For context, this was the question given before the question I am asking help for. I don't know if you need this information or not but providing just in case.

In EViews, regress the simple returns on the continuously compounded returns for the stock data. Set the constant intercept term to 0 by not including it in your regression equation.

Answer:

Dependent Variable: TOR_SIMP

Method: Least Squares

Date: 07/06/22 Time: 21:37

Sample (adjusted): 6/16/2021 6/10/2022

Included observations: 250 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

TOR_COMP 1.001781 0.001174 852.9601 0.0000

R-squared 0.999658 Mean dependent var 0.000360

Adjusted R-squared 0.999658 S.D. dependent var 0.014390

S.E. of regression 0.000266 Akaike info criterion -13.62031

Sum squared resid 1.77E-05 Schwarz criterion -13.60623

Log likelihood 1703.539 Hannan-Quinn criter. -13.61464

Durbin-Watson stat 1.590958

This is the question I am looking help for: Using the two hypothesis-testing methods i.e via the test of significance approach or via the confidence interval approach, test the hypothesis that the two sets of stock returns are not significantly different from each other. Explain clearly and succinctly the conclusions you reach with regard to differences between simple returns and continuously compounded returns. Based on your conclusions, which set (simple or continuously compounded) would you use to test the CAPM?