Question

We assume that our wages will increase as we gain experience and become more valuable to our employers. Wages also increase because of inflation. By examining a sample of employees at a given point in time, we can look at part of the picture. How does length of service (LOS) relate to wages? The data here (data35.dat) is the LOS in months and wages for 60 women who work in Indiana banks. Wages are yearly total income divided by the number of weeks worked. We have multiplied wages by a constant for reasons of confidentiality.

(b) Find the least-squares line. Summarize the significance test for the slope. What do you conclude? Wages = + LOS t = P =

(c) State carefully what the slope tells you about the relationship between wages and length of service.

(d) Give a 95% confidence interval for the slope. ( , )

worker  wages   los     size
1       44.5351 105     Large
2       53.2709 65      Small
3       50.3344 22      Small
4       37.1558 84      Small
5       77.5051 80      Large
6       42.4776 57      Small
7       40.6504 27      Large
8       47.4898 104     Large
9       51.9703 57      Large
10      42.512  70      Small
11      67.9761 97      Large
12      45.4041 22      Small
13      48.3239 63      Small
14      56.1946 148     Large
15      69.0117 30      Large
16      46.2715 164     Large
17      45.8207 77      Large
18      38.4024 69      Small
19      40.429  128     Large
20      44.5199 56      Large
21      57.2677 77      Large
22      84.327  80      Small
23      38.7327 95      Large
24      49.6938 42      Small
25      60.0627 36      Large
26      56.8834 58      Small
27      48.8619 19      Small
28      55.77   86      Large
29      41.7362 71      Large
30      38.8925 70      Large
31      48.1473 33      Small
32      45.0164 126     Large
33      48.6848 90      Large
34      40.6247 66      Small
35      64.2936 33      Large
36      56.7877 173     Large
37      38.9519 99      Large
38      52.0569 50      Small
39      41.1595 184     Large
40      45.185  68      Small
41      52.5969 66      Small
42      51.1456 73      Small
43      54.441  60      Large
44      57.9979 29      Small
45      60.6416 44      Large
46      39.4046 144     Small
47      56.2529 21      Large
48      40.6632 56      Large
49      54.6912 47      Small
50      49.8493 47      Large
51      42.4398 25      Large
52      62.5907 67      Large
53      55.4651 57      Large
54      60.8077 21      Small
55      47.5402 32      Small
56      62.2112 64      Large
57      39.8335 120     Small
58      59.1642 37      Large
59      40.0288 129     Small
60      59.0396 50      Large

Answer 1

ANSWER:-

we enter the data in excel and then goto data > data analysis tab and select regression C D G H J los wages Data Analysis ? 64.8821 39 Analysis Tools OK 81 S 39.54 F-Test Two-Sample for Variances Fourier Analysis Histogram Moving Average Random Number Generation 84.9496 45 S Cancel 61 9 57.2112 Help 64.0631 35 Rank and Percentile 48 S 65.2939 Regression Sampling t-Test: Paired Two Sample for Means t-Test: Two-Sample Assuming Equal Variances 38.3778 41 65.7746 39 54.3215 58 45 Small 53.4196 49 Large 53.5678 69.3951 111 Small 53.9431 67 Small 73 Large 55.3892 95 Large 57.1744 37.4228 146 Large 54 Large 39.4511 34 Small 40.2389 72.2661 77 Large

MIL: ? Regression Input OK Input Y Range: SBS1:SBS61 Cancel Input X Range SCS1:SCS61 Help Labels Constant is Zero 95 Confidence Level: Output options Qutput Range: O New Worksheet Ply: New Workbook Residuals Residual Plots Line Fit Plots Residuals Standardized Residuals Normal Probability Normal Probability Plots 5Tarpe SUMMARY OUTPUT los Residual Plot Regression Stotstics Multiple R 0.164132171 R Square 0.02693937 Adjusted R Square Standard Errer 0.010162462 12.61403812 60 Observations t00 o0 250 ANOVA los df gnificance F S5 MS Ragrassion Residual 255.4958202 255.4958 1.605741 0.210156 9228.609543 159.114 Total 9434.105303 10 Standard Errar Coefficients r Star P-vaiue tower 958upper 958 Lower 95.08% upper 95.0% 3.520097697 Intercept 56.9646589 16.1827 3.59E-23 49.91843 64.01091 49.91842779 64.01091001 Ips 004387208 0.055593752 -1.26713 0.210156 -0.14341 0.032225 0.143413202 0.032225758 2 sengesay

The regression equation is wages 56.96-0.0555*los The 95% Cl of the slope is Lower Upper 95% 95% 49.91843 Intercept 64.01091 los 0.14341 0.032226 The t and p values are P-value t Stat los -1,26718 0.210156 The slope means that for every unit increasein the value of los the wages decreases by 0.0555 units los 250 y -0.4846x +96.821 200 R2 0.0269 150 los 100 Linear (los) 50 C 0 20 40 60 80 100 Axis Title Axis Title OS
I HOPE THAT THIS ANS IS USEFUL .................... THANK YOU