1.1 Comparing the straight line and linear regression
1.2 The meaning of linear regression
1.3 Uses of linear regression

2.1 Scatter plot and its uses
2.2 Correlation coefficient
2.3 Test for correlation coefficient

3.1 General concepts
3.2 Method of estimation
3.3 Least squares estimation of the parameters
3.4 Estimation of error variance
3.5 Partitioning total variability
3.6 Hypothesis testing on the slope and intercept
3.7 Confidence interval on the slope and intercept
3.8 Interval estimation on the mean response
3.9 Predictions and new observations
3.10 Coefficient of determination
3.11 Regression through the origin

Topic 4: Check on Model Adequacy
4.1 Definition of residuals
4.2 Normal probability plot
4.3 Plot of residuals against fitted values
4.4 Plot of residuals against independent variable
4.5 F Test for lack-of-fit
4.6 Transformations

Topic 5: Multiple Linear Regression
5.1 General concepts
5.2 Least squares estimation of the parameters
5.3 Hypothesis testing
5.4 Confidence interval estimation on the regression coefficients
5.5 Coefficient of multiple determination
5.6 Partial-F test
5.7 Variable selection methods (Stepwise, Forward Selection, Backward Elimination)
5.8 Multicollinearity
5.9 Indicator variables
5.9.1 General Concepts
5.9.2 Least squares estimation of parameters for indicator variables
5.9.3 Uses of indicator variables

A1 : Correlation quantifies the strength of the linear relationship between a pair of variables, whereas regression expresses the relationship in the form of an equation