Advertising fees are an important source of revenue for any newspaper. In an attempt to boost these revenues and to minimize costly errors, the management of the Herald has established a task force charged with the business objective of improving customer service in the advertising department. Using a web browser, visit this books companion web site and open the web page for the Chapter 2 Herald Case or open Ad_Errors.htm, if you have downloaded the Herald Case files.
Review the task force's data collection. Identify the variables that are important in describing the customer service problems. For each variable you identify, construct the graphical representation you think is most appropriate and explain your choice. Also, suggest what other information concerning the different types of errors would be useful to examine. Offer possible courses of action for either the task force or management to take that would support the goal of improving customer service.
1. For the variable you identify, compute the appropriate numerical descriptive measures and construct in a boxplot.
2. For the variable you identify, identify another graphical display that might be useful and construct it. What conclusions can you reach from this other plot that cannot be made from the boxplot?
Please refer to the Excel attachment.
We have selected the variable 'Help Desk Calls' for the statistical analysis. Please refer to the EXCEL sheet for the numerical descriptive measures and the box-plot.
We have constructed a ...
The solution provides a detailed statistical analysis of the given data.
Statistics Problems - Regression Analysis, Autocorrelation, Multicollinearity
1. Suppose an appliance manufacturer is doing a regression analysis, using quarterly time-series data, of the factors affecting its sales of appliances. A regression equation was estimated between appliance sales (in dollars) as the dependent variable and disposable personal income and new housing starts as the independent variables. The statistical tests of the model showed large t-values for both independent variables, along with a high r2 value. However, analysis of the residuals indicated that substantial autocorrelation was present.
a. What are some of the possible causes of this autocorrelation?
b. How does this autocorrelation affect the conclusions concerning the significance of the individual explanatory variables and the overall explanatory power of the regression model?
c. Given that a person uses the model for forecasting future appliance sales, how does this autocorrelation affect the accuracy of these forecasts?
d. What techniques might be used to remove this autocorrelation from the model?
2. Suppose the appliance manufacturer discussed in Exercise 1 also developed another model, again using time-series data, where appliance sales was the dependent variable and disposable personal income and retail sales of durable goods were the independent variables. Although the r2 statistic is high, the manufacturer also suspects that serious multicollinearity exists between the two independent variables.
a. In what ways does the presence of this multicollinearity affect the results of the regression analysis?
b. Under what conditions might the presence of multicollinearity cause problems in the use of this regression equation in designing a marketing plan for appliance sales?View Full Posting Details