Question

blips 16.23 11.4 5.77 7.67 12.68 11.38 14.93 12.02 10.46 10.33 8.64 9.45 11.69 9.48 7.91 10.32 7.3 10.1 10.11 10.84 13.9 7.74 8.03 11.25 11.84 8.96 12.19 9.33 10.95 6.87 12.92 8.06 11.17 9.87 8.49 10.23 12.12 7.63 8.1 7.57 10.32 11.47 11.02 8.56 8.62 6.87 7.5 8.67 6.49 7. Use simple linear regression to assess whether the average number of \"blips\" can be predicted by i) age ii) number of months they have worked iii) the number of hours they work in a week [12] [12] [12]

          blips
16.23
11.4
5.77
7.67
12.68
11.38
14.93
12.02
10.46
10.33
8.64
9.45
11.69
9.48
7.91
10.32
7.3
10.1
10.11
10.84
13.9
7.74
8.03
11.25
11.84
8.96
12.19
9.33
10.95
6.87
12.92
8.06
11.17
9.87
8.49
10.23
12.12
7.63
8.1
7.57
10.32
11.47
11.02
8.56
8.62
6.87
7.5
8.67
6.49
7. Use simple linear regression to assess whether the average number of \"blips\" can be
predicted by
i) age
ii) number of months they have worked
iii) the number of hours they work in a week
[12]
[12]
[12]
        
Show more…
blips
16.23
11.4
5.77
7.67
12.68
11.38
14.93
12.02
10.46
10.33
8.64
9.45
11.69
9.48
7.91
10.32
7.3
10.1
10.11
10.84
13.9
7.74
8.03
11.25
11.84
8.96
12.19
9.33
10.95
6.87
12.92
8.06
11.17
9.87
8.49
10.23
12.12
7.63
8.1
7.57
10.32
11.47
11.02
8.56
8.62
6.87
7.5
8.67
6.49
7. Use simple linear regression to assess whether the average number of b̈lipsc̈an be
predicted by
i) age
ii) number of months they have worked
iii) the number of hours they work in a week
[12]
[12]
[12]

Added by Kyle F.

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Elementary Statistics a Step by Step Approach
Elementary Statistics a Step by Step Approach
Allan G. Bluman 9th Edition
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able[[months,age,hours,blips],[11,36,8,16.23],[28,27,16,11.4],[30,20,8,11],[5,29,8,5.77],[4,32,42,7.67],[60,52,8,12.68],[48,64,16,11.38],[50,24,25,14.93],[52,24,8,12.02],[38,39,25,10.46],[26,29,16,10.33],[12,19,16,8.64],[10,36,42,9.45],[67,35,42,11.69],[24,51,25,9.48],[10,18,16,7.91],[12,25,8,10.32],[5,22,42,7.3],[26,42,25,10.1],[23,35,42,10.11],[49,22,42,10.84],[37,21,42,13.9],[1,24,42,7.74],[3,20,16,8.03],[35,40,25,11.25],[20,26,42,11.84],[20,41,42,8.96],[23,37,42,12.19],[5,27,16,9.33],[36,20,8,10.95],[3,22,42,6.87],[26,37,25,12.92],[4,29,42,8.06],[16,36,16,11.17],[17,51,16,9.87],[2,24,42,8.49],[38,24,8,10.23],[59,28,25,12.12],[5,24,16,7.63],[1,29,42,8.1],[3,21,25,7.57],[47,41,8,10.32],[34,39,42,11.47],[7,37,42,11.02],[1,18,42,8.56],[3,21,42,8.62],[1,18,42,6.87],[18,42,7.5,],[22,42,8.67,],[1,22,6.49,]] Use simple linear regression to assess whether the average number of "blips" can be predicted by i) age number of months they have worked the number of hours they work in a week months 11 28 30 5 4 60 48 50 52 38 26 12 10 67 24 10 12 5 26 23 49 37 1 3 35 20 20 23 5 36 3 26 4 16 17 2 38 59 5 1 3 26 47 34 7 17 1 age 36 27 20 29 32 52 64 24 24 39 29 19 36 35 51 18 25 22 42 35 22 21 24 20 40 26 41 37 27 20 22 37 29 36 51 24 24 hours 8 16 8 8 42 8 16 25 8 25 16 16 42 42 25 16 8 42 25 42 42 42 42 16 25 42 42 42 16 8 42 25 42 16 16 42 8 25 16 42 25 8 42 42 42 42 42 42 5 42 blips 16.23 11.4 11 5.77 7.67 12.68 11.38 14.93 12.02 10.46 10.33 8.64 9.45 11.69 9.48 7.91 10.32 7.3 10.1 10.11 10.84 13.9 7.74 8.03 11.25 11.84 8.96 12.19 9.33 10.95 6.87 12.92 8.06 11.17 9.87 8.49 10.23 12.12 7.63 8.1 7.57 10.32 11.47 11.02 8.56 8.62 6.87 7.5 8.67 6.49 24 29 21 41 39 37 18 21 18 5 18 22 7. Use simple linear regression to assess whether the average number of"blips" can be predicted by age [12] number of months they have worked iii) [12] the number of hours they work in a week [12]
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b29-begintabularcccccccccccc-hline-a-b-c-d-mathrme-mathrmf-g-mathrmh-1-j-k-mathrml-hline-new-subscriptions-hours-presentation-multicolumn3c-note-under-presentation-0-formal-1-informal-hline-1256-282-2

Analyze these data and develop a multiple regression model to predict the number of new subscriptions for a week, based on the number of hours spent on telemarketing and the sales presentation type. Write a report, giving detailed findings concerning the regression model used. The report should include practical interpretations of the major sections of the printout (e.g. Multiple R, R Squared, slopes, etc.). Be sure you present the regression model and make a prediction for a week when formal presentations were made and there were 200 hours spent on telemarketing.

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begintabularcccccccccccc-hline-a-b-c-d-e-mathrmf-g-mathrmh-1-j-k-l-hline-new-subscriptions-hours-presentation-note-under-prese-ntation-0-forma-l-1-informal-hline-1256-282-0-summary-output-hline-1405-3

Analyze these data and develop a multiple regression model to predict the number of new subscriptions for a week, based on the number of hours spent on telemarketing and the sales presentation type. Write a report, giving detailed findings concerning the regression model used. The report should include practical interpretations of the major sections of the printout (e.g. Multiple R, R Squared, slopes, etc.). Be sure you present the regression model and make a prediction for a week when formal presentations were made and there were 200 hours spent on telemarketing

Dominador T.

large-company-employs-several-thousand-people-in-the-manufacture-of-keyboards-equipment-cases-and-cables-for-the-small-computer-industry-the-personnel-manager-of-the-company-would-like-to-fi-93117

A large company employs several thousand people in the manufacture of keyboards, equipment cases, and cables for the small-computer industry. The personnel manager of the company would like to find ways to forecast the absentee rate among the company employees. An effective method of forecasting would greatly strengthen the ability to plan properly. He took a sample of 15 employees and recorded the number of absent days (Y) during the last fiscal year along with employee age (X). The computer output of a regression analysis is as follows: The regression equation is absent days = 4.28 + 0.254 * age. Predictor Constant age Coef 4.277 0.25379 SE Coef 1.116 0.02850 T 3.83 8.91 P 0.002 0.000 S = 1.10807 R-Sq = 85.9% R-Sq(adj) = 84.8% Analysis of Variance Source Regression Residual Error Total DF 1 13 14 SS 97.372 15.962 113.333 MS 97.372 1.228 F 79.30 P 0.000 Test the regression coefficient B1 of age is larger than 0.2 using a 5% significance level. Find a 95% confidence interval for the intercept. An employee, John, is 30 years old. According to the regression equation, what is his expected number of absent days in the coming fiscal year? The sample mean and sample standard deviation for age are 37.87 and 10.39, respectively. Find a 95% prediction interval for the mean absent days of 30-year-old employees.

Supreeta N.


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Transcript

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00:03 Now here for our solution, so for this part we have summation of y is equals to n a plus b 1 summation of x sub 1 plus b 2 summation of x sub 2.
00:21 So summation of y x sub 1 is equals to a summation of x sub 1 plus b 1 summation of x sub 2 1 plus b 2 summation of x sub 2 x sub 1.
00:34 So we have summation of y x sub 2 will be equal to a summation of x sub 2 plus b 1...
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