how long will it take

Sorry about that. How will I give you the handout with the tables????

1-The following data (refer to Table 1C on the Final Exam Handout) represents the annual worldwide sales (in millions of dollars) of appliances by manufacturer for a given year.

On the basis of the results above, which three manufacturers collectively sell the most appliances in terms of sales on an annual basis? What is their collective share of the market? Explain.

Whirlpool, General Electric, and Bosch-Siemens are the three manufacturers that collectively sell the most appliances in terms of sales on an annual basis. The collective share of the market is

2-The following raw data is the result of selecting a random sample of new Ford Escorts and testing these cars for fuel efficiency (results are in miles per gallon):

30.2 33.9 31.2 31.5 32.3 32.4
32.8 33.2 33.3 33.7 33.8 34.5
34.6 34.8 35.3 35.6 36.5 34.1

Here is a printout from EXCEL of the descriptive statistics for this problem. Refer to Table 2C on the Final Exam Handout.

Part A :
Discuss the property of variation for this data. Give specific ranges together with the percentage of data we d expect to fall within that range. Also, what are the quartiles and what do they tell us?

Part B :
Is the data skewed? If so, how?

3-The following table (Refer to Table 3A on the Final Exam Handout) gives the number of claims at a large insurance company by kind and geographical region.

Part A:

Referring to the above table, if a bill is chosen at random, what is the probability that it is either from the Midwest or from the South?

Part B :

Referring to the above table, given that the bill is from the East, what is the probability that it is for a Physicians Visit?

4-The probability of obtaining a home equity loan from United Bank is 0.40. A random sample of 10 loan applications is selected. Find the probability that at least 7 will get a loan.

The appropriate Binomial Probability Distribution is shown in Table 4A on the Final Exam Handout.

5-The length of time to do a complete full service oil change at Speedy-Lube, is normally distributed with a mean of 15.8 minutes and a standard deviation of 2.2 minutes. Refer to Table 5A on the Final Exam Handout for MegaStat output.

Part A:
Analyze the output above to determine what percentage of complete full service oil changes will fall between 13 and 20 minutes?

Part B:
What percentage of complete full service oil changes will take less than 10 minutes? If 1000 cars had a complete full service oil change, how many would you expect to be finished in less than 10 minutes?

6- The length of time to do a cable installation by Multi-Cable Inc. is normally distributed with a mean of 42.8 minutes and a standard deviation of 6.2 minutes. Refer to Table 6A on the Final Exam Handout for MegaStat output.

Multi-Cable wants to establish how long a standard cable install appointment should be to insure that all of the work will be completed during the appointment 90% of the time. How long would you recommend the standard cable installation appointment be?

7-You are in charge of selling advertising for radio station KGSM. The fee you can set for air time is directly related to the share of the listening market your station reaches. From time to time you conduct surveys to determine KGSM's share of the market. This month, when you contacted 200 randomly selected residential phone numbers, 12 respondents said they listen to KGSM.

Refer to Table 7A on the Final Exam Handout for MegaStat output.

Part A:
What is the 99% confidence interval for the percentage of the market that are listeners of KGSM. Interpret this confidence interval.

Part B :
A KGSM salesperson tells potential advertisers that KGSM has a 10% share. What would you tell this salesperson? (Is the salesperson correct? Why?)

8-GE fluorescent bulbs have a useful lifetime which is normally distributed. We wish to estimate mean lifetime. A random sample of 16 bulbs yields the following results: sample mean = 2,120 hours and a sample standard deviation of 320 hours.

Refer to Table 8A on the Final Exam Handout for MegaStat output.

Part A:

Analyze the output above to determine the 95% confidence interval for the mean lifetime of GE fluorescent bulbs. Interpret this confidence interval.

Part B :
Suppose a A GE executive says, Our fluorescent bulbs have an average expected life of 2200 hours. Could the executive be correct? Explain

9-The publisher of Golf Illustrated has found through past experience that 60% of its subscribers renew their subscriptions. The publisher selects a random sample of 200 subscribers and finds that 108 plan to renew their subscriptions.

Does the sample data provide sufficient evidence to conclude that less than 60% of subscribers to Golf Illustrated plan to renew their subscriptions? If so, how strong is the evidence? Use the hypothesis testing procedure. Be sure to clearly state the null and alternate hypothesis using proper notation.

Refer to Table 9A on the Final Exam Handout for MegaStat output.

10-CD Camera produces a new camera that it claims can take an average of at least 7 photographs per second. A test of 49 randomly selected cameras found a sample mean of 6.8 photos/sec. The population standard deivation is known to be 0.58 photos/sec.

Does the sample data present sufficient evidence to reject the manufacturer's claim? If so, how strong is the evidence? Use the hypothesis testing procedure. Be sure to clearly state the null and alternate hypothesis using proper notation.

Refer to Table 10A on the Final Exam Handout for MegaStat output.

Bonus question

11-An accountant wishes to predict direct labor cost (y) on the basis of batch size (x) of a product produced in a job shop. A sample of 14 production runs revealed the following:

Refer to Table 11A on the Final Exam Handout for MegaStat output.

Part A
Analyze the above output to determine the regression equation.

Part B
What conclusions are possible using the meaning of bo (intercept) and b1 (size) in this problem? (That is, explain the meaning of the coefficients.)

Part C
What conclusions are possible using the coefficient of determination (r-squared)?

Part D
Calculate the coefficient of correlation. Interpret this value.

Part E
Does this data provide significant evidence (α = 0.05) that the direct labor costs are associated with the size of a batch? Find the p-value and interpret.

Part F
Predict the average direct labor cost for a batch size of 100.

Part G
What is the 95% confidence interval for the direct labor cost for a batch size of 100. What conclusion is possible using this interval?

Bosch-Siemens $ 2,200

Electrolux $ 5,100

General Electric $ 4,350

Philips $ 2,000

Maytag $ 1,580

Whirlpool $ 3,950

Matsushita Electric $ 4,180

Bosch-Siemens $ 1,200

Electrolux $ 3,100

General Electric $ 4,350

Philips $ 2,000

Maytag $ 6,380

Whirlpool $ 3,950

Matsushita Electric $ 4,180

Bosch-Siemens $ 3,200

Electrolux $ 2,100

General Electric $ 6,350

Philips $ 1,800

Maytag $ 2,480

Whirlpool $ 8,950

Matsushita Electric $ 2,180

Descriptive statistics

Count 18

Mean 36.350

sample variance 3.037

sample standard

deviation 1.743

Minimum 33

Maximum 38.2

Range 5.2

Skewness -0.735

Kurtosis -0.739

coefficient of variation

(CV) 4.79%

1st quartile 35.225

Median 36.500

3rd quartile 37.800

interquartile range 2.575

Mode 37.800

Stem and Leaf plot for Miles per Gallon

stem unit = 1

leaf unit = 0.1

Frequency Stem Leaf

2 33 0 2

2 34 2 3

2 35 1 6

4 36 3 4 5 5

5 37 6 8 8 8 9

3 38 0 1 2

18

Descriptive statistics

Count 18

Mean 32.889

sample variance 8.035

sample standard

deviation 2.835

Minimum 29.1

maximum 39.2

range 10.1

skewness 0.789

kurtosis -0.095

coefficient of variation

(CV) 8.62%

1st quartile 30.600

median 32.300

3rd quartile 34.500

interquartile range 3.900

Mode 30.200

Stem and Leaf plot for Miles per Gallon

stem unit = 1

leaf unit = 0.1

Frequency Stem Leaf

2 29 1 8

3 30 2 2 4

3 31 2 5 8

3 32 3 3 5

1 33 2

2 34 5 5

1 35 3

1 36 5

1 37 5

0 38

1 39 2

18

Descriptive statistics

Count 18

Mean 33.539

sample variance 2.625

sample standard

deviation 1.620

Minimum 30.2

Maximum 36.5

range 6.3

skewness -0.277

kurtosis -0.143

coefficient of variation

(CV) 4.83%

1st quartile 32.500

median 33.750

3rd quartile 34.575

interquartile range 2.075

mode #N/A

Stem and Leaf plot for Miles per Gallon

stem unit = 1

leaf unit = 0.1

Frequency Stem Leaf

1 30 2

2 31 2 5

3 32 3 4 8

5 33 2 3 7 8 9

4 34 1 5 6 8

2 35 3 6

1 36 5

18

Hospitalization 75 128 29 52 284

Physician's Visit 233 514 XXX XXX XXXX

Outpatient Treatment 100 326 65 99 590

Hospitalization 55 328 29 52 464

Physician's Visit 233 514 XXX XXX XXXX

Outpatient Treatment 100 526 65 102 793

Hospitalization 102 98 39 62 301

Physician's Visit 263 514 XXX XXX XXXX

Outpatient Treatment 100 226 65 99 490

Binomial Distribution

10 n

0.4 p

0 0.00605 0.00605

1 0.04031 0.04636

2 0.12093 0.16729

3 0.21499 0.38228

4 0.25082 0.63310

5 0.20066 0.83376

6 0.11148 0.94524

7 0.04247 0.98771

8 0.01062 0.99832

9 0.00157 0.99990

10 0.00010 1.00000

1.00000

Binomial distribution

10 n

0.5 p

0 0.00098 0.00098

1 0.00977 0.01074

2 0.04395 0.05469

3 0.11719 0.17188

4 0.20508 0.37695

5 0.24609 0.62305

6 0.20508 0.82813

7 0.11719 0.94531

8 0.04395 0.98926

9 0.00977 0.99902

10 0.00098 1.00000

1.00000

Binomial distribution

10 n

0.7 p

0 0.00001 0.00001

1 0.00014 0.00014

2 0.00145 0.00159

3 0.00900 0.01059

4 0.03676 0.04735

5 0.10292 0.15027

6 0.20012 0.35039

7 0.26683 0.61722

8 0.23347 0.85069

9 0.12106 0.97175

10 0.02825 1.00000

1.00000

normal distribution

p(lower) p(upper) z x mean std.dev

.0042 .9958 -2.64 10 15.8 2.2

.9719 .0281 1.91 20 15.8 2.2

.1016 .8984 -1.27 13 15.8 2.2

.8413 .1587 1.00 18 15.8 2.2

normal distribution

p(lower) p(upper) z x mean std.dev

.0042 .9958 -2.64 10 15.8 2.2

.9719 .0281 1.91 20 15.8 2.2

.1016 .8984 -1.27 13 15.8 2.2

.8413 .1587 1.00 18 15.8 2.2

normal distribution

p(lower) p(upper) z x mean std.dev

.0042 .9958 -2.64 10 15.8 2.2

.9719 .0281 1.91 20 15.8 2.2

.1016 .8984 -1.27 13 15.8 2.2

.8413 .1587 1.00 18 15.8 2.2

normal distribution

p(lower) p(upper) z x mean std.dev

.9000 .1000 1.28 50.75 42.8 6.2

.7500 .2500 0.67 46.98 42.8 6.2

.8000 .2000 0.84 48.02 42.8 6.2

.1000 .9000 -1.28 34.85 42.8 6.2

.2500 .7500 -0.67 38.62 42.8 6.2

.2000 .8000 -0.84 37.58 42.8 6.2

normal distribution

p(lower) p(upper) z x mean std.dev

.9000 .1000 1.28 50.75 42.8 6.2

.7500 .2500 0.67 46.98 42.8 6.2

.8000 .2000 0.84 48.02 42.8 6.2

.1000 .9000 -1.28 34.85 42.8 6.2

.2500 .7500 -0.67 38.62 42.8 6.2

.2000 .8000 -0.84 37.58 42.8 6.2

normal distribution

p(lower) p(upper) z x mean std.dev

.9000 .1000 1.28 50.75 42.8 6.2

.7500 .2500 0.67 46.98 42.8 6.2

.8000 .2000 0.84 48.02 42.8 6.2

.1000 .9000 -1.28 34.85 42.8 6.2

.2500 .7500 -0.67 38.62 42.8 6.2

.2000 .8000 -0.84 37.58 42.8 6.2

Confidence interval - proportion

99% confidence level

0.06 proportion

200 n

2.576 z

0.043 half-width

0.103 upper confidence limit

0.017 lower confidence limit

Confidence interval - proportion

99% confidence level

0.08 proportion

200 N

2.576 Z

0.048 half-width

0.123 upper confidence limit

0.027 lower confidence limit

Confidence interval - proportion

99% confidence level

0.11 proportion

200 N

2.576 Z

0.057 half-width

0.167 upper confidence limit

0.053 lower confidence limit

Confidence interval - mean

95% confidence level

2120 mean

320 std. dev.

16 N

2.131 T (df = 15)

170.516 half-width

2290.516 upper confidence limit

1949.484 lower confidence limit

Confidence interval - mean

95% confidence level

2360 mean

365 std. dev.

20 N

2.093 t (df = 19)

170.825 half-width

2530.825 upper confidence limit

2189.175 lower confidence limit

Confidence interval - mean

95% confidence level

1750 mean

265 std. dev.

18 n

2.110 t (df = 17)

131.781 half-width

1881.781 upper confidence limit

1618.219 lower confidence limit

Hypothesis test for proportion vs hypothesized value

0.54 0.6 p (as decimal)

108/200 120/200 p (as fraction)

108. 120. X

200 200 n

0.0346 std. error

-1.73 z

.0416 p-value (one-tailed, lower)

Hypothesis test for proportion vs hypothesized value

0.42 0.5 p (as decimal)

84/200 100/200 p (as fraction)

84. 100. X

200 200 N

0.0354 std. error

-2.26 Z

.0118 p-value (one-tailed, lower)

Hypothesis test for proportion vs hypothesized value

0.695 0.75 p (as decimal)

139/200 150/200 p (as fraction)

139. 150. X

200 200 n

0.0306 std. error

-1.80 z

.0362 p-value (one-tailed, lower)

Hypothesis Test:

Mean vs. Hypothesized Value

7.000 hypothesized value

6.800 mean Photos

0.580 std. dev.

0.083 std. error

49 n

-2.41 z

.0079

p-value (onetailed,

lower)

Hypothesis Test:

Mean vs. Hypothesized Value

6.000 hypothesized value

5.500 mean Photos

0.410 std. dev.

0.069 std. error

35 n

-7.21 z

2.70E-13

p-value (onetailed,

lower)

Hypothesis Test:

Mean vs. Hypothesized Value

8.000 hypothesized value

7.700 mean Photos

0.650 std. dev.

0.107 std. error

37 n

-2.81 z

.0025

p-value (onetailed,

lower)

Regression Analysis

r² 0.996 n 14

r 0.998 k 1

Std. Error 21.097 Dep. Var. Labor Cost

ANOVA table

Regression 1,349,259.6337 1 1,349,259.6337 3031.35 8.49E-16

Residual 5,341.2234 12 XXX.XXXX

Total 1,354,600.8571 13

Intercept 4.2768 11.0779 0.386 .7062 -19.8600 28.4135

Batch Size 10.4257 0.1894 55.058 8.49E-16 10.0131 10.8383

Predicted values for: Labor Cost

100 1,046.848 1,022.964 1,070.731 995.046 1,098.650 0.270

Regression Analysis

r² 0.994 n 14

r 0.997 k 1

Std. Error 20.490 Dep. Var. Labor Cost

ANOVA

table

Regression 841,283.2839 1 841,283.2839 2003.79 1.01E-14

Residual 5,038.1446 12 XXX.XXXX

Total 846,321.4286 13

Intercept 16.0089 10.7591 1.488 .1626 -7.4331 39.4508

Batch Size 8.2324 0.1839 44.764 1.01E-14 7.8317 8.6332

Predicted values for: Labor Cost

100 839.254 816.058 862.450 788.943 889.564 0.270

Regression Analysis

r² 0.967 n 14

r 0.983 k 1

Std. Error 71.234 Dep. Var. Labor Cost

ANOVA

table

Regression 1,781,254.6126 1 1,781,254.6126 351.04 2.99E-10

Residual 60,891.3874 12 5,074.2823

Total 1,842,146.0000 13

Intercept 49.7714 37.4039 1.331 .2080 -31.7247 XXX.XXXX

Batch Size 11.9790 0.6394 18.736 2.99E-10 10.5860 13.3721

Predicted values for: Labor Cost

100 1,247.672 1,167.031 1,328.313 1,072.767 1,422.577 0.270

Regression Analysis

R² 0.908

Adjusted

R² 0.885 n 16

R 0.953 k 3

Std. Error 0.305 Dep. Var. y

ANOVA

table

Regression 10.9730 3 3.6577 39.33 1.74E-06

Residual 1.1159 12 0.0930

Total 12.0889 15

Intercept 8.1495 0.8180 9.963 3.73E-07 6.3672 9.9318

x1 -2.1505 0.2493 -8.627 1.72E-06 -2.6936 -1.6074

x2 1.4409 0.1872 7.696 5.58E-06 1.0330 1.8488

x3 0.4008 0.0903 4.439 .0008 0.2041 0.5976

Predicted values for: y

3.7 3.9 6.5 8.41737 8.22645 8.60829 7.72606 9.10869 0.083

Regression Analysis

R² 0.937

Adjusted

R² 0.921 n 16

R 0.968 k 3

Std. Error 0.412 Dep. Var. y

ANOVA

table

Regression 30.1593 3 10.0531 59.22 1.83E-07

Residual 2.0373 12 0.1698

Total 32.1966 15

Intercept 4.8483 1.1053 4.387 .0009 2.4401 7.2564

x1 -3.6440 0.3368 -10.819 1.52E-07 -4.3779 -2.9102

x2 3.1699 0.2530 12.531 2.98E-08 2.6187 3.7210

x3 0.3589 0.1220 2.941 .0123 0.0930 0.6247

Predicted values for: y

3.7 3.9 6.5 6.06048 5.80251 6.31844 5.12640 6.99455 0.083

Regression Analysis

R² 0.961

Adjusted

R² 0.951 n 16

R 0.980 k 3

Std. Error 0.313 Dep. Var. y

ANOVA

table

Regression 28.6519 3 9.5506 97.27 1.09E-08

Residual 1.1783 12 0.0982

Total 29.8302 15

Intercept 2.3146 0.8405 2.754 .0175 0.4833 4.1460

x1 -1.8520 0.2561 -7.231 1.04E-05 -2.4101 -1.2940

x2 1.1198 0.1924 5.821 .0001 0.7007 1.5390

x3 1.1981 0.0928 12.913 2.13E-08 0.9960 1.4003

Predicted values for: y

3.7 3.9 6.5 7.617234 7.421054 7.813414 6.906875 8.327594 0.083