Module 5 Problem Set

Module 5 Problem Set

Due Date: May 04, 2016 23:59:59

Max Points:85

Details:

Some commonly employed statistical

analyses include correlation and regression. In this assignment, you will

practice correlation and regression techniques from an SPSS data set.

General

Requirements:

Use the following information to

ensure successful completion of the assignment:

Review “SPSS Access

Instructions” for information on how to access SPSS for this

assignment.

Access the document, “Introduction

to Statistical Analysis Using IBM SPSS Statistics, Student Guide” to

complete the assignment.

Download the file

“Bank.sav” and open it with SPSS. Use the data to complete the

assignment.

Download the file

“Census.sav” and open it with SPSS. Use the data to complete the

assignment.

Directions:

Locate the data set

“Bank.sav” and open it with SPSS. Follow the steps in section 10.15

Learning Activity as written. Answer all of the questions in the activity based

on your observations of the SPSS output. Type your answers into a Word document

and include supporting graphs or tables from the SPSS output for submission to

the instructor.

Locate the data set

“Census.sav” and open it with SPSS. Follow the steps in section 11.16

Learning Activity as written. Answer all of the questions in the activity based

on your observations of the SPSS output. Type your answers into a Word document

and include supporting graphs or tables from the SPSS output for submission to

the instructor.

.7P5CL_introduction_to_statistical_analysis_using_ibm_spss_statistics.pdf” href=”https://lc-grad2.gcu.edu/learningPlatform/content/content.html?operation=viewContent&contentId=bd5dbc00-99f0-406e-9e14-b6e6c08e3c9c”>RES865.7P5CL_introduction_to_statistical_analysis_using_ibm_spss_statistics.pdf

10.15 Learning Activity

The overall goal of this learning

activity is to visualize the relationship between two scale variables creating

scatterplots and to quantify this relationship with the correlation

coefficient. In this set of learning activities you will use the data file Bank.sav.

The file Bank.sav, a PASW Statistics

data file that contains information on employees of a major bank. Included is

data on beginning and current salary position, time working, and demographic

information.

1. Suppose you are interested in

understanding how an employees demographic characteristics, beginning salary,

and time at the bank and in the work force are related to current salary. Start

by producing scatterplots of salbeg, sex, time, age, edlevel, and work with

salnow. Add a fit line to each plot. Check on the variable labels for time and

work so you understand what these variables are measuring.

2. Describe the relationships based

on the scatterplots. Do they all appear to be linear? Are any relationships

negative? What is the strongest relationship?

3. Now produce correlations with all

these variables. Which correlations with salnow are significant? What is the

largest correlation in absolute value with salnow? Did this match what you

thought based on the scatterplots?

4. Examine the correlations between

the other variables? Which variables are most strongly related? Create

scatterplots for these as well to check for linearity.

5. For those with more time: Go back

and review the scatterplots with salnow. Are there any employees who are

outliersâfar from the fit lineâin any of the scatterplots? How might they be

affecting the relationship?

11.16

Learning Activity

The overall goal of this learning

activity is to run linear regressions and to interpret the output. You will use

the PASW Statistics data file Census.sav.

The file Census.sav, a PASW

Statistics data file from a survey done on the general adult population.

Questions were included about various attitudes and demographic

characteristics.

Supporting Materials

For additional information on linear

regression analysis, see:

Allison, Paul D. 1998. Multiple

Regression: A Primer. Thousand Oaks, CA: Pine Forge.

Draper, Norman and Smith, Harry.

1998. Applied Regression Analysis. 3rd ed. New York: Wiley.

1. Run a linear regression to

predict total family income (income06) with highest year of education (educ).

First, do a scatterplot of these two variables and superimpose a fit line. Does

the relationship seem linear? How would you characterize the relationship?

2. Now run the linear regression.

What is the Adjusted R square value? Is the regression significant? What is the

B coefficient for educ? Interpret it.

3. Next add the variablesborn(born

in the U.S. or overseas),age,sex, and number of brothers and

sisters (sibs). Check the coding onbornso you can interpret its

coefficient. First, do a scatterplot ofageandsibswithincome06.

Superimpose a fit line. Does the relationship seem linear? How would you

characterize the relationship? Why not do scatterplots ofincome06withsex

andborn?

4. Use all these variables to

predictincome06. Request residual statistics including the histogram of

errors and the scatterplot of standardized values. Also request casewise

diagnostics. What is the Adjusted R square? How much has it increased from

above?

5. Which variables are significant

predictors? What is the effect of each onincome06? Which variable is

the strongest predictor? The weakest?

6. Examine the casewise diagnostics.

Do you see any pattern? Are there more cases with large errors than we would

expect?

7. Examine the histogram and

scatterplot. Are the errors normally distributed? Do you see any pattern in the

scatterplot? What might that mean?

8. What is the prediction equation

forincome06?

9.For those with more time:Add

additional variables to the regression equation forincome06. Examples

are father and motherâs education, or number of children. Be careful to add

variables that are at least on an interval scale of measurement. Repeat the

exercise above. Are the new variables significant predictors? Does adding

variables change the effects of the variables already in the model from above?

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