R-square：represents the proportion of the variance for a dependent variable that's explained by variables in a regression model.
adjusted R-square
analyze the central tendency of data: Mean, Median and Mode
Central Limit Theorem: If n is large, the sampling distribution of y_bar is approximately normal,  regardless of the distribution of y

Hypothesis tests

t-test: test means of 2 sample independent & same variance, two samples are paired and dependent, 
F test: compare between-group variability and within-group variability.
(e vs X) residual plot: U shape or inverted U shape non-random, constant variance

Diagnostic Plots

residual vs time: randomly distributed suggests no serial correlation
Q-Q plot checks whether residuals are normally distributed as the points lie on the line y = x.
Residuals vs Fitted plot: the red line suggests that the residuals seem to have no obvious curved pattern, which means trying a model with a quadratic term included will not help.

Assumptions of Linear Regression

  1. the relationship between the independent and dependent variables to be linear
  2. all variables to be multivariate normal
  3. No or little multicollinearity in the data
  4. little or no autocorrelation in the data. Occurs when the residuals are not independent of each other
  5. requires variances of residuals to be constant