Simple Linear Regression

1. Simple Linear
Regression
Presented by
Ms.A.Jasmine Anitha
Assistant Professor
Sri Ramakrishna College Arts & Science

2. Dependent vs. Independent Variables
Dependent Variable
The variable you want to predict or explain. Its value depends
on the independent variable.
Independent Variable
The variable used to predict or explain changes in the
dependent variable. It is the predictor.

3. Checking Data Assumptions
& Preparation
1 Linearity
The relationship between
variables must be linear.
2 Normality
Residuals should be normally
distributed.
3 Homoscedasticity
Variance of residuals should be constant.

4. Running the Analysis: Step-
by-Step
Analyze
Go to Analyze > Regression > Linear.
Variables
Assign dependent and independent variables.
Statistics
Select desired statistics like R-squared, ANOVA.
Plots
Request plots for assumption checking.

5. Model Summary and ANOVA Tables
Model Summary
R-squared indicates variance
explained by the model.
ANOVA
Tests the overall significance of the
regression model.
Significance
A p-value less than 0.05 means the
model is significant.

6. Coefficients and Regression
Equation
1 Coefficients Table
Shows the intercept and slope coefficients.
2 Equation
Y = Intercept + Slope * X. Use to predict Y.
3 Significance
P-values indicate the significance of each predictor.

7. Visualizing Results
Scatterplots
Examine the relationship between X and Y.
Residual Plots
Check for homoscedasticity and normality.