This week we discuss the distinction between explanatory and predictive modeling and spotlight the workhorses of statistical modeling:
See you in class!
– Peter Bruce,
Chief Academic Officer, Author, Instructor, and Founder
The Institute for Statistics Education at Statistics.com
Explain or Predict?
Are you flummoxed by the profusion of assessment metrics for statistical models like linear regression? Typical multiple linear regression output will contain, in addition to a distribution of errors (residuals) and RMSE (root mean squared error), such values as R-squared, adjusted R-squared, t-statistics, F-statistics, P-values, degrees of freedom, plus more. How do you make sense of them all? […]
Problem of the Week
The Second Heads
A friend tosses two coins, and you ask “Is one of them a heads?” The friend replies “Yes.” What is the probability that the other is a heads? […]
These two courses cover the workhorses of statistical modeling, multiple linear regression and logistic regression.
Oct 4 – Nov 1: Regression Analysis
Regression Analysis is taught by Iain Pardoe, the author of Applied Regression Modeling, the popular text that is used in the course.
In Regression Analysis, you will learn how to
- Calculate a simple linear regression model, assess its performance and check assumptions
- Extend the model to multiple linear regression
- Transform predictors and response variables to improve model fit
- Deal with qualitative predictors, interactions and influential points
Oct 4 – Nov 1: Categorical Data Analysis
Categorical Data Analysis is taught by Brian Marx, Professor of Statistics at Louisiana State University, coordinating editor of Statistical Modelling: An International Journal, and co-author or co-editor of several books on statistical modeling.
In Categorical Data Analysis you will learn how to
- Work with RxC tables and test for independence, and equality of proportions
- Fit logistic models for binary data
- Fit Poisson models for count data
See you in class!
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