Salary Age MBA What would be the difference in salary betwe

Salary ($) Age MBA

What would be the difference in salary between two 46 year excutives; one with the MBA degree and one without. Please show using the multiple regression analysis.

y= ?

difference=?

Salary ($)	Age	MBA
65,000	26	0
85,000	28	1
74,000	36	0
83,000	35	0
110,000	35	1
160,000	40	1
100,000	41	0
122,000	42	1
85,000	45	0
120,000	46	1
105,000	50	0
135,000	51	1
125,000	55	0
175,000	50	1
156,000	61	1
140,000	63	0

Solution

What would be the difference in salary between two 46 year excutives; one with the MBA degree and one without. Please show using the multiple regression analysis.

y= ?

difference=?

Regression Analysis

R²

0.783

Adjusted R²

0.750

n

16

R

0.885

k

2

Std. Error

16258.881

Dep. Var.

Salary ($)

ANOVA table

Source

SS

df

MS

F

p-value

Regression

12,423,434,098.4553

2  

6,211,717,049.2277

23.50

4.82E-05

Residual

3,436,565,901.5447

13  

264,351,223.1957

Total

15,860,000,000.0000

15  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=13)

p-value

95% lower

95% upper

Intercept

6,974.4478

18,080.2953

0.386

.7059

-32,085.6554

46,034.5510

Age

2,054.7134

390.7042

5.259

.0002

1,210.6484

2,898.7785

MBA

35,236.3216

8,130.0275

4.334

.0008

17,672.4652

52,800.1781

Predicted values for: Salary ($)

95% Confidence Intervals

95% Prediction Intervals

Age

MBA

Predicted

lower

upper

lower

upper

46

1

136,727.588

124,208.524

149,246.651

99,438.111

174,017.064

46

0

101,491.266

88,943.780

114,038.752

64,192.238

138,790.294

The regression line

Salary =6,974.4478 +2,054.7134 *age+35,236.3216*MBA

Predicted salary for 46 year with MBA =$136727.59

Predicted salary for 46 year without MBA =$101491.27

The required difference =$136727.59 - $101491.27

= $35236.32

Regression Analysis
	R²	0.783
	Adjusted R²	0.750	n	16
	R	0.885	k	2
	Std. Error	16258.881	Dep. Var.	Salary ($)
ANOVA table
Source	SS	df	MS	F	p-value
Regression	12,423,434,098.4553	2	6,211,717,049.2277	23.50	4.82E-05
Residual	3,436,565,901.5447	13	264,351,223.1957
Total	15,860,000,000.0000	15
Regression output				confidence interval
variables	coefficients	std. error	t (df=13)	p-value	95% lower	95% upper
Intercept	6,974.4478	18,080.2953	0.386	.7059	-32,085.6554	46,034.5510
Age	2,054.7134	390.7042	5.259	.0002	1,210.6484	2,898.7785
MBA	35,236.3216	8,130.0275	4.334	.0008	17,672.4652	52,800.1781
Predicted values for: Salary ($)
			95% Confidence Intervals	95% Prediction Intervals
Age	MBA	Predicted	lower	upper	lower	upper
46	1	136,727.588	124,208.524	149,246.651	99,438.111	174,017.064
46	0	101,491.266	88,943.780	114,038.752	64,192.238	138,790.294