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Adjusted R-Square, Degrees of Freedom, Dummy Variables, Intercept Outliers | PSY 395, Assignments of Psychology

Material Type: Assignment; Class: Computer Data Analysis; Subject: Psychology; University: University of La Verne; Term: Unknown 1989;

Typology: Assignments

Pre 2010

Uploaded on 08/19/2009

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PSY/BHV 395 Name:
Chapter 7 In-class assignment
Define the following terms:
Adjusted R-Square Degrees of Freedom Dummy Variables Intercept
Outliers Residuals Spurious Factors
To create a bivariate linear regression:
1. Open the STATES.sav data file
a. Click “Analyze”
i. Choose “Regression”
ii. Select “Linear”
1. in the “Dependent” box, select the variable “BIH29” (teenage
birth rate in 1997)
2. in the “Independent(s)” box, select the variable “PVS500”
(Poverty Rate 1998)
iii. Click “OK”
Examine your output:
1. What is the Unstandardized Coefficient? What does it mean?
2. Is the relationship statistically significant?
3. What is the R-Square value? What does this tell you?
To graph the regression line using the output from our analyses of the linear regression:
1. Open the STATES.sav data file
a. Click on “Transform” and then “Compute”
b. Input as the “Target Variable” a new variable called “BIRTHPRE”
c. In the “Numeric Expression” box, enter the equation: (15.160 +2.735 *) and
after the multiplication sign, select the variable “PVS500”
d. Click on the “Type and Label” box and label the variable “BIRTHPRE” as
“Predicted Teen Birth Rate From Poverty Rates”
e. Click “Continue”
f. Click “OK”
To plot this line:
1. Click on “Graphs” and select “Scatter”
a. Choose “Simple”
b. Click on “Define”
i. For the “Y Axis”, select the variable BIH29
ii. For the “X Axis”, select the variable PVS500
c. Double click the graph when it is generated
i. Click on “Chart”
1. Click on “Options”
a. In the “Fit Line” area, choose “Total”
2. Click “OK”
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Download Adjusted R-Square, Degrees of Freedom, Dummy Variables, Intercept Outliers | PSY 395 and more Assignments Psychology in PDF only on Docsity!

PSY/BHV 395 Name: Chapter 7 In-class assignment Define the following terms:

Adjusted R-Square Degrees of Freedom Dummy Variables Intercept

Outliers Residuals Spurious Factors

To create a bivariate linear regression:

  1. Open the STATES.sav data file a. Click “Analyze” i. Choose “Regression” ii. Select “Linear”
  2. in the “Dependent” box, select the variable “BIH29” (teenage birth rate in 1997)
  3. in the “Independent(s)” box, select the variable “PVS500” (Poverty Rate 1998) iii. Click “OK” Examine your output:
  4. What is the Unstandardized Coefficient? What does it mean?
  5. Is the relationship statistically significant?
  6. What is the R-Square value? What does this tell you?

To graph the regression line using the output from our analyses of the linear regression:

  1. Open the STATES.sav data file a. Click on “Transform” and then “Compute” b. Input as the “Target Variable” a new variable called “BIRTHPRE” c. In the “Numeric Expression” box, enter the equation: (15.160 +2.735 *) and after the multiplication sign, select the variable “PVS500” d. Click on the “Type and Label” box and label the variable “BIRTHPRE” as “Predicted Teen Birth Rate From Poverty Rates” e. Click “Continue” f. Click “OK” To plot this line:
  2. Click on “Graphs” and select “Scatter” a. Choose “Simple” b. Click on “Define” i. For the “Y Axis”, select the variable BIH ii. For the “X Axis”, select the variable PVS c. Double click the graph when it is generated i. Click on “Chart”
  3. Click on “Options” a. In the “Fit Line” area, choose “Total”
  4. Click “OK”

PSY/BHV 395 Name: Chapter 7 In-class assignment Hypothesis 1: Teen birth rate is positively associated with poverty. IV: PVS Hypothesis 2: Teen birth rate is negatively associated with expenditures per pupil. IV: SCS Hypothesis 3: Teen birth rate is positively associated with the unemployment rate. IV EMS Hypothesis 4: Teen birth rate is positively associated with the amount of “temporary assistance To needy family” a family receives. IV: PVS

To test the hypotheses (Hypotheses 1 to 4) stated on page 139:

  1. Click on “Analyze”
  2. Choose “Regression” a. Select “Linear” i. In the “Dependent” box, select the variable “BIH29 (births to teens/1000 1997) ii. In the “Independents” box, select the following variables
  3. PVS500 (Poverty rate 1998)
  4. SCS141 (Expenditures per Pupil in 1999)
  5. EMS171 (Unemployment Rate in 1999)
  6. PVS526 (Maximum Monthly TANF benefit for Family of Three in 1999) b. Click “OK”
  7. Examine the output and see if the hypotheses are supported.

The hypothesis that teen birth rate is positively associated with poverty was supported. Multiple linear regression analysis showed that there is a significant positive relationship, such that states with higher pove rty rates will have higher teen births.

The hypothesis that teen birth rate is negatively associated with expenditures per pupil was not supported. Multiple linear regression analysis showed that there is no significant relationship between these two variables.

The hypothesis that high unemployment rates are related to high teen birth rates is not supported. Multiple linear regression analysis showed that there is no significant relationship between these two variables.

The hypothesis that the more a state spends on a needy family the higher the teen birth rate was not supported, however, there was a significant relationship between these two variables as indicated by a multiple linear regression analysis. In this sample, the more a state spent on a needy family, the lower the teen birth rate.

How good is the model of the 4 independent variables and their effect on the dependent variable? Adjusted R-Square = .594 or 59.4% variation in the teen birth rate can be explained by the four variables.