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Assignment 9: Excel & SPSS Linear Regression Analysis

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#### 1.1. Simple Regression in Excel 2010, 2013, & 2016

To get the Data Analysis tool, first click on File > Options > Add-Ins > Go > Select Data Analysis Toolpack & Toolpack VBA. Data Analysis is now available under Excel’s Data tab. Open the Excel Worksheet GPAvsGMAT.xls and select Data Analysis > Regression. Then fill out the popup window as shown below, specifying GPA as the Y variable and GMAT as the X variable:

This will produce the output:

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.8086001

R Square 0.6538342 Adjusted R Square 0.6346027 Standard Error 0.4350142

Observations 20

ANOVA

df SS MS F Significance F

Regression 1 6.43372807 6.43373 33.9982 1.59668E-05

Residual 18 3.40627193 0.18924

Total 19 9.84

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept -1.699561 0.72677682 -2.3385 0.0311 -3.22646284 -0.17266 -3.2264628 -0.17265997

GMAT 0.0083991 0.001440476 5.8308 1.6E-05 0.005372795 0.011425 0.0053728 0.01142545

### 1.1b. Simple Regression in Excel 2007

To get the Data Analysis tool in Excel 2007, click on the Office button (top left corner), and select Excell options > Add-Ins > Go > Select Data Analysis Toolpack & Toolpack VBA. Data Analysis is now available under Excel’s Data tab.

#### 1.1c. Simple Regression in Excel 2003

To get the Data Analysis tool in Excel 2003, select Tools > Add-Ins > Select Data Analysis Toolpack & Toolpack VBA. Regression analysis is then available under Tools > Data Analysis

> Regression.

1.2 Repeat these steps for the data of IQvsGPA.xls.

1.2.1 Provide your output as shown above. (12.5 points)

## 2. Simple Regression in IBM SPSS.

2.1 Start IBM SPSS Statistics 21, available from the Statistics menu of the standard COB PC configuration. Select File > Open > Data. Select to see Files of type: Excel. Open GPAvsGMAT.xls. Confirm that variable names should be read from the first row of data.

2.1.1 Select Analyze > Regression > Linear. Specify Dependent=GPA, Independent=GMAT.

Select OK.

This will produce the following output:

Regression

Variables Entered/Removed b

 Model Variables Entered Variables Removed Method 1 GMAT a . Enter

a. All requested variables entered.

b. Dependent Variable: GPA

Model Summary

 Model R R Square Adjusted R Square Std. Error of the Estimate 1 .809 a .654 .635 .4350

a. Predictors: (Constant), GMAT

ANOVAb

 Model Sum of Squares df Mean Square F Sig. 1 Regression 6.434 1 6.434 33.998 .000(a) Residual 3.406 18 .189 Total 9.840 19

a. Predictors: (Constant), GMAT

b. Dependent Variable: GPA

Coefficientsa

 Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) GMAT -1.700 .008 .727 .001 .809 -2.338 5.831 .031 .000

a. Dependent Variable: GPA

2.2. Repeat these steps for the data of IQvsGPA.xls. Provide your output as shown above. (12.5 points)

### You have to provide the same screenshots and results as in the examples that I provided.

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