How do I answer these questions: It is important to remember that typically a two-factor regression model cannot accurately describe the entire situation. Look at the dependent variable that your peer chose. Name at least 2 independent factors you would use to run a Multiple Linear Regression (MLR) and explain why you feel they are related. Then use those factors to run a Multiple Linear Regression (MLR) on your peer's data and see if the variables you chose are related to the dependent variable they chose. What is your MLR equation? Is your MLR significant? Are any of the Independent factors significant? What is the R2 value? Explain and interpret this value and how it relates to the MLR. Make sure you include your MLR Excel output as an attachment in your response post. to this peer post: Good evening class, Here we are at week 7! I hope you all are surviving like I am and once again the handy helpers really helped out step by step. For this week you will see my work below. I chose the two variables, PRICE and MPG (highway). I think that when the price of a vehicle goes up, the HWY MPGs will go down. I chose to correlate the two because I think that the more fancier cars tend to not be the practical since look as if the eye catcher is more of the driving factor. The independent variable or x price dependent variable or y will be MPG (highway). This will be due to the high way MPG being DEPENDENT on the price. My R value - 0.683082651629234 Agrees with the assumption I made of a negative correlation. My R squared value is 0.466601908956825 My p-value for my data is 0.466601908956825. Alpha for us 0.05, so my data shows that the results are 0.466601908956825 > 0.05. This is telling me that the price would not be a significant predictor of the highways MPG. The coefficient for my y intercept highway MPG is 112812.98 while my coefficient for price was -2427.1271. The formula for my regression is MPG (highway) = -2427.1271 (Price) + 112812.98. Please see the attached excel sheet below for my work shown for this weeks assignment. Make Model Price MPG (Highway) Year = -2427.12709205021 ( ) + 112812.980648536 Toyota Rav 4 $32,946 33 2021 = -2427.12709205021 ( ) + 112812.980648536 Toyota Camry $26,934 39 2024 GMC Acadia SLT $40,640 27 2023 Toyota 4 Runner $39,996 19 2023 Ford Explorer Limited $29,995 28 2020 Dodge Challenger SXT $29,582 30 2023 BMW X5 Sports Package $43,991 26 2020 Mercedes Benz GLE 53 AMG $63,995 22 2022 Dodge Durango SRT Hellcat $90,995 17 2023 Chevrolet Corvette $83,440 25 2024 RESIDUAL OUTPUT SUMMARY OUTPUT Regression Statistics Multiple R 0.683082652 R Square 0.466601909 Adjusted R Square 0.399927148 Standard Error 5.049398851 Observations 10 ANOVA df SS MS F Significance F Regression 1 178.42857 178.42857 6.998178911 0.029465038 Residual 8 203.97143 25.49642875 Total 9 382.4 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 35.87606775 3.852926978 9.311380141 1.44216E-05 26.99120221 44.7609333 26.99120221 44.7609333 Price -0.000192245 7.26711E-05 -2.645407135 0.029465038 -0.000359824 -2.46648E-05 -0.000359824 -2.46648E-05 RESIDUAL OUTPUT Observation Predicted MPG (Highway) Residuals Standard Residuals 1 29.54237944 3.457620559 0.726296441 2 30.69815356 8.30184644 1.743858652 3 28.06325002 -1.063250022 -0.223342814 4 28.1870555 -9.187055499 -1.929802765 5 30.10969305 -2.109693052 -0.443155207 6 30.18909004 -0.189090043 -0.039719635 7 27.4190386 -1.419038599 -0.298078597 8 23.57337901 -1.573379005 -0.330498837 9 18.38277667 -1.382776674 -0.290461536 10 19.8351841 5.164815896 1.084904298