Estimate: GPA = β₀ + β₁GRE + ε, where GRE is a student’s score on the math portion of the Graduate Record Examination (GRE) score and GPA is the student’s grade point average in graduate school. [You may find it useful to reference the t table.]

a. Construct the 90% confidence interval for the expected GPA for an individual who scored 730 on the math portion of the GRE. (Round regression estimates to at least 4 decimal places, "t_α/2,df" value to 3 decimal places, and final answers to 2 decimal places.)

b. Construct the 90% prediction interval for GPA for an individual who scored 730 on the math portion of the GRE. (Round regression estimates to at least 4 decimal places, "t_α/2,df" value to 3 decimal places, and final answers to 2 decimal places.)

The home run percentage is the number of home runs per 100 times at bat. A random sample of 43 professional baseball players gave the following data for home run percentages.

1.6, 2.4, 1.2, 6.6, 2.3, 0.0, 1.8, 2.5, 6.5, 1.8, 2.7, 2.0, 1.9, 1.3, 2.7, 1.7, 1.3, 2.1, 2.8, 1.4, 3.8, 2.1, 3.4, 1.3, 1.5, 2.9, 2.6, 0.0, 4.1, 2.9, 1.9, 2.4, 0.0, 1.8, 3.1, 3.8, 3.2, 1.6, 4.2, 0.0, 1.2, 1.8, 2.4

(a) Use a calculator with mean and standard deviation keys to find x and s. (Round your answers to two decimal places.)

x = %

s = %

(b) Compute a 90% confidence interval for the population mean μ of home run percentages for all professional baseball players. Hint: If you use the Student's t distribution table, be sure to use the closest d.f. that is smaller. (Round your answers to two decimal places.)

lower limit %

upper limit %

(c) Compute a 99% confidence interval for the population mean μ of home run percentages for all professional baseball players. (Round your answers to two decimal places.)

lower limit %

upper limit %

Q1

A. The following data refers to yield of tomatoes (kg/plot) for four different levels of salinity. Salinity level here refers to electrical conductivity (EC), where the chosen levels were EC = 1.6, 3.8, 6.0 and 10.2 nmhos/cm

I- Use the F test at level α = 0.05 to test for any differences in true average yield due to the different salinity levels. (use the method given in the lecture notes only)

II- What happens when Tukey’s procedure is applied?

B- For the following pairs of assertions, indicate which do not comply with our rules for setting up hypotheses and why (the subscripts 1 and 2 differentiate between quantities for two different populations or samples).

I. Ho: µ = 100, Ha: µ >100

II. Ho: p ≠ 0.25, Ha: p = 0.25

III. Ho: S²1 = S²2, Ha: S²1 ≠ S²2

IV. Ho: s1/s2 = 1, Ha: s1/s2 ≠ 1

Situation:

Fidelity Investment has collected data polling online investors on their experiences. As a part of the survey, the investors were asked to rate the quality of the speed of execution with their brokers and an overall satisfaction rating for online trades. Possible responses were Unsatisfied (score = 1), Somewhat satisfied (score = 2), Satisfied (score = 3), and Very satisfied (score = 4). An average score for each broker was computed and average execution speed in seconds for each investor was recorded as shown in the following table.

Action

Perform a correlation and regression analysis to predict satisfaction score using execution speed. Discuss the following:

1. Scatter Diagram
2. R, R2 and 1-R2
3. Show the regression equation. Comment on the interpretation of the slope of regression equation.
4. What is the standard error of estimate value? What is the interpretation of this value?
5. Conduct the appropriate test of hypothesis for the regression model. Use a .05 level of significance. Does trade execution speed appear to be good predictor of the satisfaction rating? Why or why not?

Brokerage Satisfaction with Trade Price Satisfaction with Speed of Execution Overall Satisfaction with Electronic Trades

a. Develop an estimated regression equation using trade price and speed of execution to predict overall satisfaction with the broker. What is the coefficient of determination?

b. Develop an estimated regression equation using trade price and speed of execution to predict overall satisfaction with the broker. What is the SSR?

c. Develop an estimated regression equation using trade price and speed of execution to predict overall satisfaction with the broker. Can you conclude that there is a relationship between satisfaction with speed of execution and overall satisfaction with the electronic trade (can you reject the hypothesis that the parameter is = 0)? Group of answer choices

The home run percentage is the number of home runs per 100 times at bat. A random sample of 43 professional baseball players gave the following data for home run percentages.

(a) Use a calculator with mean and standard deviation keys to find x and s. (Round your answers to two decimal places.)

(b) Compute a 90% confidence interval for the population mean μ of home run percentages for all professional baseball players. Hint: If you use the Student's t distribution table, be sure to use the closest d.f. that is smaller. (Round your answers to two decimal places.)

(c) Compute a 99% confidence interval for the population mean μ of home run percentages for all professional baseball players. (Round your answers to two decimal places.)

Upon graduation from NAU with your business degree, you took a job as a Management Consultant. Fogler River Supply, Inc. (FRS) has hired your consulting firm to analyze their credit management and evaluate their credit policy. Jill Fogler, a finance graduate, owns a river rafting supply business with her brother, Joe, who majored in Recreation Services Management. The firm sells primarily to rafting tour companies. Sales are slow during the cold winter months, rise during the spring and summer, and then fall off again in the fall when river flows decline and the weather turns cold. The Foglers are concerned about the firm’s current credit policy. The terms of sale are net 30, but they expect only 55% of the customers (by dollar value) to pay the full amount on day 30, while the other 45% pay, on average, on Day 50. Gross sales are currently $350,000 per year. Of the gross sales, 2% end up as bad debt losses. Monthly sales for the first six months of 2017 are provided in the table below.

Table 1 – 2017 Monthly Sales (first six months) January February March April May June $20,000 $25,000 $35,000 $40,000 $40,000 $35,000

FRS is considering a change in credit policy. The change would entail 1) changing the credit terms to 2/10, net 30, 2) employing stricter credit standards before granting credit, and 3) enforcing collections with greater vigor than in the past. Thus, cash customers and those paying within 10 days would receive a 2% discount, but all others would have to the pay the full amount within 30 days. The owners believe the discount would both attract additional customers and encourage some existing customers to purchase more from the firm – after all, the discount amounts to a price reduction. The net expected result is for sales to increase to $375,000, for 35% of the paying customers to take the discount and pay on the 10 th day, for 45% to pay the full amount on day 30, for 15% to pay late on day 35, and for bad debt losses to fall from 2% to 1.5% of gross sales. The firm’s operating (variable) cost ratio will remain unchanged at 65%, and its cost for financing (notes payable or required return on investments) will remain unchanged at 5%. The company would have to purchase some new inventory to cover the additional sales. Inventory turnover averages 4 times per year, and CGS is 55% of sales. The most recent income statement with relevant information is given below.

Table 2 - 2017 Income Statement Gross Sales $350,000 Less: discounts 0 Net Sales $350,000 Variable Costs (65%) 227,500 Profit before credit costs and taxes (CM) $122,500 Credit related costs: A/R Carrying costs Inventory Investment cost Bad Debt Losses Profit before taxes Taxes (26%) Net Income

Case requirements: 1. To provide some insight for Jill and her brother describe the four variables that make up a firm’s credit policy, and explain how each of them affects sales and DSO considering a stricter (tighter) credit policy. 2. FRS would like you to determine and explain the primary factors that influence the level of receivables outstanding. Additionally, they would like to know what factors influence the dollar cost of carrying the receivables. 3. Refer to the monthly sales in Table 1 on the previous page and the current customer payment patterns to answer the following questions. a. What were FRS’s receivables balances at the end of Quarters 1 and 2 for 2017? b. Assume 90 days per calendar quarter. What were the average daily sales (ADS) and days sales outstanding (DSO) for the each of the first two quarters of 2017? What were the ADS and DSO for the first six months of 2017? c. Does the DSO indicate that the firm’s customers have changed their payment behavior from the first quarter to the second quarter of 2017? Is DSO a good management tool in this situation? Why or why not? d. Would the aging schedule or uncollected balances schedule properly measure customer payment patterns? Based on your answer, construct the schedule that measures payment patterns and explain to the Foglers what the schedule indicates about their customers’ payment patterns from one quarter to the next. 4. Determine the incremental after tax profit associated with the change in credit terms being considered. In other words, what is the difference in profit from old to new credit terms? Use a 26% tax rate. (Hint: Construct income statements under each policy, consider the four variables that affect profitability with a credit policy change, and focus on the expected change.) Based on the findings, should the company make the change? 5. Suppose the firm makes the change, but its competitors react by making similar changes to their own credit terms, with the net result being that gross sales remain at the current $350,000 level. If this were to happen, no additional inventory purchases would be necessary. What would be the impact on the firm’s after tax profitability? Based on the findings, should the company make the change? 6. Given your sensitivity analysis of FRS’s credit policy change, will you recommend that the company make the change to the new credit policy or continue using their existing credit policy? Justify your answer

How does the multiplier process work when there is an initial decrease in autonomous spending?