BA 620 Spring 2022 Homework 1 Due by Monday, February 14th by 8:00 AM Submit your work on Canvas using the submission link under the Homework 1 module. All work should be provided in a single Word or...

this is a 4 quetion for an accounting class


BA 620 Spring 2022 Homework 1 Due by Monday, February 14th by 8:00 AM Submit your work on Canvas using the submission link under the Homework 1 module. All work should be provided in a single Word or pdf file. Please do not submit multiple files. [#1] Unknown population mean. Suppose a large manufacturing company is concerned that it offered salaries that were too high during the tight labor market of 2018-2019. The HR department obtained a random sample of 100 employees who were hired during this time period. In the sample, the average employee had a salary of $52,000. Assume you do not know the actual population mean, but you do know that the population standard deviation is equal to $10,000. Using a significance level of 0.05, answer the questions below: (a) Is there enough evidence to suggest that the average salary exceeded $50,000? Conduct a hypothesis using the test statistic method and interpret your result. (b) Re-do part (a) using the p-value method. (c) Is there enough evidence to suggest that the average salary differed from $50,000? Conduct a hypothesis using the test statistic method and interpret your result. (d) Re-do part (c) using the p-value method. [#2] Regression Analysis. A package delivery company is analyzing factors affecting shipping costs. Currently, market analysts are focusing on the roles of package weight and the distance it is shipped. Y = Cost = The cost of shipping a package (in dollars) X1 = Weight = The weight of a package (in pounds) X2 = Distance = The distance a package is shipped (in miles) (questions on next page) (…#2 continued) (a) Interpret the (unstandardized) slope coefficient on Weight. Type the numerical value and explain what that exact number means. (b) Interpret the standardized slope coefficient on Distance. Type the numerical value and explain what that exact number means. (c) What is the value of the constant? Explain what it indicates. (d) Using the p-value method, conduct a hypothesis test for whether a linear relationship exists between Cost and Weight. Show all steps and use a 5% significance level. (e) Using the test statistic method, conduct a hypothesis test for whether a linear relationship exists between Cost and Distance. Show all steps and use a 5% significance level. [#3] Regression Analysis, part 2. Suppose you work for a manufacturing company. A regression model for employee salaries is shown above. The variables included in this new regression model are: Y = Salary = Current annual salary in dollars. X1 = Years_Previous_Experience = Number of years of relevant experience prior to coming to the company. X2 = Years_Employed = Number of years employed by the company. X3 = Years_Education = Number of years of education beyond high school. X4 = Number_Supervised = Number of employees supervised by this employee. X5 = Female = Indicator variable equal to “1” if the employee is female (base category is male). X6 = Department: Purchasing = Indicator variable equal to “1” if the employee works in the Purchasing department (base category is Sales department). X7 = Department: Advertising = Indicator variable equal to “1” if the employee works in the Advertising department (base category is Sales department). X8 = Department: Engineering = Indicator variable equal to “1” if the employee works in the Engineering department (base category is Sales department). (…#3 continued) a. After adding additional independent variables, what happened to the value of the Rsquare? Does this indicate that the regression model has been improved or made worse? Explain. b. Can you think of an omitted variable that could improve the explanatory fit of the model? Provide an example and explain your reasoning. c. Using the test statistic method, do a hypothesis test for whether a linear relationship exists between Number_Supervised and Salary. Show all steps to your hypothesis test and use a significance level of 5%. d. Using the p-value method, perform separate hypothesis tests for whether there is a statistically significant difference between salaries in each of the three departments compared to the Sales department. e. Can you think of an omitted variable that, if added to the model, could introduce multicollinearity? Provide an example and explain your reasoning. [#4] Concept questions. (a) What is a linear probability model? Provide an example of how it can be used to study something of interest to a manufacturing company. Provide examples of outcomes of interest for which a linear probability model would be appropriate to use. (b) If you think multicollinearity exists in the model, how can you identify it? (c) Read the Harvard Business Review article titled “Why Underdogs Frequently Come out on Top.” It is in the file labeled HW1_HBR. Discuss of a regression model could be used to study this topic.