Question

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A researcher interviews 50 employees of a large manufacturer and colects data on each worker's hourly wage (Wage), years of higher education (EDUC), experience (EXPER) and age AGE). The data can be found in the SPSS 6 Wage excel data file posted on Connect. Use SPSS to generate the output. Upload the one page Word file on to Connect by the due date. The face to face and hybrid students also need to turn in the paper copy in the class on the following Monday to earn the credit. Paste SPSS output in the Blue cells, and write your responses in the green cells. Make sure that your work doesn't exceed one page. SPSS Regression ANOVA table (F test) SPSS Regression Coefficients table (T-tests) SPSS Model Summary Output Write down the estimated regression equation to predict estimated Wage. You can use the following equation template ,0000X1 + .0000X2 + .000x3 Are all (b1, b2, b3) signs as expected? Interpret the coefficient of EDUGC Interpret the coefficient of determination. Is age significantly related to wage when Education and Experience are present in the model? Explain your findings based on the data provided to you. Why do you think this might be happening? Predict the hourly wage of 40-year old employee who has 5 years of higher education and 8 years of experience. 1 2 5 5 20 1 R2-29522 5 2 4 3 5 8 1 0 9 2 3 4 5 3 5 3 9 4 5 2 8 4 4 3 8 6 3 3 5 9 3 4 6 0 5 0 4 1027251 1 1 2 112 14456694518974191469644566466957464465944956794ー4 1 1 5 5 52 8 1 2 5 17 4 7 1 4 1 4 5 5 0 9 7 8 0 4 7 5 5 0 0 6 6 3 0 4 3 7 5 6 9 6 8 5 8 3 7 6 4 5 7 5 0 1573653634 2122 32122212221221 2112211112

Answer 1

regression summary , coefficient table and F-test

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.585123024
R Square	0.342368953
Adjusted R Square	0.299479971
Standard Error	5.412552887
Observations	50
ANOVA
	df	SS	MS	F	Significance F
Regression	3	701.5751592	233.8583864	7.982678579	0.00021757
Residual	46	1347.603523	29.29572876
Total	49	2049.178682
	Coefficients	Standard Error	t Stat	P-value	Lower 95%
Intercept	7.872986591	4.087193427	1.926257401	0.06026464	-0.354107071
EDUC	1.437060506	0.338639743	4.243626263	0.000105402	0.755414058
EXPER	0.448282229	0.141867317	3.159869648	0.002789784	0.162718131
AGE	-0.011386246	0.083422321	-0.1364892	0.89203017	-0.179306669

wage^ = 7.873 + 1.4371 Educ + 0.4483 Exper -0.0114 Age

yes, all signs are as expected
wage is positively correlated with Education and experience
and is negatively correlated with age

coefficient of Education is 1.4371
as education increases by 1 unit, on average wage will increase by 1.45 units

coefficient of detemrination = 0.3427
hence 34.27% of variation in wage is explained by this model