We have learnt about time-series data and regression process with different data transformation techniques in the introduction section. This chapter explains the importance of hypothesis testing in OLS regression. Hypothesis(es) helps determine whether the estimated coefficients significantly differ from zero and if a meaningful relationship exists between variables. The process involves setting up null and alternative hypotheses and using t-tests or F-tests to evaluate them based on p-values and a chosen significance level (α). According to the Gauss-Markov theorem, OLS estimators are the Best Linear Unbiased Estimators (BLUE) under key assumptions: linearity, no endogeneity, no simultaneity, normality, homoscedasticity, no autocorrelation, and no multicollinearity. To ensure a reliable model, various diagnostic tests are used to assess these assumptions. These include checks for multicollinearity, linearity, normality of residuals, homoscedasticity, independence of errors, outliers or influential points, and overall model fit.