It has been well documented that wages of employees increase as employees gain experience and become more valuable on the labour market. However, wage increases vary a lot depending on employer's remuneration policies, employee's profession, education, and experience, and many other factors. A labour union analyst wants to examine part of the picture by looking at how length of service (LOS) relates to wages among their members. The Assign4Data.xlsx file gives data on LOS in months and wages in thousands of dollars for 60 union members. Treating this data as random sample data from the labour union membership, answer the following questions. a) Using appropriate graphs, comment on the distribution of the wages data and the linear relationship between wages and LOS. Make sure to include a statement about the presence of outliers, leverage and/or influential points, and whether the conditions for performing a regression analysis using these data are warranted. (StatCrunch). b) Irrespective of your answer in a) above, assume that the conditions for a regression analysis are met and perform a simple regression analysis. Make sure to include your StatCrunch output, to write the theoretical and observed regression equations, and to comment of the proportion of the total variation in the dependent variable that is explained by the model. (StatCrunch). c) Produce a scatter plot of standardized (or studentized) residuals vs predicted values for the regression (include your StatCrunch output). Which two model assumptions can be checked with this kind of plot? Comment on the validity of these 2 model assumptions (StatCrunch).
It has been well documented that wages of employees increase as employees gain experience and become more valuable on the labour market. However, wage increases vary a lot depending on employer's remuneration policies, employee's profession, education, and experience, and many other factors. A labour union analyst wants to examine part of the picture by looking at how length of service (LOS) relates to wages among their members. The Assign4Data.xlsx file gives data on LOS in months and wages in thousands of dollars for 60 union members. Treating this data as random sample data from the labour union membership, answer the following questions. a) Using appropriate graphs, comment on the distribution of the wages data and the linear relationship between wages and LOS. Make sure to include a statement about the presence of outliers, leverage and/or influential points, and whether the conditions for performing a regression analysis using these data are warranted. (StatCrunch). b) Irrespective of your answer in a) above, assume that the conditions for a regression analysis are met and perform a simple regression analysis. Make sure to include your StatCrunch output, to write the theoretical and observed regression equations, and to comment of the proportion of the total variation in the dependent variable that is explained by the model. (StatCrunch). c) Produce a scatter plot of standardized (or studentized) residuals vs predicted values for the regression (include your StatCrunch output). Which two model assumptions can be checked with this kind of plot? Comment on the validity of these 2 model assumptions (StatCrunch).
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
Related questions
Question
This is the data we are given
Wages | LOS |
48.3355 | 94 |
49.0279 | 48 |
40.8817 | 102 |
36.5854 | 20 |
46.7596 | 60 |
59.5238 | 78 |
39.1304 | 45 |
39.2465 | 39 |
40.2037 | 20 |
38.1563 | 65 |
50.0905 | 76 |
46.9043 | 48 |
43.1894 | 61 |
60.5637 | 30 |
97.6801 | 70 |
48.5795 | 108 |
67.1551 | 61 |
38.7847 | 10 |
51.8926 | 68 |
51.8326 | 54 |
64.1026 | 24 |
54.9451 | 222 |
43.8095 | 58 |
43.3455 | 41 |
61.9893 | 153 |
40.0183 | 16 |
50.7143 | 43 |
48.84 | 96 |
34.3407 | 98 |
80.5861 | 150 |
33.7163 | 124 |
60.3792 | 60 |
48.84 | 7 |
38.5579 | 22 |
39.276 | 57 |
47.6564 | 78 |
44.6864 | 36 |
45.7875 | 83 |
65.6288 | 66 |
33.5775 | 47 |
41.2088 | 97 |
67.9096 | 228 |
43.0942 | 27 |
40.7 | 48 |
40.5748 | 7 |
39.6825 | 74 |
50.1742 | 204 |
54.9451 | 24 |
32.3822 | 13 |
51.713 | 30 |
55.8379 | 95 |
54.9451 | 104 |
70.2786 | 34 |
57.2344 | 184 |
54.1126 | 156 |
39.8687 | 25 |
27.4725 | 43 |
67.9584 | 36 |
44.9317 | 60 |
51.5612 | 102 |
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