Reference no: EM131319937

Module 5 Problem Set

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.

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 outliers-far from the fit line-in 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 variables born (born in the U.S. or overseas), age, sex, and number of brothers and sisters (sibs). Check the coding on born so you can interpret its coefficient. First, do a scatterplot of age and sibs with income06. Superimpose a fit line. Does the relationship seem linear? How would you characterize the relationship? Why not do scatterplots of income06 with sex and born?

4. Use all these variables to predict income06. 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 on income06? 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 for income06?

9. For those with more time: Add additional variables to the regression equation for income06. Examples are father and mother's 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?