidence interval for a population mean |
using SciPy |
2 (view) |
| How to compute a confidence interval for a population mean using z-scores |
using SciPy |
1 (view) |
| How to compute a confidence interval for a regression coefficient |
using statsmodels |
1 (view) |
| How to compute a confidence interval for a single population variance |
using SciPy |
1 (view) |
| How to compute a confidence interval for the difference between two means when both population variances are known |
using NumPy and SciPy |
1 (view) |
| How to compute a confidence interval for the difference between two means when population variances are unknown |
using NumPy and SciPy |
1 (view) |
| How to compute a confidence interval for the difference between two proportions |
using SciPy |
1 (view) |
| How to compute a confidence interval for the expected value of a response variable |
using statsmodels and sklearn |
1 (view) |
| How to compute a confidence interval for the population proportion |
using SciPy |
1 (view) |
| How to compute a confidence interval for the ratio of two population variances |
using SciPy |
1 (view) |
| How to compute adjusted R-squared |
using statsmodels |
1 (view) |
| How to compute covariance and correlation coefficients |
using pandas and NumPy |
1 (view) |
| How to compute probabilities from a distribution |
using SciPy |
3 (view) |
| How to compute R-squared for a simple linear model |
using SciPy |
2 (view) |
| How to compute summary statistics |
using pandas and NumPy |
3 (view) |
| How to compute the derivative of a function |
using SymPy |
1 (view) |
| How to compute the domain of a function |
using SymPy |
None |
| How to compute the error bounds on a Taylor approximation |
using SymPy |
None |
| How to compute the limit of a function |
using SymPy |
None |
| How to compute the power of a test comparing two population means |
using statsmodels |
1 (view) |
| How to compute the residuals of a linear model |
using statsmodels |
1 (view) |
| How to compute the standard error of the estimate for a model |
using statsmodels |
1 (view) |
| How to compute the Taylor series for a function |
using SymPy |
None |
| How to conduct a mixed designs ANOVA |
using pandas and pingouin |
1 (view) |
| How to conduct a repeated measures ANOVA |
using pandas and pingouin |
1 (view) |
| How to convert a text column into dates |
using pandas |
1 (view) |
| How to create a box (and whisker) plot |
using Matplotlib |
1 (view) |
| How to create a data frame from scratch |
solution |
1 (view) |
| How to create a histogram |
using Matplotlib |
1 (view) |
| How to create a QQ-plot |
using SciPy, using statsmodels |
1 (view) |
| How to create basic plots |
using Matplotlib |
1 (view) |
| How to create bivariate plots to compare groups |
using Matplotlib and Seaborn |
1 (view) |
| How to create symbolic variables |
using SymPy |
None |
| How to define a mathematical sequence |
using SymPy |
None |
| How to define a mathematical series |
using SymPy |
None |
| How to do a goodness of fit test for a multinomial experiment |
using SciPy |
1 (view) |
| How to do a hypothesis test for a mean difference (matched pairs) |
using SciPy |
1 (view) |
| How to do a hypothesis test for a population proportion |
using SciPy |
1 (view) |
| How to do a hypothesis test for the difference between means when both population variances are known |
using SciPy |
1 (view) |
| How to do a hypothesis test for the difference between two proportions |
using SciPy |
1 (view) |
| How to do a hypothesis test for the mean with known standard deviation |
using SciPy |
1 (view) |
| How to do a hypothesis test for the ratio of two population variances |
using SciPy |
1 (view) |
| How to do a Kruskal-Wallis test |
using SciPy |
1 (view) |
| How to do a one-sided hypothesis test for two sample means |
using SciPy |
1 (view) |
| How to do a one-way analysis of variance (ANOVA) |
using SciPy |
2 (view) |
| How to do a Spearman rank correlation test |
using SciPy |
1 (view) |
| How to do a test of joint significance |
using statsmodels |
1 (view) |
| How to do a two-sided hypothesis test for a sample mean |
using SciPy |
2 (view) |
| How to do a two-sided hypothesis test for two sample means |
using SciPy |
2 (view) |
| How to do a two-way ANOVA test with interaction |
using statsmodels |
1 (view) |
| How to do a two-way ANOVA test without interaction |
using statsmodels |
1 (view) |
| How to do a Wilcoxon rank-sum test |
using SciPy |
1 (view) |
| How to do a Wilcoxon signed-rank test |
using SciPy |
1 (view) |
| How to do a Wilcoxon signed-rank test for matched pairs |
using SciPy |
1 (view) |
| How to do basic mathematical computations |
using NumPy, using SymPy, solution |
3 (view) |
| How to do implicit differentiation |
using SymPy |
None |
| How to find the critical numbers of a function |
using SymPy |
None |
| How to find the critical points of a multivariate function |
using SymPy |
None |
| How to fit a linear model to two columns of data |
using SciPy, using statsmodels |
2 (view) |
| How to fit a multivariate linear model |
using statsmodels |
1 (view) |
| How to generate random values from a distribution |
using SciPy |
3 (view) |
| How to graph a two-variable function as a surface |
using SymPy |
None |
| How to graph curves that are not functions |
using SymPy |
None |
| How to graph mathematical functions |
using NumPy and Matplotlib, using SymPy |
1 (view) |
| How to graph mathematical sequences |
using SymPy and Matplotlib |
None |
| How to isolate one variable in an equation |
using SymPy |
None |
| How to perform a chi-squared test on a contingency table |
using SciPy |
2 (view) |
| How to perform an analysis of covariance (ANCOVA) |
using pingouin |
1 (view) |
| How to perform pairwise comparisons |
using statsmodels |
1 (view) |
| How to perform post-hoc analysis with Tukey’s HSD test |
using statsmodels and Matplotlib |
2 (view) |
| How to plot continuous probability distributions |
using SciPy |
3 (view) |
| How to plot discrete probability distributions |
using SciPy |
2 (view) |
| How to plot interaction effects of treatments |
using Matplotlib and Seaborn |
1 (view) |
| How to predict the response variable in a linear model |
using statsmodels |
1 (view) |
| How to quickly load some sample data |
solution |
2 (view) |
| How to solve an ordinary differential equation |
using SymPy |
None |
| How to solve symbolic equations |
using SymPy |
None |
| How to substitute a value for a symbolic variable |
using SymPy |
None |
| How to summarize a column |
solution |
2 (view) |
| How to summarize and compare data by groups |
solution |
1 (view) |
| How to test data for normality with the D’Agostino-Pearson test |
using SciPy |
None |
| How to test data for normality with the Jarque-Bera test |
using SciPy |
None |
| How to test for a treatment effect in a single factor design |
using SciPy and statsmodels |
1 (view) |
| How to write a piecewise-defined function |
using SymPy |
None |
| How to write an ordinary differential equation |
using SymPy |
None |
| How to write and evaluate definite integrals |
using SymPy |
None |
| How to write and evaluate indefinite integrals |
using SymPy |
None |
| How to write and evaluate Riemann sums |
using SymPy |
None |
| How to write symbolic equations |
using SymPy |
None |
