what is simple linear regression equation

Other assumptions: For each value of the independent variable, the distribution of the dependent variable must be normal.Categorical variables, such as religion, major field of study or region of residence, need to be recoded to binary (dummy) variables or other types of contrast variables. Data: Dependent and independent variables should be quantitative.

Plots: Consider scatterplots, partial plots, histograms and normal probability plots.Also, consider 95-percent-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. 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.For each variable: Consider the number of valid cases, mean and standard deviation.Assumptions to be considered for success with linear-regression analysis:

What is simple linear regression equation