You may find this pdf useful to get started with R.
Definition of the Simple Regression Model
Deriving the Ordinary Least Squares Estimates
Properties of OLS
Fitted values and residuals
Algebraic properties of OLS statistics
Goodness-of-fit
Units of Measurement and Functional Form
Expected Values and Variances of the OLS estimators
Hypothesis testing
Sampling Distribution of the OLS estimators
Testing hypothesis about a single population parameter
Testing against one-sided alternatives
Two-sided alternatives
Computing p-values for t-tests
Confidence intervals
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
Interpreting the OLS regression equation
Meaning of “holding other factors fixed’’
Changing more than one independent variable simultaneously
Partialling out interpretation of multiple regression
Comparison of simple and multiple regression estimates
Goodness-of-fit
Expected values of the OLS estimates
Including irrelevant variables in a regression model
Omitted variable bias
Variance of the OLS estimators
Components of the OLS variances, multicollinearity
Misspecified models
Efficiency of OLS
Testing multiple linear restrictions; the F - test
Binary (or Dummy) Variables
A single dummy independent variable
Using dummy variables for multiple categories
Interactions involving dummy variables