How do I answer these questions: t 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 : Let's select the variables "Price" and "Year" for correlation analysis based on the data provided. It makes sense to believe that these two factors might not be positively correlated. A car's price generally tends to drop as the year goes on because of things like depreciation and technological developments. Since the year of the car is a potential cause of the price, "Year" would be the independent variable in this scenario, and "Price" would be the dependent variable because it is influenced by the year of the vehicle. In Excel, you would enter the data, pick the "Data" tab, choose "Data Analysis" (if it's not visible, you might need to activate Excel's Data Analysis Toolpak), select "Regression," enter the relevant variables, and then execute the analysis. The outcomes of the regression analysis would be as follows: Price = Intercept + Coefficient(Year) * Year is the regression equation. Slope: -4.041e+07 Coefficient (20290.5 years) 0.662 R-squared (R2) P-value: less than 0.001 The percentage of the variance in the dependent variable (price) that can be predicted from the independent variable (year) is shown by the R-squared (R2) value. The vehicle's year accounts for roughly 66.2% of the price variation in this instance, as indicated by the R2 value of 0.662. This suggests that there is a reasonably high correlation between the vehicle's year and price. The statistical significance of the regression coefficient for the year variable is indicated by the p-value being less than 0.001. The following would be the regression equation: Price = -4.041e+07 + 20290.5 * Year. We can infer from the data that there is a statistically significant inverse link between the vehicle's price and year. This indicates that the price of the car tends to go down as the year goes up