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ID: 3316769 • Letter: P
Question
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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 glven point in time, we can look at part af the picture. How does length ot service Los relate to wages? The data here data 2, dat is the L s in months and wages or b women who work in Indiana banks. wages re year y total income divided by the number o weeks worked we have multiplied wages by a constant or reasons of contidentiality, a) Plot wages versus Los. Consider the relationship and whether or not linear regression might be appropriate. (Do this on paper. Your instructor may ask you to turn in this graph.) b Find the least-squares line. Summarize the significance test for the slope. What do you conclude? wages LOs State carefully what the slope talls you about thc relationship between wagcs and length of servicc (d) Give 95% confidence interval for the slopeExplanation / Answer
Enter the data into Excel and create a scatter plot.
(a)The plot is seen to be clustered and the straight line doesn’t seem to appropriately represent the points.
(b)Click on Data tab -> Data Analysis Tool pack -> Regression -> Y-axis : wages, X-axis : los, tick labels -> OK.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.12615
R Square
0.015914
Adjusted R Square
-0.00105
Standard Error
14.93795
Observations
60
ANOVA
df
SS
MS
F
Significance F
Regression
1
209.2905
209.2905
0.937923
0.336834
Residual
58
12942.26
223.1425
Total
59
13151.55
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
54.47979
3.873921
14.06322
2.42E-20
46.7253
62.23429
los
0.040345
0.041659
0.968464
0.336834
-0.04304
0.123735
Wages = 54.47979 + 0.040345*los
= 0.12615
(c) The slope indicates the increase in the value of wage for an unit increase in the value of los.
(d)95% CI : (-0.04304 0.123735)
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.12615
R Square
0.015914
Adjusted R Square
-0.00105
Standard Error
14.93795
Observations
60
ANOVA
df
SS
MS
F
Significance F
Regression
1
209.2905
209.2905
0.937923
0.336834
Residual
58
12942.26
223.1425
Total
59
13151.55
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
54.47979
3.873921
14.06322
2.42E-20
46.7253
62.23429
los
0.040345
0.041659
0.968464
0.336834
-0.04304
0.123735