Question

Suppose the following table was generated from sample data of 20 employees relating hourly wage to years of experience and whether or not they have a college degree. Using statistical software, create an indicator (dummy) variable for the variable "Degree" and find the regression equation. Is there enough evidence to support the claim that on average employees with a college degree have higher hourly wages than those without a college degree at the 0.05 level of significance? If yes, write the coefficient of the dummy variable in the space provided, rounded to two decimal places. Else, select "There is not enough evidence."

Wage   Experience   Degree
16.00 16 No
24.52 20 Yes
17.68 12 Yes
16.00 16 No
33.98 27 Yes
19.51 29 No
19.86 26 No
16.00 16 No
6.75 1 Yes
28.70 27 Yes
18.97 24 No
17.29 21 No
10.60 3 Yes
17.88 12 Yes
12.77 9 No
7.48 3 Yes
27.70 26 Yes
11.20 6 Yes
19.20 30 No
22.61 30 No

Answer 1

Result:

Suppose the following table was generated from sample data of 20 employees relating hourly wage to years of experience and whether or not they have a college degree. Using statistical software, create an indicator (dummy) variable for the variable "Degree" and find the regression equation. Is there enough evidence to support the claim that on average employees with a college degree have higher hourly wages than those without a college degree at the 0.05 level of significance? If yes, write the coefficient of the dummy variable in the space provided, rounded to two decimal places. Else, select "There is not enough evidence."

Dummy variable created with Degree with yes=1 and No =0.

The regression coefficient for degree is 6.654 is positive and is significant at 0.05 level ( t= 4.92, P=0.0001).

There is enough evidence to support the claim that on average employees with a college degree have higher hourly wages than those without a college degree at the 0.05 level of significance.

coefficient of the dummy variable : 6.65

Excel Addon Megastat used.

Menu used: correlation/Regression ---- Regression Analysis

Regression Analysis
	R²	0.860
	Adjusted R²	0.844	n	20
	R	0.927	k	2
	Std. Error of Estimate	2.741	Dep. Var.	Wage
Regression output					confidence interval
variables		coefficients	std. error	t (df=17)	p-value	95% lower	95% upper
Intercept	a =	2.019	1.775	1.137	.2713	-1.727	5.764
Experience	b1 =	0.728	0.071	10.201	0.0000	0.578	0.879
Degree	b2 =	6.654	1.352	4.920	.0001	3.801	9.507
ANOVA table
Source	SS	df	MS	F	p-value
Regression	785.214	2	392.607	52.26	0.0000
Residual	127.711	17	7.512
Total	912.926	19