Learn about goodness of fit, mean squared error, R squared statistic, F statistic, ANOVA, and overfitting.
- [Instructor] I have copied some code from the last video…to load models, set up plotting,…and create three linear models.…I'll execute all the cells.…So here are plots of predictions…on top of data for two of the models.…Surviving, on top, includes constant terms for every region,…plus a single main term, a slope, for age five survival.…
Surviving population by region includes the constant terms,…a different slope for every region,…and a main term for population.…How can we tell which model is better?…In this section, we will be looking at…classical measure of goodness of fit,…such as mean square residual, R², the F statistics,…and ANOVA tables.…All of these and many more have been developed…in the analytics-heavy pre-computer era,…so they do require some mathematical development…to be understood fully.…
The first thing we can do, though,…is to plot the actual residuals.…This is saved in the StatsModels fit results object.…So we can do a scatter plot of an explanatory variable…against the residuals.…And let's overlay two more.…
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
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