Ken Kelly

Ken Kelly

Regression Analyses Using Excel:

Regression analyses using excel:

Part 1:

BENEFITS and INTRINSIC as independent and dependent variable respectively: (equation 1)

The following is a summary of the results:

Regression Statistics

Multiple R

0.468804

R Square

0.219778

Adjusted R Square

0.185855

Standard Error

0.932503

Observations

25

ANOVA

df

SS

MS

F

Significance F

Regression

1

5.63369

5.63369

6.4787733

0.018084926

Residual

23

19.99991

0.869561

Total

24

25.6336

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

1.554273

1.411685

1.101006

0.2822837

-1.366019034

4.4745652

BENEFITS

0.664006

0.260871

2.545343

0.0180849

0.124353418

1.2036581

Equation 1:

INTRINSIC = 1.554273 + 0.664 BENEFITS

T critical value at the 95% level of test is 2.06; comparing this with the t statistics value it is evident that the slope is significant while the intercept is not significant. (Ken, 2009)

Part 2:

BENEFITS and EXTRINSIC as independent and dependent variable respectively: (equation 2)

The following is a summary of the results:

Regression Statistics

Multiple R

0.34268

R Square

0.11743

Adjusted R Square

0.079057

Standard Error

1.016694

Observations

25

ANOVA

df

SS

MS

F

Significance F

Regression

1

3.163276

3.163276

3.0602491

0.093563229

Residual

23

23.77432

1.033666

Total

24

26.9376

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

7.404902

1.539139

4.811069

7.457E-05

4.220951367

10.588853

BENEFITS

-0.49756

0.284424

-1.74936

0.0935632

-1.085933127

0.0908167

Equation 2:

EXTRINSIC = 7.404902-0.49756 BENEFITS

T critical value at the 95% level of test is + or - 2.06, comparing this with the t statistics value it is evident that the slope is not significant while the intercept is significant. (Ken, 2009)

Part 3:

BENEFITS and OVERALL as independent and dependent variable respectively:

(Equation 3)

The following is a summary of the results:

Regression Statistics

Multiple R

0.140186

R Square

0.019652

Adjusted R Square

-0.02297

Standard Error

0.990779

Observations

25

ANOVA

df

SS

MS

F

Significance F

Regression

1

0.452594

0.452594

0.4610571

0.503906493

Residual

23

22.57781

0.981644

Total

24

23.0304

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

4.162472

1.499908

2.775152

0.0107666

1.059676321

7.2652673

BENEFITS

0.188204

0.277174

0.679012

0.5039065

-0.385173504

0.7615822

Equation 3:

OVERALL = 4.1625 + 0.1882 BENEFITS

T critical value at the 95% level of test is + or - 2.06, comparing this with the t statistics value it is evident that the slope is not significant while the intercept is significant. (Ken, 2009)

Part 4:

Graphs and Regression line equations:

Equation 1:

INTRINSIC = 1.554273 + 0.664 BENEFITS

The chart below summarizes the results for equation 1:

From the chart there is a positive relationship between the two variables, when benefit increase then Intrinsic increases.

Slope and intercept:

The slope of the regression model is 0.664 and the Y intercept value is 1.554273. This means if benefits increase by one unit then intrinsic will increase by 0.664 units. (Bowerman, 2003)

R squared:

The R squared value for this model is 0.219778, meaning benefits explain 21.98% of changes in Intrinsic variable. (Bowerman, 2003)

Equation 2:

EXTRINSIC = 7.404902-0.49756 BENEFITS

The chart below summarizes the results for equation 2:

From the chart there is an inverse relationship between the two variables, when benefit increase then extrinsic decreases.

Slope and intercept:

The slope of the regression model is -0.49756 and the Y intercept value is 7.4. This means if benefits increase by one unit then extrinsic will increase by -0.49756 units. (Bowerman, 2003)

R squared:

The R squared value for this model is 0.11743, meaning benefits explain 11.74% of changes in extrinsic variable. (Bowerman, 2003)

Equation 3:

OVERALL = 4.1625 + 0.1882 BENEFITS

The chart below summarizes the results for equation 3:

From the chart there is a positive relationship between the two variables, when benefit increase then overall increases.

Slope and intercept:

The slope of the regression model is 0.1882 and the Y intercept value is 4.1625. This means if benefits increase by one unit then overall will increase by 0.1882 units. (Bowerman, 2003)

R squared:

The R squared value for this model is 0.019652, meaning benefits explain 1.97% of changes in overall variable. (Bowerman, 2003)

Comment:

The first equation INTRINSIC = 1.554273 + 0.664 BENEFITS produces the strongest relationship, this model has the highest slope value showing a strong relationship between the variables, the R squared value is 0.2198 which is the highest value and therefore this shows the strongest correlation coefficient. The slope of this model is statistically significant while none of the other models value are significant, the second modelEXTRINSIC = 7.404902-0.49756 BENEFIT has a negative slope and from the other models benefits increases satisfaction, therefore this model will not be appropriate in estimating satisfaction values.

Refereences:

Bruce Bowerman (2003) Business statistics in practice‎, New Jersey: Prentice Hall press

Ken Black (2009) Business Statistics: Contemporary Decision Making, New York: McGraw hill press

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