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Q1 The bad debt ratio for a financial institution is defined to be the dollar value of loans defaulted divided by the total dollar value of all loans made. Suppose a random sample of seven Ohio banks is selected and that the bad debt ratios (written as percentages) for these banks are 7 percent, 4 percent, 6 percent, 7 percent, 5 percent, 4 percent, and 9 percent. Assuming the bad debt ratios are approximately normally distributed, the MINITAB output of a 95 percent confidence interval for the mean bad debt ratio of all Ohio banks is as follows:
Variable N Mean StDev SE Mean 95.0% CI
d-ratio 7 6.000 1.826 0.690 ( 4.311, 7.689)
a Using the sample mean and standard deviation on the MINITAB output, verify the calculation of the 95 percent confidence interval.
b. Calculate a 99 percent confidence interval for the mean debt-to-equity ratio.
c Banking officials claim the mean bad debt ratio for all banks in the Midwest region is 3.5 percent and that the mean bad debt ratio for Ohio banks is higher. Using the 95 percent confidence interval, can we be 95 percent confident that this claim is true? Using the 99 percent confidence interval, can we be 99 percent confident that this claim is true? Explain.
Q2 A production supervisor at a major chemical company wishes to determine whether a new catalyst, catalyst XA-100, increases the mean hourly yield of a chemical process beyond the current mean hourly yield, which is known to be roughly equal to, but no more than, 750 pounds per hour. To test the new catalyst, five trial runs using catalyst XA-100 are made. The resulting yields for the trial runs (in pounds per hour) are 801, 814, 784, 836, and 820. Assuming that all factors affecting yields of the process have been held as constant as possible during the test runs, it is reasonable to
regard the five yields obtained using the new catalyst as a random sample from the population of all possible yields that would be obtained by using the new catalyst. Furthermore, we will assume that this population is approximately normally distributed.
a Using the Excel descriptive statistics output given below, find a 95 percent confidence interval for the mean of all possible yields obtained using catalyst XA-100.
b Based on the confidence interval, can we be 95 percent confident that the mean yield using catalyst XA-100 exceeds 750 pounds per hour? Explain.
Standard Error 8.786353
Standard Deviation 19.64688
Sample Variance 386
Confidence Level(95.0%) 24.39488
Part X: For each of the following situations, indicate whether an error has occurred and, if so, indicate what kind of error (Type I or Type II) has occurred.
a We do not reject H0 and H0 is true.
b We reject H0 and H0 is true.
c We do not reject H0 and H0 is false.
d We reject H0 and H0 is false.
Part Y: What is the level of significance alpha? Specifically, state what you understand by an alpha value of 0.05 and how it is related to Type 1 error?
Q4 Consolidated Power, a large electric power utility, has just built a modern nuclear power plant. This plant discharges waste water that is allowed to flow into the Atlantic Ocean. The Environmental Protection Agency (EPA) has ordered that the waste water may not be excessively warm so that thermal pollution of the marine environment near the plant can be avoided. Because of this order, the waste water is allowed to cool in specially constructed ponds and is then released into the ocean. This cooling system works properly if the mean temperature of waste water discharged is 60°F or cooler. Consolidated Power is required to monitor the temperature of the waste water. A sample of 100 temperature readings will be obtained each day, and if the sample results cast a substantial amount of doubt on the hypothesis that the cooling system is working properly (the mean temperature of waste water discharged is 60°F or cooler), then the plant must be shut down and appropriate actions must be taken to correct the problem.
a Consolidated Power wishes to set up a hypothesis test so that the power plant will be shut down when the null hypothesis is rejected. Set up the null and alternative hypotheses that should be used.
b In the context of this situation, interpret making a Type I error; interpret making a Type II error.
c Suppose Consolidated Power decides to use a level of significance alpha = 0.05, and suppose a random sample of 100 temperature readings is obtained. For each of the following sample results, determine whether the power plant should be shut down and the cooling system repaired:
1. Sample Mean = 60.482 and Sample Standard Deviation = 2
2. Sample Mean = 60.262 and Sample Standard Deviation = 2
3. Sample Mean = 60.618 and Sample Standard Deviation = 2
You should show the 5 step STOH for each sample result.
Q5. Advertising research indicates that when a television program is involving (such as the 2002 Super Bowl between the St. Louis Rams and New England Patriots, which was very exciting), individuals exposed to commercials tend to have difficulty recalling the names of the products advertised. Therefore, in order for companies to make the best use of their advertising dollars, it is important to show their most original and memorable commercials during involving programs. In an article in the Journal of Advertising Research, Soldow and Principe (1981) studied the effect of program content on the response to commercials. Program content, the factor studied, has three levels-more involving programs, less involving programs, and no program (that is, commercials only)-which are the treatments. To compare these treatments, Soldow and Principe employed a completely randomized experimental design. For each program content level, 29 subjects were randomly selected and exposed to commercials in that program content
level as follows:
(1) 29 randomly selected subjects were exposed to commercials shown in more involving programs,
(2) 29 randomly selected subjects were exposed to commercials shown in less involving programs, and,
(3) 29 randomly selected subjects watched commercials only (note: this is called the control group).
Then a brand recall score (measured on a continuous scale) was obtained for each subject. The 29 brand recall scores for each program content level are assumed to be a sample randomly selected from the population of all brand recall scores for that program content level. The mean brand recall scores for these three groups were as follows:
Furthermore, a one-way ANOVA of the data shows that SST = 21.40 and SSE = 85.56.
a. Identify the value of n, the total number of observations, and k, the number of treatments.
b. Calculate MST using MST = SST/(k-1)
c. Calculate MSE using MSE = SSE/(n-k+1)
d. Calculate F = MST/MSE.
e. Define the null and alternate hypotheses using the treatment means M1, M2, and M3 to represent each group. Then test for statistically significant differences between these treatment means. Set alpha =.05. Use the F-table to obtain the critical value of F. You should show the 5 steps in the STOH.
f. If you found a difference due to the treatments, between which groups do you think this treatment is most likely? Note you do have to perform tests to provide this answer.
Q6 An accountant wishes to predict direct labor cost (y) on the basis of the batch size (x) of a product produced in a job shop. Using labor cost and batch size data for 12 production runs, the following Excel Output of a Simple Linear Regression Analysis of the Direct Labor Cost Data was obtained. The scatter plot of this data is also shown.
Multiple R 0.99963578
R Square 0.999271693
Adjusted R Square 0.999198862
Standard Error 8.641541
df SS MS F Significance F
Regression 1 1024593f 1024593 13720.47k 5.04436E-17m
Residual 10 746.7624g 74.67624
Total 11 1025340h
Coefficients Standard Error t Stat P-value
Intercept 18 a 4.67658 3.953211c 0.00271e
BatchSize(X) 10b 0.08662 117.13d 5.04436E-17e
For your aid, the different values in the ANOVA table are explained below using the superscript notation:
a: b0, b: b1, c: t for testing H0: b0 = 0, d: t for testing H0: b1 = 0,
e: p-values for t statistics, f: Explained variation, g: SSE = Unexplained variation,
h: Total variation, k: F(model) statistic, m: p-value for F(model)
Answer the following questions based on the information provided above:
a. Write the regression equation for the LaborCost (y) and BatchSize (x). Note that your equation has to identify the point estimates for b0 and b1 in the equation:
y = b0 + b1x
b Identify the t statistic and the p-value for this t statistic for testing the significance of the slope of the regression line. Using this, determine whether the null hypothesis
H0: b1 = 0 can be rejected?
c What do you conclude about the relationship between LaborCost (y) and BatchSize (x)? Use the different test statistics provided in the data to support your case.
d. Interpret the meanings of b0 and b1. Does the interpretation of b0 make practical sense for this case? Think carefully about what the value of x will be when y = b0 .
e Estimate the value of LaborCost for a batch size of 10. Use your regression equation and show all your steps.
Q7 Use the following data for the given situation: International Machinery, Inc., produces a tractor and wishes to use quarterly tractor sales data observed in the last four years to predict quarterly tractor sales next year.
All the data for answering the problems (a) through (c) has been provided to you. You do not have to compute any data for parts (a) through (c).
a. What type of seasonal variation do you see in the sales data? Is there no seasonal variation, constant seasonal variation, increasing seasonal variation, or decreasing seasonal variation? State your reasons. Find and identify the four seasonal factors for quarters 1, 2, 3, and 4.
b. What type of trend is indicated by the plot of the deseasonalized data?
c. What is the equation of the estimated trend that has been calculated using the deseasonalized data?
d. Compute a point forecast of tractor sales (based on trend and seasonal factors) for each of the quarters next year. You should show all your steps for each quarter forecast. (Hint: Note that you will use the equation from ( c ). This will provide you with the deseasonalized data. You then have to adjust it for the seasonal factor applicable for the quarter.)
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