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Normality: the data follows a normal distribution.

Homoscedasticity: the size of the error in our prediction does not change significantly across the independent variable values.

The variance of the dependent variable for each independent variable value should be normal and constant.

Variable data: the data like religion, gender, the cast should be measured quantitatively using some dummy variables.

Consider various plots like scatterplots, histograms, partial plots and probability plots.

For each model, consider: regression coefficients, correlation matrix, part and partial correlations, multiple R, R2, adjusted R2, change in R2, standard error of the estimate, analysis-of-variance table, predicted values and residuals, 95-per cent-confidence intervals for each regression coefficient, variance-covariance matrix, variance inflation factor, tolerance, Durbin-Watson test, distance measures (Mahalanobis, Cook and leverage values), DfBeta, DfFit, prediction intervals and case-wise diagnostic information.

If the dependent variable is modeled as a non-linear function because the data relationships. If you use two or more explanatory variables to predict the dependent variable, you deal with multiple linear regression.

For each variable, consider a number of valid cases, mean and standard deviation. Simple linear regression models the relationship between a dependent variable and one independent variable using a linear function.

Excel linear regression output