(LO 8-3) Download the Hiking Shoe Demand Predictors dataset in Excel which has hiking shoe sales over 48 months from January 2021 to December 2024. The companies believe that the three predictors of monthly sales are Gross Domestic Product

(GDP), the weather (average temperature) and whether it was the holiday season

(when gifts of hiking shoes are given). To the extent that we are able predict the GDP, the weather and/or the holiday season, we may be able to get a better prediction of future hiking shoe demand. (Hint: It may be helpful to complete Lab 8-4 and/

or Lab 8-5 before completing this problem).

Required 1. Run a regression with hiking shoe demand as the dependent variable, and GDP, weather (temperature) and holiday season (1 if holiday month, 0 otherwise). Take a screenshot of the regression output.

2. Using the adjusted R-square as an indicator, how good is the model fit?

3. Which variables have a p-value less than 5%, and are thus are significant predictors of hiking shoe demand?

4. In January 2025, assume the GDP is 111, the temperature is 34 degrees, but is not a holiday month. Using the regression results, estimate the product demand in January 2025. Round results to two digits.

5. In February 2025, assume the GDP is 111.5, the temperature is 37 degrees, but is not a holiday month. Using the regression results, estimate the product demand in February 2025. Round results to two digits.