Completely work the problems in Excel, conclusions and answers may be typed in Excel or Word #1 An electronics company is looking to develop a regression model to predict the number of units sold for a special running watch. Data on Sales, Price, Advertising expenditures, and whether or not there was a Holiday are provided below.| Sales (units) | Price ($) | Advertising ($) | Holiday ||---------------|-----------|------------------|---------|| 500 | $100.00 | $50.00 | Yes || 480 | $120.00 | $40.00 | Yes || 485 | $110.00 | $45.00 | No || 510 | $103.00 | $55.00 | Yes || 490 | $108.00 | $40.00 | No || 488 | $109.00 | $30.00 | No || 496 | $106.00 | $45.00 | Yes |a. Develop a multiple regression analysis using Price, Advertising, and Holiday as independent variables. To incorporate holiday, create a dummy variable as follows:D-H = {1 if Yes, 0 otherwise}b. Write the estimated regression equation.c. Explain the adjusted coefficient of determination.d. Test for significance of the overall model using α = 0.05.e. Test for significance in the relationship of each independent variable using α = 0.05.

Since the regression models always take numerical values as the input; therefore, convert the column Holiday into a dummy variable by replacing "Yes" with 1 and "No" with 0.a) By using the MS-Excel, use the following steps to perform the multiple linear regression model: Go to the Data tab. Under the Data tab, select Data Analysis. Under the Data Analysis, select Regression and click OK. Under the Input Y Range, select the column Sales. Under the Input X Range, select the columns Price, Advertising, and Holiday. Under Confidence Level, enter 95%, and click OK.b) The estimated regression equation is, Sales=596.0238-1.0931Price+0.2815Advertising+4.3399Holidayc) The adjusted coefficient of determination is a measure of the goodness of fit of the model. It measures the variation in the dependent variable that is explained by the model. It is the modified version of the coefficient of determination and unlike the coefficient of determination, its value only increases if the new added independent variable improves the model. It ranges between 0 and 1. A model with adjusted R2 equals 0 indicates that the model has no predictive value and a model with adjusted R2 equals 1 indicates that the model perfectly predicts the target variable. Here, the adjusted coefficient of determination is 0.6968, it implies that about 69.68% of the total variability in the Sales is explained by the model. d) For testing the overall significance of the regression model, the null and the alternative hypothesis are: H0:β1=β2=β3=0H1:At least β is not equal to zero. From the output table, the p-value for testing the overall significance of the model is 0.0955, which is greater than the level of significance 0.05; therefore, the null hypothesis does not get rejected. Hence, at 5% level of significance, there is insufficient evidence to conclude that the overall regression is statistically significant. e) For testing the significance of each independent variable, the null and the alternative hypothesis are: H0:βi=0H1:βi≠0 Where 'i' represents the ith independent variable. From the output table, the p-value corresponding to the independent variable "Price", "Advertising", and "Holiday" are 0.0899, 0.5659, and 0.4939, respectively. Since the p-value corresponding to each of the independent variables is greater than the level of significance 0.05; therefore, the null hypothesis does not get rejected for testing the significance of each of the independent variables. Hence, at 5% level of significance, there is sufficient evidence to conclude that none of the independent variables is related to the dependent ariable.