Multiple Regression Analysis with R

Last Update: September, 2019

1. Course Objective

Learn Multiple Regression Analysis main topics using R statistical software® in this practical course for all knowledge levels. Feel free to take a look at Course Curriculum.

2. Skills Learned

At the end of this course you will know how to:

  • Define dependent variable, independent variables and calculate their descriptive statistics (mean, standard deviation, skewness, kurtosis).
  • Analyze multiple regression statistics output (coefficient of determination or R squared, adjusted R squared, regression standard error), analysis of variance ANOVA (regression, residuals and total degrees of freedom, sum of squares, mean squared error, regression F statistic, regression p-value) and coefficients (values, standard errors, t statistics and regression coefficients p-values).
  • Evaluate regression correct specification (individual coefficients statistical significance), independent variables no linear dependence (multicollinearity test), correct functional form (Ramsey-RESET test), residuals no autocorrelation (Breusch-Godfrey test), residuals homoscedasticity (White test, Breusch-Pagan test) and residuals normality (Jarque-Bera test).
  • Correct regression correct specification (backward elimination stepwise regression), independent variables no linear dependence (correct specification re-evaluation), correct functional form (non-linear quadratic, logarithmic or reciprocal variables transformations), residuals no autocorrelation (adding lagged dependent variable data as independent variables to original regression) and residuals homoscedasticity (heteroscedasticity consistent standard errors estimation).
  • Assess regression forecasting accuracy (mean absolute error, root mean squared error, mean absolute percentage error) and compare it with benchmarks (random walk, arithmetic mean).

3. Typical Student

This course is ideal for you as:

  • Undergraduate or postgraduate who wants to learn about the subject.
  • Academic researcher who wishes to deepen your knowledge in data science, applied statistics, economics, econometrics or quantitative finance.
  • Business data scientist who desires to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.