Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
1 total work
*Furnishes a thorough introduction and detailed information about the linear regression model, including how to understand and interpret its results, test assumptions, and adapt the model when assumptions are not satisfied.
*Uses numerous graphs in R to illustrate the model's results, assumptions, and other features.
*Does not assume a background in calculus or linear algebra; rather, an introductory statistics course and familiarity with elementary algebra are sufficient.
*Provides many examples using real world datasets relevant to various academic disciplines.
*Fully integrates the R software environment in its numerous examples.