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Directions:Be sure to make an electronic copy of your answer before submitting it to Ashworth College for grading. Unless otherwise stated, answer in complete sentences, and be sure to use correct English spelling and grammar. Sources must be cited in APA format. Your response should be four (4) double‐spaced pages; refer to the "Assignment Format" page for specific format requirements.
Please provide the following information to check your understanding of the course material for this course.
Part AExplain the applicability and functionality of chi-square testing. Provide an original example of a data chart that illustrates this concept with appropriate explanation including hypotheses.
Part BDiscuss the concept of data mining and two (2) examples of current companies involved in this type of business discussing their operations. Furthermore explain the integral role marketing plays in this venue of data mining.
Directions:Be sure to make an electronic copy of your answer before submitting it to Ashworth College for grading. Unless otherwise stated, answer in complete sentences, and be sure to use correct English spelling and grammar. Sources must be cited in APA format. Your response should be four (4) double‐spaced pages; refer to the "Assignment Format" page for specific format requirements.
Please provide the following information to check your understanding of the course material for this course:
Part AExplain the relevance and significance of samples, sample sizes, and populations as they impact studies and research. Include a discussion about the various types of sampling used and examine common necessary elements of survey validity. Include arguments for and against various scenarios as they relate to criteria choice in research design.
Part BDiscuss the shapes and significance of plotting quantitative data. Include two original examples using pictures as a means of explanation. Express the strengths and weaknesses of visual depictions of quantitative data.
Part CDiscuss correlation, necessary conditions, and properties, best-fit lines, and explain how to ensure proper model usage.
Note: It is recommended that you save your response as you complete each question.
Question 1 (2.5 points)
Insurance companies track life expectancy information to assist in determining the cost of life insurance policies. Last year the average life expectancy of all policyholders was 77 years. ABI Insurance wants to determine if their clients now have a longer life expectancy, on average, so they randomly sample some of their recently paid policies. The ages of the clients in the sample are shown below.
86
75
83
84
81
77
78
79
79
81
76
85
70
76
79
81
73
74
72
83
What is the sample mean and standard deviation?
Question 1 options:
mean 77.8 years; SD 4.48 years
mean 78.6 years; SD 4.46 years
mean 78.6 years; SD 4.44 years
mean 78.6 years; SD 4.48 years
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Question 2 (2.5 points)
In a metal fabrication process, metal rods are produced to a specified target length of 15 feet. Suppose that the lengths are normally distributed. A quality control specialist collects a random sample of 16 rods and finds the sample mean length to be 14.8 feet and a standard deviation of 0.65 feet. What is the standard error?
Question 2 options:
0.1275 ft.
0.1425 ft.
0.1625 ft.
0.3250 ft.
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Question 3 (2.5 points)
Insurance companies track life expectancy information to assist in determining the cost of life insurance policies. Last year the average life expectancy of all policyholders was 77 years. ABI Insurance wants to determine if their clients now have a longer life expectancy, on average, so they randomly sample some of their recently paid policies. The ages of the clients in the sample are shown below.
86
75
83
84
81
77
78
79
79
81
76
85
70
76
79
81
73
74
72
83
Suppose ABI samples 100 recently paid policies. This sample yields a mean of 77.7 years and a standard deviation of 3.6 years. The 90% confidence interval for true mean age is:
Question 3 options:
76.1 to 77.3 years. We are 90% confident that the average age of clients with recently paid policies is between 77.1 years to 78.3 years.
77.1 to 78.3 years. We are 90% confident that the average age of clients with recently paid policies is between 77.1 years to 78.3 years.
77.1 to 78.5 years. We are 90% confident that the average age of clients with recently paid policies is between 77.1 years to 78.3 years.
77.8 to 80.0 years. We are 90% confident that the average age of clients with recently paid policies is between 77.1 years to 78.3 years.
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Question 4 (2.5 points)
In a metal fabrication process, metal rods are produced to a specified target length of 15 feet. Suppose that the lengths are normally distributed. A quality control specialist collects a random sample of 16 rods and finds the sample mean length to be 14.8 feet and a standard deviation of 0.65 feet. For 95% confidence, what is the margin of error?
Question 4 options:
0.334 ft.
0.346 ft.
0.382 ft.
0.402 ft.
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Question 5 (2.5 points)
A mid-sized company has decided to implement an enterprise resource planning (ERP) system and management suspects that many of its employees are concerned about the planned implementation. Managers are considering holding informational workshops to help decrease anxiety levels among employees. To determine whether such an approach would be effective, they randomly select 16 employees to participate in a pilot workshop. These employees were given a questionnaire to measure anxiety levels about ERP before and after participating in the workshop. Does the data indicate that anxiety levels about ERP decreases as a result of the workshop?
Pre-workshop anxiety level
7
6
9
5
6
7
5
7
6
4
3
2
1
3
4
2
Post-workshop anxiety level
4
3
7
3
4
5
4
6
5
3
2
2
1
3
4
3
Difference (Post-Pre)
-3
-3
-2
-2
-2
-2
-1
-1
-1
-1
-1
0
0
0
0
1
Create and interpret a 90% confidence interval.
Question 5 options:
With 13 degrees of freedom, the critical t-value is 1.753. The 90% confidence interval is (-1.52, -0.51). We are 90% confident that, on average, participating in this workshop will decrease employee anxiety between .51 and 1.52 points (on the scale).
With 13 degrees of freedom, the critical t-value is 1.753. The 90% confidence interval is (-1.63, -0.62). We are 90% confident that, on average, participating in this workshop will decrease employee anxiety between .62 and 1.63 points (on the scale).
With 15 degrees of freedom, the critical t-value is 1.753. The 90% confidence interval is (-1.63, -0.62). We are 90% confident that, on average, participating in this workshop will decrease employee anxiety between .62 and 1.63 points (on the scale).
With 15 degrees of freedom, the critical t-value is 1.852. The 90% confidence interval is (-1.63, -0.62). We are 90% confident that, on average, participating in this workshop will decrease employee anxiety between .62 and 1.63 points (on the scale).
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Question 6 (2.5 points)
Data was collected on annual personal time (in hours) taken by a random sample of 16 women and 7 men employed by a medium-sized company. The women took an average of 24.75 hours of personal time per year with a standard deviation of 2.84 hours. The men took an average of 21.89 hours of personal time per year with a standard deviation of 3.29 hours. The Human Resources Department believes that women tend to take more personal time than men because they tend to be the primary childcare givers in the family.
Personal Time (hours)
Women
Men
25 21
22
22 27
21
19 29
24
25 26
27
24 30
19
25 27
23
24 26
17
23 23
Histogram Information: For Women: At 20, frequency is 1; At 22, frequency is 2; At 24, frequency is 4; At 26, frequency is 5; At 28, frequency is 2; At 30, frequency is 2;
For Men: At 18, frequency is 1; At 20, frequency is 1; At 22, frequency is 2; At 24, frequency is 2; At 26, frequency is 1;
Write the null and alternative hypotheses.
Question 6 options:
H0: μ1 - μ2 = 0 and HA: μ1 - μ2 < 0
H0: μ1 - μ2 < 0 and HA: μ1 - μ2 > 0
H0: μ1 - μ2 = 0 and HA: μ1 - μ2 > 0
H0: μ1 - μ2 > 0 and HA: μ1 - μ2 < 0
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Question 7 (2.5 points)
Data was collected on annual personal time (in hours) taken by a random sample of 16 women and 7 men employed by a medium-sized company. The women took an average of 24.75 hours of personal time per year with a standard deviation of 2.84 hours. The men took an average of 21.89 hours of personal time per year with a standard deviation of 3.29 hours. The Human Resources Department believes that women tend to take more personal time than men because they tend to be the primary childcare givers in the family.
Personal Time (hours)
Women
Men
25 21
22
22 27
21
19 29
24
25 26
27
24 30
19
25 27
23
24 26
17
23 23
Histogram Information: For Women: At 20, frequency is 1; At 22, frequency is 2; At 24, frequency is 4; At 26, frequency is 5; At 28, frequency is 2; At 30, frequency is 2;
For Men: At 18, frequency is 1; At 20, frequency is 1; At 22, frequency is 2; At 24, frequency is 2; At 26, frequency is 1;
Are the assumptions and conditions for carrying out the t-test for two means satisfied?
Question 7 options:
Independent group assumption: Women and men are dependent groups.<br /> Randomization condition: These are random samples.<br /> Nearly Normal condition: The histogram information shows that both sets are unimodal and roughly symmetric.
Independent group assumption: Women and men are definitely independent groups.<br /> Randomization condition: These are convenience samples.<br /> Nearly Normal condition: The histogram information shows that both sets are unimodal and roughly symmetric.
Independent group assumption: Women and men are definitely independent groups.<br /> Randomization condition: These are random samples.<br /> Nearly Normal condition: The histogram information shows that both sets are multimodal and roughly symmetric.
Independent group assumption: Women and men are definitely independent groups.<br /> Randomization condition: These are random samples.<br /> Nearly Normal condition: The histogram information shows that both sets are unimodal and roughly symmetric.
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Question 8 (2.5 points)
A small business ships specialty homemade candies to anywhere in the world. Past records indicate that the weight of orders is normally distributed. Suppose a random sample of 16 orders is selected and each is weighed. The sample mean was found to be 110 grams with a standard deviation of 14 grams. For 90% confidence, what is the margin of error?
Question 8 options:
7.235 grams
6.238 grams
6.135 grams
6.055 grams
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Question 9 (2.5 points)
A mid-sized company has decided to implement an enterprise resource planning (ERP) system and management suspects that many of its employees are concerned about the planned implementation. Managers are considering holding informational workshops to help decrease anxiety levels among employees. To determine whether such an approach would be effective, they randomly select 16 employees to participate in a pilot workshop. These employees were given a questionnaire to measure anxiety levels about ERP before and after participating in the workshop. Does the data indicate that anxiety levels about ERP decreases as a result of the workshop?
Pre-workshop anxiety level
7
6
9
5
6
7
5
7
6
4
3
2
1
3
4
2
Post-workshop anxiety level
4
3
7
3
4
5
4
6
5
3
2
2
1
3
4
3
Difference (Post-Pre)
-3
-3
-2
-2
-2
-2
-1
-1
-1
-1
-1
0
0
0
0
1
Explain if the assumptions and conditions for carrying out the paired t-test satisfied paired data.
Question 9 options:
Paired data: The data are paired because they are measurements on different individuals both before and after the workshop.<br /> Independence: The anxiety level of any employee is independent of the anxiety level of any other employee, so the differences are independent.
Paired data: The data are paired because they are measurements on different individuals both before and after the workshop.<br /> Independence: The anxiety level of any employee is dependent of the anxiety level of any other employee, so the differences are dependent.
Paired data: The data are paired because they are measurements on the same individuals both before and after the workshop.<br /> Independence: The anxiety level of any employee is independent of the anxiety level of any other employee, so the differences are independent.
Paired data: The data are paired because they are measurements on the same individuals both before and after the workshop.<br /> Independence: The anxiety level of any employee is dependent of the anxiety level of any other employee, so the differences are dependent.
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Question 10 (2.5 points)
In a metal fabrication process, metal rods are produced to a specified target length of 15 feet. Suppose that the lengths are normally distributed. A quality control specialist collects a random sample of 16 rods and finds the sample mean length to be 14.8 feet and a standard deviation of 0.65 feet. Based on the sample results, the 95% confidence interval for true mean length is from:
Question 10 options:
14.454 to 15.146 ft. We are 95% confident that the average length of metal rods from this process is between 14.454 and 15.146 ft.
14.324 to 15.246 ft. We are 95% confident that the average length of metal rods from this process is between 14.324 and 15.246 ft.
14.454 to 15.246 ft. We are 95% confident that the average length of metal rods from this process is between 14.454 and 15.246 ft.
14.324 to 15.146 ft. We are 95% confident that the average length of metal rods from this process is between 14.324 and 15.146 ft.
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Question 11 (2.5 points)
Data was collected on annual personal time (in hours) taken by a random sample of 16 women and 7 men employed by a medium-sized company. The women took an average of 24.75 hours of personal time per year with a standard deviation of 2.84 hours. The men took an average of 21.89 hours of personal time per year with a standard deviation of 3.29 hours. The Human Resources Department believes that women tend to take more personal time than men because they tend to be the primary childcare givers in the family.
Personal Time (hours)
Women
Men
25 21
22
22 27
21
19 29
24
25 26
27
24 30
19
25 27
23
24 26
17
23 23
Histogram Information: For Women: At 20, frequency is 1; At 22, frequency is 2; At 24, frequency is 4; At 26, frequency is 5; At 28, frequency is 2; At 30, frequency is 2;
For Men: At 18, frequency is 1; At 20, frequency is 1; At 22, frequency is 2; At 24, frequency is 2; At 26, frequency is 1;
Create and interpret a 90% confidence interval.
Question 11 options:
Using 10 degrees of freedom, the critical t-value is 1.824. The 90% confidence interval is (0.40, 5.58). We are 90% confident that, on average, women use between .40 and 5.58 more hours of personal time per year than men.
Using 10 degrees of freedom, the critical t-value is 1.812. The 90% confidence interval is (0.30, 5.48). We are 90% confident that, on average, women use between .30 and 5.48 more hours of personal time per year than men.
Using 8 degrees of freedom, the critical t-value is 1.812. The 90% confidence interval is (0.30, 5.48). We are 90% confident that, on average, women use between .30 and 5.48 more hours of personal time per year than men.
Using 8 degrees of freedom, the critical t-value is 1.812. The 90% confidence interval is (0.40, 5.58). We are 90% confident that, on average, women use between .40 and 5.58 more hours of personal time per year than men.
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Question 12 (2.5 points)
A consumer group was interested in comparing the operating time of cordless toothbrushes manufactured by two different companies. Group members took a random sample of 18 toothbrushes from Company A and 15 from Company B. Each was charged overnight and the number of hours of use before needing to be recharged was recorded. Company A toothbrushes operated for an average of 119.7 hours with a standard deviation of 1.74 hours; Company B toothbrushes operated for an average of 120.6 hours with a standard deviation of 1.72 hours. Do these samples indicate that Company B toothbrushes operate more hours on average than Company A toothbrushes?
Hours of Operation
Company A
Company B
121 199
122 120
122 121
121 118
117 122
121 121
120 119
122 123
120 119
121 118
121 118
119 120
118 120
120 124
120 123
119
117 118
Histogram information: Company A At 117, frequency is 2; At 118, frequency is 3; At 119, frequency is 3; At 120, frequency is 4; At 121, frequency is 3; At 122, frequency is 2; At 123, frequency is 1;
Company B At 118, frequency is 2; At 119, frequency is 2; At 120, frequency is 3; At 121, frequency is 4; At 122, frequency is 2; At 123, frequency is 1; At 124, frequency is 1;
Write the null and alternative hypotheses.
Question 12 options:
H0: μ1 - μ2 = 0 and HA: μ1 - μ2 > 0
H0: μ1 - μ2 > 0 and HA: μ1 - μ2 < 0
H0: μ1 - μ2 = 0 and HA: μ1 - μ2 < 0
H0: μ1 - μ2 < 0 and HA: μ1 - μ2 =0
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Question 13 (2.5 points)
Insurance companies track life expectancy information to assist in determining the cost of life insurance policies. Last year the average life expectancy of all policyholders was 77 years. ABI Insurance wants to determine if their clients now have a longer life expectancy, on average, so they randomly sample some of their recently paid policies. The ages of the clients in the sample are shown below.
86
75
83
84
81
77
78
79
79
81
76
85
70
76
79
81
73
74
72
83
For more accurate cost determination, ABI Insurance wants to estimate the average life expectancy to within one year with 95% confidence. They would need to sample approximately __________ recently paid policies.
Question 13 options:
76
78
79
80
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Question 14 (2.5 points)
A manufacturer of cheese-filled ravioli supplies a pizza restaurant chain. Based on data collected from its automatic filling process, the amount of cheese inserted into the ravioli is normally distributed. To make sure that the automatic filling process is on target, quality control inspectors take a sample of 25 ravioli and measure the weight of cheese filling. They find a sample mean weight of 15 grams with a standard deviation of 1.5 grams. What is the standard error?
Question 14 options:
0.1 grams
0.2 grams
0.3 grams
0.4 grams
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Question 15 (2.5 points)
A manufacturer of cheese-filled ravioli supplies a pizza restaurant chain. Based on data collected from its automatic filling process, the amount of cheese inserted into the ravioli is normally distributed. To make sure that the automatic filling process is on target, quality control inspectors take a sample of 25 ravioli and measure the weight of cheese filling. They find a sample mean weight of 15 grams with a standard deviation of 1.5 grams. What is the margin of error for 90% confidence?
Question 15 options:
0.3368 grams
0.4323 grams
0.5133 grams
0.5168 grams
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Question 16 (2.5 points)
A manufacturer of cheese-filled ravioli supplies a pizza restaurant chain. Based on data collected from its automatic filling process, the amount of cheese inserted into the ravioli is normally distributed. To make sure that the automatic filling process is on target, quality control inspectors take a sample of 25 ravioli and measure the weight of cheese filling. They find a sample mean weight of 15 grams with a standard deviation of 1.5 grams. For a more accurate determination of the mean weight, the quality control inspectors wish to estimate it within .25 grams with 99% confidence. They should sample approximately __________ ravioli.
Question 16 options:
247
242
239
238
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Question 17 (2.5 points)
A manufacturer of cheese-filled ravioli supplies a pizza restaurant chain. Based on data collected from its automatic filling process, the amount of cheese inserted into the ravioli is normally distributed. To make sure that the automatic filling process is on target, quality control inspectors take a sample of 25 ravioli and measure the weight of cheese filling. They find a sample mean weight of 15 grams with a standard deviation of 1.5 grams. Based on the sample results, the 90% confidence interval for true mean weight is:
Question 17 options:
14.69 to 15.71 grams. We are 90% confident that the mean weight of cheese in the ravioli made by this process is between 14.69 and 15.71 grams.
14.49 to 15.51 grams. We are 90% confident that the mean weight of cheese in the ravioli made by this process is between 14.49 and 15.51 grams.
14.39 to 15.41 grams. We are 90% confident that the mean weight of cheese in the ravioli made by this process is between 14.39 and 15.41 grams.
14.29 to 15.31 grams. We are 90% confident that the mean weight of cheese in the ravioli made by this process is between 29 and 15.31 grams.
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Question 18 (2.5 points)
A small business ships specialty homemade candies to anywhere in the world. Past records indicate that the weight of orders is normally distributed. Suppose a random sample of 16 orders is selected and each is weighed. The sample mean was found to be 110 grams with a standard deviation of 14 grams. The sampling distribution for the sample mean can be modeled using the t-distribution with __________ degrees of freedom.
Question 18 options:
16
15
14
13
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Question 19 (2.5 points)
A consumer group was interested in comparing the operating time of cordless toothbrushes manufactured by two different companies. Group members took a random sample of 18 toothbrushes from Company A and 15 from Company B. Each was charged overnight and the number of hours of use before needing to be recharged was recorded. Company A toothbrushes operated for an average of 119.7 hours with a standard deviation of 1.74 hours; Company B toothbrushes operated for an average of 120.6 hours with a standard deviation of 1.72 hours. Do these samples indicate that Company B toothbrushes operate more hours on average than Company A toothbrushes?
Hours of Operation
Company A
Company B
121 199
122 120
122 121
121 118
117 122
121 121
120 119
122 123
120 119
121 118
121 118
119 120
118 120
120 124
120 123
119
117 118
Histogram information: Company A At 117, frequency is 2; At 118, frequency is 3; At 119, frequency is 3; At 120, frequency is 4; At 121, frequency is 3; At 122, frequency is 2; At 123, frequency is 1;
Company B At 118, frequency is 2; At 119, frequency is 2; At 120, frequency is 3; At 121, frequency is 4; At 122, frequency is 2; At 123, frequency is 1; At 124, frequency is 1;
Are the assumptions and conditions for carrying out the t-test for two means satisfied?
Question 19 options:
Independent group assumption: Company A and Company B are two independent groups.<br /> Randomization condition: Samples were taken by random selection.<br /> Nearly Normal condition: The histograms below show that both sets are unimodal and roughly symmetric.
Independent group assumption: Company A and Company B are dependent groups.<br /> Randomization condition: Samples were taken by random selection.<br /> Nearly Normal condition: The histograms below show that both sets are unimodal and roughly symmetric.
Independent group assumption: Company A and Company B are two independent groups.<br /> Randomization condition: Samples were taken by convenience.<br /> Nearly Normal condition: The histograms below show that both sets are unimodal and roughly symmetric.
Independent group assumption: Company A and Company B are two independent groups.<br /> Randomization condition: Samples were taken by random selection.<br /> Nearly Normal condition: The histograms below show that both sets are unimodal and not symmetric.
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Question 20 (2.5 points)
A small business ships specialty homemade candies to anywhere in the world. Past records indicate that the weight of orders is normally distributed. Suppose a random sample of 16 orders is selected and each is weighed. The sample mean was found to be 110 grams with a standard deviation of 14 grams. Based on the sample results, the 90% confidence interval for true mean weight of orders is from:
Question 20 options:
87 to 116.14 grams. We are 90% confident that the average weight of candy orders is between 103.87 and 116.14 grams.
102.87 to 115.14 grams. We are 90% confident that the average weight of candy orders is between 103.87 and 116.14 grams.
102.77 to 116.04 grams. We are 90% confident that the average weight of candy orders is between 103.87 and 116.14 grams.
103.77 to 116.04 grams. We are 90% confident that the average weight of candy orders is between 103.87 and 116.14 grams.
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Question 21 (2.5 points)
Vending machines on a college campus offer a variety of drinks. The purchasing agent believes that each type of drink is equally preferred by students and consequently orders equal quantities. The number of drinks sold from vending machines on this campus for the last six months is shown in the following table.
DrinkType/Flavor
Lemon Lime Sports Drink
Kiwi Strawberry
Tropical Punch
Grape Sports Drink
Purchase Frequency
159
198
174
149
What is the value of the test statistic and its associated P-value?
Question 21 options:
Assuming that the null hypothesis is not true, the expected frequencies are not equal across the four drinks. Therefore, the expected frequencies = 190.
Assuming that the null hypothesis is not true, the expected frequencies are equal across the four drinks. Therefore, the expected frequencies = 170.
Assuming that the null hypothesis is true, the expected frequencies are equal across the four drinks. Therefore, the expected frequencies = 170.
Assuming that the null hypothesis is true, the expected frequencies are equal across the four drinks. Therefore, the expected frequencies = 190.
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Question 22 (2.5 points)
Vending machines on a college campus offer a variety of drinks. The purchasing agent believes that each type of drink is equally preferred by students and consequently orders equal quantities. The number of drinks sold from vending machines on this campus for the last six months is shown in the following table.
DrinkType/Flavor
Lemon Lime Sports Drink
Kiwi Strawberry
Tropical Punch
Grape Sports Drink
Purchase Frequency
159
198
174
149
State appropriate hypotheses to test whether the purchasing agent is correct.
Question 22 options:
H<sub>0</sub> : The types of drinks are not uniformly distributed (equally preferred) among the students buying drinks.<br /> H<sub>A</sub> : The types of drinks are not uniformly distributed (equally preferred) among the students buying drinks.
H<sub>0</sub> : The types of drinks are uniformly distributed (equally preferred) among the students buying drinks.<br /> H<sub>A</sub> : The types of drinks are uniformly distributed (equally preferred) among the students buying drinks.
H<sub>0</sub> : The types of drinks not uniformly distributed (equally preferred) among the students buying drinks.<br /> H<sub>A</sub> : The types of drinks are uniformly distributed (equally preferred) among the students buying drinks.
H<sub>0</sub> : The types of drinks are uniformly distributed (equally preferred) among the students buying drinks.<br /> H<sub>A</sub> : The types of drinks are not uniformly distributed (equally preferred) among the students buying drinks.
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Question 23 (2.5 points)
Vending machines on a college campus offer a variety of drinks. The purchasing agent believes that each type of drink is equally preferred by students and consequently orders equal quantities. The number of drinks sold from vending machines on this campus for the last six months is shown in the following table.
DrinkType/Flavor
Lemon Lime Sports Drink
Kiwi Strawberry
Tropical Punch
Grape Sports Drink
Purchase Frequency
159
198
174
149
What does x2 equal?
Question 23 options:
6.412
6.812
8.012
8.016
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Question 24 (2.5 points)
A manufacturing plant for recreational vehicles receives shipments from three different parts vendors. There has been a defect issue with some of the electrical wiring in the recreational vehicles manufactured at the plant. The plant manager believes that the defect issue is dependent on the parts vendor. The plant manager reviews a sample of quality assurance inspections from the last six months.
Perfect Parts Co.
Made-4-U Co.
25 Hour Parts Co.
Rejected
53 (57.68)
48 (51.51)
70 (61.81)
Perfect
93 (85.01)
71 (75.90)
88 (91.08)
Not perfect but acceptable
22 (25.30)
31 (22.59)
22 (27.11)
State the conclusion at α = .05.
Question 24 options:
The P-value is rather low (less than α), so we reject the null hypothesis. There is little evidence to indicate that defects vary by vendor.
The P-value is rather low (less than α), so we fail to reject the null hypothesis. There is little evidence to indicate that defects vary by vendor.
The P-value is rather high (greater than α), so we reject the null hypothesis. There is little evidence to indicate that defects vary by vendor.
The P-value is rather high (greater than α), so we fail to reject the null hypothesis. There is little evidence to indicate that defects vary by vendor.
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Question 25 (2.5 points)
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected and the number of units each sold last month recorded. Relevant data appear in the table below.
College GPA
Units Sold
3.25
24
2.87
21
2.66
18
3.33
22
2.87
22
3.21
22
2.76
18
3.91
28
3.55
29
2.55
18
2.44
20
3.22
24
3.01
21
3.44
24
3.22
25
Below are the scatterplot, regression results, and residual plots for these data.
The regression equation is
Units Sold = - 0.48 + 7.42 GPA
Predictor
Coef
SE Coef
T
p
Constant
-0.484
3.256
-0.15
0.884
GPA
7.423
1.044
7.11
0.000
S = 1.57429 R-Sq = 78.3% R-Sq(adj) = 76.8%
Analysis of Variance
Source
DF
SS
MS
F
P
Regression
1
125.30
125.30
50.56
0.000
Residual Error
14
3 4.70
2.48
Total
15
160.00
Are the assumptions/conditions for regression and inference satisfied?
Question 25 options:
Equal spread condition: Neither the original scatterplot nor the residual plot shows any changes in the spread about the line.
Equal spread condition: Neither the original scatterplot nor the residual plot shows any changes in the spread about the line.
Non-equal spread condition: Both the original scatterplot and the residual plot show changes in the spread about the line.
Non-equal spread condition: Both the original scatterplot and the residual plot show any changes in the spread about the line.
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Question 26 (2.5 points)
An operations manager was interested in determining if there is a relationship between the amount of training received by production line workers and the time it takes for them to troubleshoot a process problem. A sample of recently trained line workers was selected. The number of hours of training time received and the time it took (in minutes) for them to troubleshoot their last process problem were captured. The estimated regression equation fit to the data was found to be significant at α = 0.05. The 95% prediction interval for troubleshooting time with 8 hours of training was determined to be 12.822 to 19.261. What is the correct interpretation?
Question 26 options:
We can be 95% confident that the troubleshooting time by a particular line worker who received 8 hours of training will be between 12.822 and 19.261 minutes.
We can be 95% confident that the average troubleshooting time by line workers receiving 8 hours of training is between 12.822 and 19.261 minutes.
The troubleshooting time by a line worker who received 8 hours of training will be between 12.822 and 19.261 minutes 95% of the time.
95% of the time the average troubleshooting time is between 12.822 and 19.261 minutes.
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Question 27 (2.5 points)
A real estate agency, located in a metropolitan area in the northeastern United States, kept data on the various types of properties purchased in the area. Historically, 15% of purchases were for condominiums, 30% were for townhouses, 40% for single family homes, 10% for commercial properties, and 5% for land. With changing demographics, the agency wondered if the current distribution matches the historical distribution. Recent data showed the following:
Type of Property
Condos
Townhouses
Homes
Commercial
Land
Frequency
89 (48.75)
121 (97.5)
78 (130)
25 (32.5)
12 (16.25)
What is the value of x 2?
Question 27 options:
64.538
64.328
62.538
62.328
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Question 28 (2.5 points)
Linear models give a predicted value for each case in the data. Put a new x-value into the equation, and it gives a predicted value, yn, to go with it. But when the new x-value lies far from the data we used to build the regression, how trustworthy is the prediction? The simple answer is that the farther the new x-value is from x, the center of the x-values, the less trust we should place in the predicted value. Once we venture into new x territory, such a prediction is called a(n):
Question 28 options:
extrapolation.
hypothesis.
prediction.
association.
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Question 29 (2.5 points)
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected and the number of units each sold last month recorded. Relevant data appear in the table below.
College GPA
Units Sold
3.25
24
2.87
21
2.66
18
3.33
22
2.87
22
3.21
22
2.76
18
3.91
28
3.55
29
2.55
18
2.44
20
3.22
24
3.01
21
3.44
24
3.22
25
Below are the scatterplot, regression results, and residual plots for these data.
The regression equation is
Units Sold = - 0.48 + 7.42 GPA
Predictor
Coef
SE Coef
T
p
Constant
-0.484
3.256
-0.15
0.884
GPA
7.423
1.044
7.11
0.000
S = 1.57429 R-Sq = 78.3% R-Sq(adj) = 76.8%
Analysis of Variance
Source
DF
SS
MS
F
P
Regression
1
125.30
125.00
50.56
0.000
Residual Error
14
3 4.70
2.48
Total
15
160.00
What is the P-value?
Question 29 options:
< 0.1300
< 0.1000
< 0.0024
< 0.0001
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Question 30 (2.5 points)
Before being released to market, a drug company tests a new allergy medication for potential side effects. A random sample of 160 individuals with allergies was selected for the study. The new allergy medication was randomly assigned to 80 of them, and another popular allergy medication already on the market (Brand C) was assigned to the rest. Out of the 80 given the new allergy medication, 14 reported drowsiness; 22 of the 80 taking Brand C reported drowsiness. Compute the 95% confidence interval for the difference in proportions reporting drowsiness. The confidence interval is:
Question 30 options:
(-0.028, 0.348)
(-0.028, 0.228)
(-0.062, 0.244)
(-0.024, 0.232)
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Question 31 (2.5 points)
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected, the number of units each sold last month recorded. The regression results are shown below.
The regression equation is
Units Sold = - 0.48 + 7.42 GPA
Predictor
Coef
SE Coef
T
p
Constant
-0.484
3.256
-0.15
0.884
GPA
7.423
1.044
7.11
0.000
S = 1.57429 R-Sq = 78.3% R-Sq(adj) = 76.8%
Based on these results, at α = .05 we will:
Question 31 options:
reject the null hypothesis.
not reject the null hypothesis.
conclude that there is no significant relationship between GPA and sales
performance.
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Question 32 (2.5 points)
A real estate agency, located in a metropolitan area in the northeastern United States, kept data on the various types of properties purchased in the area. Historically, 15% of purchases were for condominiums, 30% were for townhouses, 40% for single family homes, 10% for commercial properties, and 5% for land. With changing demographics, the agency wondered if the current distribution matches the historical distribution. Recent data showed the following:
Type of Property
Condos
Townhouses
Homes
Commercial
Land
Frequency
89 (48.75)
121 (97.5)
78 (130)
25 (32.5)
12 (16.25)
State the conclusion at α = .05.
Question 32 options:
The P-value is very large (greater than α), so we reject the null hypothesis. There is strong evidence that the current distribution of property sales differs from the historical distribution.
The P-value is very small (less than α), so we reject the null hypothesis. There is strong evidence that the current distribution of property sales differs from the historical distribution.
The P-value is very small (less than α), so we do not reject the null hypothesis. There is strong evidence that the current distribution of property sales does not differ from the historical distribution.
The P-value is very large (less than α), so we reject the null hypothesis. There is strong evidence that the current distribution of property sales differs from the historical distribution.
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Question 33 (2.5 points)
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected, the number of units each sold last month recorded. The regression results shown are below.
The regression equation is
Units Sold = - 0.48 + 7.42 GPA
Predictor
Coef
SE Coef
T
p
Constant
-0.484
3.256
-0.15
0.884
GPA
7.423
1.044
7.11
0.000
S = 1.57429 R-Sq = 78.3% R-Sq(adj) = 76.8%
Based on these results, the percentage of variability in sales performance (units sold per month) accounted for by college GPA is:
Question 33 options:
34.70%.
50.56%.
78.3%.
100%
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Question 34 (2.5 points)
A real estate agency, located in a metropolitan area in the northeastern United States, kept data on the various types of properties purchased in the area. Historically, 15% of purchases were for condominiums, 30% were for townhouses, 40% for single family homes, 10% for commercial properties, and 5% for land. With changing demographics, the agency wondered if the current distribution matches the historical distribution.
Recent data showed the following:
Type of Property
Condos
Townhouses
Homes
Commercial
Land
Frequency
89 (48.75)
121 (97.5)
78 (130)
25 (32.5)
12 (16.25)
State appropriate hypotheses to test whether the purchasing agent is correct.
Question 34 options:
H<sub>0</sub> : The current distribution of property sales matches the historical distribution.<br /> H<sub>A</sub> : The current distribution of property sales differs from the historical distribution.
H<sub>0</sub> : The current distribution of property sales does not match the historical distribution.<br /> H<sub>A</sub> : The current distribution of property sales differs from the historical distribution.
H<sub>0</sub> : The current distribution of property sales matches the historical distribution.<br /> H<sub>A</sub> : The current distribution of property sales does not differ from the historical distribution.
H<sub>0</sub> : The current distribution of property sales does not match the historical distribution.<br /> H<sub>A</sub> : The current distribution of property sales does not differ from the historical distribution.
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Question 35 (2.5 points)
We check the __________ by plotting the residuals versus either the x variable or the predicted values.
Question 35 options:
Confidence Interval
Variability
Standard Error
Linearity Condition
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Question 36 (2.5 points)
It seems that India stands out from the other countries. There is a much larger proportion of respondents from India who responded Extremely Important. But are the observed differences in the percentages real or just natural sampling variation? Our null hypothesis is that the proportions choosing each alternative are the same for each country. To test that hypothesis, we use a:
Question 36 options:
chi-square test of homogeneity.
chi-square test of independence.
goodness-of-fit test.
linear regression analysis.
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Question 37 (2.5 points)
To see if a model fits the observations or data, we use this concept.
Question 37 options:
Chi-square statistics
Statistical assessment
Goodness-of-fit tests
Prediction intervals
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Question 38 (2.5 points)
A sales manager was interested in determining if there is a relationship between college GPA and sales performance among salespeople hired within the last year. A sample of recently hired salespeople was selected, the number of units each sold last month recorded. The regression results are shown below.
The regression equation is
Units Sold = - 0.48 + 7.42 GPA
Predictor
Coef
SE Coef
T
p
Constant
-0.484
3.256
-0.15
0.884
GPA
7.423
1.044
7.11
0.000
S = 1.57429 R-Sq = 78.3% R-Sq(adj) = 76.8%
Based on these results, the residual standard deviation is:
Question 38 options:
1.044.
1.574.
3.256.
34.70.
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Question 39 (2.5 points)
Vending machines on a college campus offer a variety of drinks. The purchasing agent believes that each type of drink is equally preferred by students and consequently orders equal quantities. The number of drinks sold from vending machines on this campus for the last six months is shown in the following table.
DrinkType/Flavor
Lemon Lime Sports Drink
Kiwi Strawberry
Tropical Punch
Grape Sports Drink
Purchase Frequency
159
198
174
149
State the conclusion at α = .05.
Question 39 options:
The P-value is high (greater than α), so we reject the null hypothesis. This data shows evidence that the drink options are not equally preferred by the students.
The P-value is high (greater than α), so we do not reject the null hypothesis. This data shows evidence that the drink options are equally preferred by the students.
The P-value is low (less than α), so we do not reject the null hypothesis. This data shows evidence that the drink options are equally preferred by the students.
The P-value is low (less than α), so we reject the null hypothesis. This data shows evidence that the drink options are not equally preferred by the students.
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Question 40 (2.5 points)
Chi-square tests are common. Tests for independence are especially widespread. Unfortunately, many people interpret a small P-value as proof of:
