1. Payroll Register

   The following data for Throwback Industries Inc. relate to the payroll for the week ended December 9, 20Y8:

   	Hours		Hourly		Weekly		Federal		Retirements
   Employee	Worked		Rate		Salary		Income Tax		Savings
   Aaron	43		$26				$266.11		$105
   Cobb	48		44				491.92		100
   Clemente	45		34				306.85		90
   DiMaggio	37		28				217.56		80
   Griffey, Jr.	41		42				366.03		70
   Mantle					$2,290		549.60		65
   Robinson	35		30				157.50		85
   Williams					2,560		563.20		90
   Vaughn	44		40				368.00		90

   Employees Mantle and Williams are office staff, and all of the other employees are sales personnel. All sales personnel are paid 1½ times the regular rate for all hours in excess of 40 hours per week. The social security tax rate is 6%, and Medicare tax is 1.5% of each employee's annual earnings. The next payroll check to be used is No. 901.

   Required:

   1. Prepare a payroll register for Throwback Industries Inc. for the week ended December 9, 20Y8. Assume the normal working hours in a week are 40 hours. Enter amounts as positive numbers. Round your intermediate calculations and final answers to the nearest whole cent (two decimal places).

   Payroll for Week Ending December 9, 20Y8
   		Earnings			Deductions Withheld					Paid		Accounts Debited
   Name	Total Hours	Regular	Overtime	Total	Social Security Tax	Medicare Tax	Federal Income Tax	Retirement Savings	Total	Net Pay	Ck. No.	Sales Salaries Expense	Office Salaries Expense
   Aaron	43	$	$	$	$	$	$266.11	$105	$	$	901	$
   Cobb	48						491.92	100			902
   Clemente	45						306.85	90			903
   DiMaggio	37						217.56	80			904
   Griffey, Jr.	41						366.03	70			905
   Mantle							549.60	65			906		$
   Robinson	35						157.50	85			907
   Williams							563.20	90			908
   Vaughn	44						368.00	90			909
   Totals		$	$	$	$	$	$	$	$	$		$	$

   2. Journalize the entry to record the payroll for the week. If required, round your answers to two decimal places. If an amount box does not require an entry, leave it blank.

Check My Work5 more Check My Work uses remaining.

Davis Stores sells clothing in 15 stores located around the southwestern United States. The managers at Davis are considering expanding by opening new stores and are interested in estimating costs in potential new locations. They believe that costs are driven in large part by store volume measured by revenue. During a discussion, one of the managers suggests that number of employees might be better at explaining cost than store revenues. As a result of that suggestion, managers collected the following information from last year’s operations (revenues and costs in thousands of dollars):

d-1. Enter the regression coefficients.

d-2. Estimate the cost of a store with 42 employees using the results from a simple regression of store cost on employees.

Enter the regression coefficients. (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

Estimate the cost of a store with 42 employees using the results from a simple regression of store cost on employees. (Round your intermediate calculations and final answer to 2 decimal places.)

[PYTHON] Modify the examaverages.py program included with this assignment so it will also compute the overall average test grade. E.g if there are 3 test each student must take and the user enters the following set of test scores for the two students…:

30, 40, 50 for the first student

50, 60, 70 for the second student

…then program will print the average for each student (i.e. 40 for the first student and 60 for the second student – the existing program already does this) and additionally will also print the overall average (i.e. (40 + 60) /2 = 50).

------------

this is the provided code from 'examaverages.py' :

##
#  This program computes the average grade for multiple students.
#

# Obtain the number of test grades per student.
numExams = int(input("How many grades does each student have? "))

# Initialize moreGrades to a non-sentinel value.
moreGrades = "Y"

# Compute average grades until the user wants to stop.
while moreGrades == "Y" :
   
   # Compute the average grade for one student.
   print("Enter the grades out of 100.")
   total = 0
   for i in range(1, numExams + 1) :
      score = int(input("test %d: " % i))   # Prompt for each test grade.
      total = total + score
      
   average = total / numExams
   print("The average is %.2f" % average)
   
   # Prompt as to whether the user wants to enter grades for another student.
   moreGrades = input("Enter grades for another student (Y/N)? ")
   moreGrades = moreGrades.upper()

Westerville Company reported the following results from last year’s operations:

At the beginning of this year, the company has a $287,500 investment opportunity with the following cost and revenue characteristics:

The company’s minimum required rate of return is 15%.

6. What is the ROI related to this year’s investment opportunity?

7. If the company pursues the investment opportunity and otherwise performs the same as last year, what margin will it earn this year? (Round your percentage answer to 1 decimal place (i.e .1234 should be entered as 12.3))

8. If the company pursues the investment opportunity and otherwise performs the same as last year, what turnover will it earn this year?

9. If the company pursues the investment opportunity and otherwise performs the same as last year, what ROI will it earn this year?

11. What is last year’s residual income?

PART I Starbucks launched its prepaid (debit) Starbucks Card in November 2001. The card, which holds between $5 and $500 can be used at any Starbucks location. Suppose Starbucks management wants to study the reasons why some people purchase debit cards with higher prepaid amounts than do other people. Suppose a study of 25 randomly selected prepaid purchasers is taken. Respondents are asked the amount of the prepaid card, the customer’s age, the number of days per month the customer makes a purchase at Starbucks, the number of cups of coffee that customer drinks per day, and the customer’s income. The data is given in the Excel spreadsheet. 1. Using these data, develop a multiple regression model to study how well the amount of the prepaid card can be predicted by the other variables and which variables seem to be more promising in doing the prediction. 2. Rerun your model based on the significant independent variables only and explain the results. 3. What sales implications might be evident from this analysis?

PART II Suppose marketing wants to be able to profile frequent visitors to a Starbucks store. Using the same data set already provided, develop a multiple regression model to predict days per month at Starbucks by Age, Income and Number of cups of coffee per day. 4. How strong is the model? 5. Which independent variables seem to have more promise in predicting how many days per month a customer visits Starbucks? 6. What marketing implications might be evident from this analysis?

PART I Starbucks launched its prepaid (debit) Starbucks Card in November 2001. The card, which holds between $5 and $500 can be used at any Starbucks location. Suppose Starbucks management wants to study the reasons why some people purchase debit cards with higher prepaid amounts than do other people. Suppose a study of 25 randomly selected prepaid purchasers is taken. Respondents are asked the amount of the prepaid card, the customer’s age, the number of days per month the customer makes a purchase at Starbucks, the number of cups of coffee that customer drinks per day, and the customer’s income. The data is given in the Excel spreadsheet.

1. Using these data, develop a multiple regression model to study how well the amount of the prepaid card can be predicted by the other variables and which variables seem to be more promising in doing the prediction.

2. Rerun your model based on the significant independent variables only and explain the results.

3. What sales implications might be evident from this analysis?

PART II Suppose marketing wants to be able to profile frequent visitors to a Starbucks store. Using the same data set already provided, develop a multiple regression model to predict days per month at Starbucks by Age, Income and Number of cups of coffee per day.

4. How strong is the model?

5. Which independent variables seem to have more promise in predicting how many days per month a customer visits Starbucks?

6. What marketing implications might be evident from this analysis?

Use the following information to answer the first 5 questions:

Suppose we wanted to estimate the average age of a certain group of divorced men during 1988 and took a random sample of (n = ) 100 giving us x =35.2 years old.

1. this is the symbol that represents the average age of the population
2. this is the amount of people in the sample
3. this is the average age found from our sample
4. this is the symbol we use to represent the average age of the sample
5. this is the population

Answer choices

(a) 35.2 (b) 100

(c) m (d) x

(e) the average age of a certain group of divorced men during 1988

Suppose we wanted to estimate the average age of a certain group of divorced men during 1988 and took a random sample of n = 7 and received the following ages: 37, 22, 31, 43, 39, 61, and 26.

6) Calculate . You can use a Google Spreadsheet if you’d like since you should know by now how to calculate averages this way. We will learn another way soon (which most of you know at this time from previous courses).

x = 37 years old Answer choices: (a) True (b) False

Use the following information to answer the next three questions and choose the best answer::

To determine the average height of Cornell University males, two Sociology students sampled 50 males, yielding an average height of 5’10”.

7) What is the population parameter of interest?

(a) two Sociology students

(b) 50 males

(c) the average height of Cornell University males

8) What is the sample?

(a) two Sociology students

(b) 50 males

(c) the average height of Cornell University males

9) What symbol would we use to represent the average height of 5’10” ?

(a) n

(b) x

(c) m

Decide if the following statements are descriptive or inferential.

10) About 85% of women in the US have a job.  

11) The mean grade on the MA 161 final given last semester was an 83.

12) Drinking bubble tea can raise cholesterol levels by 10%.  

Answer choices: (a) Descriptive (b) Inferential

Decide if the following statements are quantitative or categorical (if categorical whether they are binary)

13) The age of a groom on his wedding day.

14) Whether a spun penny lands on heads or tails.

Answer choices:

(a) Quantitative

(b) Categorical - not binary    

(c) Categorical - binary

15) Any x found is a parameter Answer choices: (a) True (b) False

Short Answer Writing Assignment All answers should be complete sentences.

In the Week 2 Lab, you found the mean and the standard deviation for the SLEEP variable for both males and females. Use those values for follow these directions to calculate the numbers again.

(From Week 2 Lab: Calculate descriptive statistics for the variable Sleep by Gender. Sort the data by gender by clicking on Data and then Sort. Copy the Sleep of the males from the data file into the Descriptive Statistics worksheet of the Week 1 Excel file. [Write down the mean and standard deviation.] These are sample data. Then, copy and paste the female data into the Descriptive Statistics workbook and do the same. Keep three decimal places.)

You will also need the number of males and the number of females in the dataset. You can actually count these in the dataset. Then use the Week 5 spreadsheet to calculate the following confidence intervals. The male confidence interval would be one calculation in the spreadsheet and the females would be a second calculation.

1. Give and interpret the 95% confidence intervals for males and a second 95% confidence interval for females on the SLEEP variable. Which is wider and why?

2. Give and interpret the 99% confidence intervals for males and a second 99% confidence interval for females on the SLEEP variable. Which is wider and why? We need to find the confidence interval for the SHOE SIZE variable. To do this, we need to find the mean and standard deviation with the Week 1 spreadsheet. Then we can the Week 5 spreadsheet to find the confidence interval. This does not need to be separated by males and females, rather one interval for the entire data set. First, find the mean and standard deviation by copying the SHOE SIZE variable and pasting it into the Week 1 spreadsheet. Write down the mean and the sample standard deviation as well as the count. Open the Week 5 spreadsheet and type in the values needed in the green cells at the top to find the confidence interval.

3. Give and interpret the 95% confidence interval for the size of students’ shoes. Change the confidence level to 99% to find the 99% confidence interval for the SHOE SIZE variable.

4. Give and interpret the 99% confidence interval for the size of students’ shoes.

5. Compare the 95% and 99% confidence intervals for the size of students’ shoes. Explain the difference between these intervals and why this difference occurs.

6. Find the mean and standard deviation of the DRIVE variable by copying that variable into the Week 1 spreadsheet. Use the Week 4 spreadsheet to determine the percentage of data points from that data set that we would expect to be less than 25. To find the actual percentage in the dataset, sort the DRIVE variable and count how many of the data points are less than 25 out of the total 35 data points. That is the actual percentage. How does this compare with your prediction? Mean: ______________ Standard deviation: ____________________ Predicted percentage: Actual percentage: Comparison ___________________________________________________ ______________________________________________________________

7. What percentage of data would you predict would be between 25 and 50 and what percentage would you predict would be more than 50 miles? Use the Week 4 spreadsheet again to find the percentage of the data set we expect to have values between 25 and 50 as well as for more than 50. Now determine the percentage of data points in the dataset that fall within each of these ranges, using same strategy as above for counting data points in the data set. How do each of these compare with your prediction and why is there a difference? Predicted percentage between 25 and 50: ______________________________ Actual percentage: Predicted percentage more than 50 miles: Actual percentage: ___________________________________________ Comparison ____________________________________________________ _______________________________________________________________ Why? __________________________________________________________ ________________________________________________________________

Choose one of the following companies: Apple, Nike, Netflix, H&M, Ford, Pfizer

You have been asked to craft a marketing message for your company. Walk through the seven stages of the marketing communication process, highlighting the importance of each stage. Do a detailed analysis of at least three (3) of the stages and make at least two (2) recommendations on how to craft the marketing message. Discuss any opportunities for cause-related marketing.

The following table gives Foust Company's earnings per share for the last 10 years. The common stock, 8.6 million shares outstanding, is now (1/1/17) selling for $77 per share. The expected dividend at the end of the current year (12/31/17) is 50% of the 2016 EPS. Because investors expect past trends to continue, g may be based on the historical earnings growth rate. (Note that 9 years of growth are reflected in the 10 years of data.)

The current interest rate on new debt is 9%; Foust's marginal tax rate is 40%; and its target capital structure is 30% debt and 70% equity.

1. Calculate Foust's after-tax cost of debt. Round your answer to two decimal places.
   %
   
   Calculate Foust's cost of common equity. Calculate the cost of equity as r_s = D₁/P₀ + g. Round your answer to two decimal places. Do not round your intermediate calculations.
   %
2. 
   
3. Find Foust's WACC. Round your answer to two decimal places. Do not round your intermediate calculations.
   %