BUS 308 Week 5 assignment See comments at the right of the data set. ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade 8 23 1.000 23 32 90 9 1 5.8 0 F A The ongoing question that the weekly as

jesbun

Main
Similar Questions

Home >Homework Answsers >Business & Finance homework help >Accounting homework help

Pearson

Score:

Week 5

Correlation and Regression

<1 point>

Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)

Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?

b. Place table here (C8):

Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are

significantly related to Salary?

To compa?

Looking at the above correlations - both significant or not - are there any surprises -by that I

mean any relationships you expected to be meaningful and are not and vice-versa?

Does this help us answer our equal pay for equal work question?

<1 point>

Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint,

age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of

expressing an employee’s salary, we do not want to have both used in the same regression.)

Plase interpret the findings.

Ho: The regression equation is not significant.

Ha: The regression equation is significant.

Ho: The regression coefficient for each variable is not significant

Note: technically we have one for each input variable.

Ha: The regression coefficient for each variable is significant

Listing it this way to save space.

Sal

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.9915591

R Square

0.9831894

Adjusted R Square

0.9808437

Standard Error

2.6575926

Observations

ANOVA

Significance F

Regression

17762.3

2960.38

419.1516

1.812E-36

Residual

303.7003

7.0628

Total

18066

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-1.749621

3.618368

-0.4835

0.631166

-9.046755

5.5475126

-9.04675504

5.54751262

Midpoint

1.2167011

0.031902

38.1383

8.66E-35

1.1523638

1.2810383

1.152363828

1.28103827

Age

-0.004628

0.065197

-0.071

0.943739

-0.136111

0.1268547

-0.13611072

0.1268547

Performace Rating

-0.056596

0.034495

-1.6407

0.108153

-0.126162

0.0129695

-0.12616237

0.01296949

Service

-0.0425

0.084337

-0.5039

0.616879

-0.212582

0.1275814

-0.21258209

0.12758138

Gender

2.4203372

0.860844

2.81159

0.007397

0.6842792

4.1563952

0.684279192

4.15639523

Degree

0.2755334

0.799802

0.3445

0.732148

-1.337422

1.8884885

-1.33742165

1.88848848

Note: since Gender and Degree are expressed as 0 and 1, they are considered dummy variables and can be used in a multiple regression equation.

Interpretation:

For the Regression as a whole:

What is the value of the F statistic:

What is the p-value associated with this value:

Is the p-value <0.05?

Do you reject or not reject the null hypothesis:

What does this decision mean for our equal pay question:

For each of the coefficients:

Intercept

Midpoint

Age

Perf. Rat.

Service

Gender

Degree

What is the coefficient's p-value for each of the variables:

Is the p-value < 0.05?

Do you reject or not reject each null hypothesis:

What are the coefficients for the significant variables?

Using only the significant variables, what is the equation?

Salary =

Is gender a significant factor in salary:

If so, who gets paid more with all other things being equal?

How do we know?

<1 point>

Perform a regression analysis using compa as the dependent variable and the same independent

variables as used in question 2. Show the result, and interpret your findings by answering the same questions.

Note: be sure to include the appropriate hypothesis statements.

Regression hypotheses

Ho:

Ha:

Coefficient hyhpotheses (one to stand for all the separate variables)

Ho:

Ha:

Place D94 in output box.

Interpretation:

For the Regression as a whole:

What is the value of the F statistic:

What is the p-value associated with this value:

Is the p-value < 0.05?

Do you reject or not reject the null hypothesis:

What does this decision mean for our equal pay question:

For each of the coefficients:

Intercept

Midpoint

Age

Perf. Rat.

Service

Gender

Degree

What is the coefficient's p-value for each of the variables:

Is the p-value < 0.05?

Do you reject or not reject each null hypothesis:

What are the coefficients for the significant variables?

Using only the significant variables, what is the equation?

Compa =

Is gender a significant factor in compa:

If so, who gets paid more with all other things being equal?

How do we know?

<1 point>

Based on all of your results to date,

Do we have an answer to the question of are males and females paid equally for equal work?

If so, which gender gets paid more?

How do we know?

Which is the best variable to use in analyzing pay practices - salary or compa? Why?

What is most interesting or surprising about the results we got doing the analysis during the last 5 weeks?

<2 points>

Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question?

What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?

See comments at the right of the data set.
ID	Salary	Compa	Midpoint	Age	Performance Rating	Service	Gender	Raise	Degree	Gender1	Grade
8	23	1.000	23	32	90	9	1	5.8	0	F	A	The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
10	22	0.956	23	30	80	7	1	4.7	0	F	A	Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
11	23	1.000	23	41	100	19	1	4.8	0	F	A
14	24	1.043	23	32	90	12	1	6	0	F	A	The column labels in the table mean:
15	24	1.043	23	32	80	8	1	4.9	0	F	A	ID – Employee sample number	Salary – Salary in thousands
23	23	1.000	23	36	65	6	1	3.3	1	F	A	Age – Age in years	Performance Rating – Appraisal rating (Employee evaluation score)
26	24	1.043	23	22	95	2	1	6.2	1	F	A	Service – Years of service (rounded)	Gender: 0 = male, 1 = female
31	24	1.043	23	29	60	4	1	3.9	0	F	A	Midpoint – salary grade midpoint	Raise – percent of last raise
35	24	1.043	23	23	90	4	1	5.3	1	F	A	Grade – job/pay grade	Degree (0= BS\BA 1 = MS)
36	23	1.000	23	27	75	3	1	4.3	1	F	A	Gender1 (Male or Female)	Compa - salary divided by midpoint
37	22	0.956	23	22	95	2	1	6.2	1	F	A
42	24	1.043	23	32	100	8	1	5.7	0	F	A
3	34	1.096	31	30	75	5	1	3.6	0	F	B
18	36	1.161	31	31	80	11	1	5.6	1	F	B
20	34	1.096	31	44	70	16	1	4.8	1	F	B
39	35	1.129	31	27	90	6	1	5.5	1	F	B
7	41	1.025	40	32	100	8	1	5.7	0	F	C
13	42	1.050	40	30	100	2	1	4.7	1	F	C
22	57	1.187	48	48	65	6	1	3.8	0	F	D
24	50	1.041	48	30	75	9	1	3.8	1	F	D
45	55	1.145	48	36	95	8	1	5.2	0	F	D
17	69	1.210	57	27	55	3	1	3	0	F	E
48	65	1.140	57	34	90	11	1	5.3	1	F	E
28	75	1.119	67	44	95	9	1	4.4	1	F	F
43	77	1.149	67	42	95	20	1	5.5	1	F	F
19	24	1.043	23	32	85	1	0	4.6	1	M	A
25	24	1.043	23	41	70	4	0	4	0	M	A
40	25	1.086	23	24	90	2	0	6.3	0	M	A
2	27	0.870	31	52	80	7	0	3.9	0	M	B
32	28	0.903	31	25	95	4	0	5.6	0	M	B
34	28	0.903	31	26	80	2	0	4.9	1	M	B
16	47	1.175	40	44	90	4	0	5.7	0	M	C
27	40	1.000	40	35	80	7	0	3.9	1	M	C
41	43	1.075	40	25	80	5	0	4.3	0	M	C
5	47	0.979	48	36	90	16	0	5.7	1	M	D
30	49	1.020	48	45	90	18	0	4.3	0	M	D
1	58	1.017	57	34	85	8	0	5.7	0	M	E
4	66	1.157	57	42	100	16	0	5.5	1	M	E
12	60	1.052	57	52	95	22	0	4.5	0	M	E
33	64	1.122	57	35	90	9	0	5.5	1	M	E
38	56	0.982	57	45	95	11	0	4.5	0	M	E
44	60	1.052	57	45	90	16	0	5.2	1	M	E
46	65	1.140	57	39	75	20	0	3.9	1	M	E
47	62	1.087	57	37	95	5	0	5.5	1	M	E
49	60	1.052	57	41	95	21	0	6.6	0	M	E
50	66	1.157	57	38	80	12	0	4.6	0	M	E
6	76	1.134	67	36	70	12	0	4.5	1	M	F
9	77	1.149	67	49	100	10	0	4	1	M	F
21	76	1.134	67	43	95	13	0	6.3	1	M	F
29	72	1.074	67	52	95	5	0	5.4	0	M	F