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Related to Chapter 6: Regression Analysis Explain in detail the difference between simple and multiple regression analysis. Related to Chapter 6:...

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#1. Related to Chapter 6: Regression Analysis Explain in detail the difference between simple and multiple regression analysis. #2. Related to Chapter 6: Regression Analysis The manager of a large banking organization would like to develop a model to predict the total amount of time per day it takes to process all invoices. He collects data from a sample of 30 days and obtains the number of invoices processed per day and their total daily processing time in hours. He runs a regression model and finds: the estimated intercept b 0 = 15, the estimated slope b 1 = 0.5, the coefficient of determination R 2 = 0.48, and the 95% confidence interval for the slope to be between 0.2 and 0.8.Thus, the linear regression equation can be written as follows: X Y 5 . 0 15 ˆ + = a) Which variable was used as the dependent variable Y according to the business problem above? b) Which variable was used as the independent variable X according to the business problem above? c) Using your business knowledge, what can you say about the value of the computed intercept? Does it make business sense? d) At a 95% confidence level, is there a linear relationship between X and Y in the business problem above? Why? e) Write the null and alternative hypotheses you applied in part (d) above. f) If X = 0, what is the predicted value of Y? Does the answer make sense considering the business problem of the manager? Why? g) If X = 3000, what is the predicted value of Y? Does the answer make sense considering the business problem of the manager? Why? h) Do you think the manager collected the correct variable X that influences Y? If yes, why? If no, why not?
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i) Is there any other X variable you think the manager should add to improve his model? If yes, what other X variable would you recommend and why? If no, why not? j) If we add another X variable, what would happen to the above regression results, in general? #3. Related to Chapter 6: Regression Analysis A real estate agent in Los Angeles hired an assistant to collect data of houses in the city of Los Angeles and devise a forecasting model in order to be able to set the future selling prices of houses. The assistant collects the data of 42 houses, devises a forecasting model called “Regression Analysis” and with the help of Excel 2007 computes the regression model (from Excel menu he clicks on “Data” clicks on “Data Analysis” selects “Regression” from the list selects the data points for the Y and X variables and marks “Confidence Level = 95%”). The Excel output is the following: Regression Analysis Regression Statistics Multiple R 0.7455 R Square 0.5558 Adjusted R Square 0.5330 Standard Error 7212 Observations 42 ANOVA df SS MS F Significance F Regression 2 2537650171 1268825085 24.3954 0.0000 Residual 39 2028419591 52010759 Total 41 4566069762 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 47331 13884 3.4090 0.0015 19248 75415 House Age (in years) -825 607 -1.3587 0.1820 -2054 403 Square Feet 40.91 6.70 6.1093 0.0000 27.37 54.46 Another real estate agent, one from Newport Beach, becomes aware of the sophisticated model the assistant is analyzing, so he offers a higher paying job to the assistant. The assistant leaves the job in Los Angeles before writing a conclusion about the data
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