low well do floor space area (Floorspace), the number of competing ads (CompetingAds), and price Price) predict the number of mountain bikes sold (UnitsSold)? The Bike Sales worksheet includes data or a random sample of 30 stores. Before studying the relationships among the above variables, your first step consists of examining each variable by itself. First, you will describe each of the four variables visually. Prepare a frequency listribution and a histogram for each of the four variables. You should have at least 10 bins for each variable. Move each histogram to a new worksheet. Provide a description of each histogram, specifying the modal class, any skewness, and any outliers. Next, you will describe each of the four variables numerically. Run descriptive statistics for each of the our variables. Create a new worksheet "Descriptives" and insert the results of the descriptive statistics n this new worksheet. Based on the mean and median for each variable, describe the data to indicate he presence or absence of any skewness. Also specify the direction of the skewness where applicable. After examining each variable by itself, you will now examine the relationships among the pairs of variables. Produce a correlation matrix to determine how the variables correlate with each other. Insert the correlation matrix in a new worksheet named "Correlation". Identify any correlation coefficient which is above 0.7. For that pair of variables, draw a scatter plot. Add a trendline to the scatter plot, as well as the R-squared (R') value. What does the R2-value suggest about the relationship between these wo variables? Finally, we will see how well these variables predict the number of mountain bikes sold. With three ndependent variables, there are three models that have a single independent variable (FloorSpace & UnitsSold; CompetingAds & UnitsSold; Price & UnitsSold), three that have two single independent variables (FloorSpace, CompetingAds, & UnitsSold; FloorSpace, Price, & UnitsSold; CompetingAds, Price & UnitsSold) and one with all three included (FloorSpace, CompetingAds, Price, & UnitsSold). Run these regression models (Note: for the models with two or more explanatory variables, you will need to earrange the independent variables such that they are next to each other when running the regression). For each of the seven models, write the regression equation. Also provide the following information for each model in the MR_Results worksheet: The regression coefficients . The t statistic for each coefficient, indicating with "Yes" or "No" the coefficients that are statistically significant at the 0.05 significance level or not respectively (Note: Use an IF function to display the text string "Yes" if the p-value (cell E3) is less or equal to 0.05, and "No" otherwise. cells F4 6-57 89.F10 F12.F14 F16-F18 E 27.F27
