Simple regression is a
statistical method used to
model the relationship
between a dependent variable
(y) and a single independent
variable (x). It helps predict
the value of y based on x.
Assumptions
1.Linearity – The relationship
between x and y is linear.
2. Independence – Observations
are independent of each other.
3. Homoscedasticity – The
variance of residuals (errors) is
constant.
4. Normality of Residuals – The
residuals follow a normal
distribution.
5. No Multicollinearity – Only
one independent variable is
used.
Steps in Hypothesis Testing
Formula