# Assignment template for Week 5 IP

ASSIGNMENT TEMPLATE for Week 5 IP

Please use this template to submit your assignment, by copying and pasting it into Word - **Assignment template for Week 5 IP** introduction. Please note that points have been set aside for careful use of the template. The template parallels the assignment requirements closely and is therefore likely to help you in meeting the requirements.

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A title page in appropriate APA format should accompany your submission

Please note the page limits indicated and conform to them (3- to 4 pages)

Points (total points 150)

1. Run a simple regression calculation in EXCEL using BENEFITS as the independent variable and INTRINSIC job satisfaction as the dependent variable using all 29 data points from the AIU dataset.

1.a. Give the graph of the trendline below:

(To do the correlation and regression, the steps in EXCEL are as indicated on page 547, under “Correlation and Regression”.

In order to graph a scatter plot and then insert a trendline in it, you can do the following:

1. Follow the steps on page 92 of the text to get the scatter plot.

2. Once you have the scatter plot, to include the trendline (which is also the regression line). right click directly on one of the dots on the scatter plot. Then select Add Trendline from the shortcut menu that appears)

1.b. Provide the summary results of the regression copied and pasted below from your Excel output, as shown in the text. Do not submit Excel files.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.160724

R Square

0.025832

Adjusted R Square

-0.01025

Standard Error

1.184525

Observations

29

ANOVA

df

SS

MS

F

Significance F

Regression

1

1.004573

1.004573

0.715966

0.404907

Residual

27

37.8837

1.4031

Total

28

38.88828

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

6.639812

1.437345

4.619499

8.47E-05

3.690626

9.588997

3.690626

9.588997

X Variable 1

-0.23345

0.275901

-0.84615

0.404907

-0.79955

0.332649

-0.79955

0.332649

1.c. State regression results below in mathematical form (i.e. in the form y=mx+b), and explain slope and y-intercept and their signs, and explain their meanings:

The regression equation is y = -0.2335x + 6.6398

The slope m = -0.2335, this gives the rate at which intrinsic job satisfaction decreases per unit increase in the benefits.

The y-intercept c = 6.6398; this is the value of intrinsic job satisfaction with no benefits.

1.d. What is the R-squared value and its significance?

The R-squared value is 0.0258. This value lies in the range from 0 to 1 for any regression analysis. The values close to 1 means very good fit of the data and values close to 0 means poor fit of data.

Small value of R-squared in this analysis means that the regression equation will give erroneous results.

2. Run a simple regression calculation using BENEFITS as the independent variable and EXTRINSIC job satisfaction as the dependent variable using all 29 data points from the AIU dataset.

2. a. Give the graph of the trendline below:

2.b. Provide the summary results of the regression copied and pasted below from your Excel output, as shown in the text. Do not submit Excel files.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.072946

R Square

0.005321

Adjusted R Square

-0.03152

Standard Error

1.648413

Observations

29

ANOVA

df

SS

MS

F

Significance F

Regression

1

0.392474

0.392474

0.144437

0.706882

Residual

27

73.36615

2.717265

Total

28

73.75862

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

5.958132

2.000242

2.978706

0.006053

1.853977

10.06229

1.853977

10.06229

X Variable 1

-0.14592

0.38395

-0.38005

0.706882

-0.93372

0.64188

-0.93372

0.64188

2.c. State regression results below in mathematical form (i.e. in the form y=mx+b, and explain slope and y-intercept and their signs, and explain their meanings:

The regression equation is y = -0.1459x + 5.9581

The slope m = -0.1459, this gives the rate at which intrinsic job satisfaction decreases per unit increase in the benefits.

The y-intercept c = 5.9581; this is the value of intrinsic job satisfaction with no benefits.

2.d. What is the R-squared value and its significance?

The R-squared value is 0.0053. This value lies in the range from 0 to 1 for any regression analysis. The values close to 1 means very good fit of the data and values close to 0 means poor fit of data.

Small value of R-squared in this analysis means that the regression equation will give erroneous results.

40 points

3. Run a simple regression calculation using BENEFITS as the independent variable and OVERALL job satisfaction as the dependent variable using all 29 data points from the AIU dataset.

3.a.Give the graph of the trendline below:

3.b. Provide the summary results of the regression copied and pasted below from your Excel output, as shown in the text. Do not submit Excel files.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.200858

R Square

0.040344

Adjusted R Square

0.004801

Standard Error

1.331033

Observations

29

ANOVA

df

SS

MS

F

Significance F

Regression

1

2.010974

2.010974

1.135085

0.296128

Residual

27

47.83454

1.77165

Total

28

49.84552

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

6.134972

1.615123

3.798456

0.000752

2.821016

9.448928

2.821016

9.448928

X Variable 1

-0.3303

0.310026

-1.0654

0.296128

-0.96642

0.305817

-0.96642

0.305817

3.c. State regression results below in mathematical form (i.e. in the form y=mx+b, and explain slope and y-intercept and their signs, and explain their meanings:

The regression equation is y = -0.3303x + 6.135

The slope m = -0.3303, this gives the rate at which intrinsic job satisfaction decreases per unit increase in the benefits.

The y-intercept c = 6.135; this is the value of intrinsic job satisfaction with no benefits.

3.d. What is the R-squared value and its significance?

The R-squared value is 0.0403. This value lies in the range from 0 to 1 for any regression analysis. The values close to 1 means very good fit of the data and values close to 0 means poor fit of data.

Small value of R-squared in this analysis means that the regression equation will give erroneous results.

40 points

4. a. Comment specifically on the similarities and differences in the output results.

The results are similar in the sense that all the three parameters i.e. Intrinsic, Extrinsic and Overall job satisfaction show decreasing trend with increasing benefits. This is evident from the negative value of the slope of the regression line.

4.b. Show formula used to calculate the correlation coefficient.

Correlation coefficient was calculated using the following formula:

Correlation coefficient

4.c. Identify which regression produced the strongest correlation coefficient.

The regression between overall job satisfaction and benefits produced the strongest correlation coefficient.

4.d. Explain why it has the strongest correlation.

This has the strongest correlation because the value of R-squared is the largest for this regression.

20 points

Citations

10 points

Submit file using the following file naming system: your last name_first name_U5_ip

Points for using required template and appropriate file name

Grading Guideline for Unit 5 DB

20/20: Describe practical applications of Unit 2 Individual project study and use of results in workplace.

20/20: Describe correlational research. Name variable from Unit 2 survey and one from workplace (that is not already included in the AIU survey) and explain possible correlation relationship and basis for choice.

10/10: Describe what you learned about correlation and causation during course

25/25: Comment substantively on the postings of at least two other students/bibliography