Why is intercept important in regression analysis?
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- The dependent variable should be a scalar variable. Examples of scalar variables include height, salaries and age.
- The independent variables should be scalar or categorical variables (sometimes referred as nominal variables). For example, gender is a categorical variable which has two categories- male and female.
- The data should not have any outliers. This can be checked by using various methods including histogram and boxplot techniques. Also, the residuals of the regression line should be normally distributed.
- The relationship between the dependent and independent variables should be linear. We can use scatterplot to verify the linearity in our data.
- The data needs to showcase homoscedasticity, which means the variance around the regression line is same for all the values of the independent variables.
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