Posted: January 11th, 2023

Some commonly employed statistical analyses include correlation and regression

Module 5 Problem SetModule 5 Problem SetDue Date: May 04, 2016 23:59:59Max Points: 85Details:Some commonly employed statistical analyses include correlation and regression. In thisassignment, 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 thisassignment.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 theassignment.Download the file "Census.sav" and open it with SPSS. Use the data to complete theassignment.Directions:Locate the data set "Bank.sav" and open it with SPSS. Follow the steps in section 10.15 LearningActivity as written. Answer all of the questions in the activity based on your observations of theSPSS output. Type your answers into a Word document and include supporting graphs or tablesfrom the SPSS output for submission to the instructor.Locate the data set "Census.sav" and open it with SPSS. Follow the steps in section 11.16Learning Activity as written. Answer all of the questions in the activity based on yourobservations of the SPSS output. Type your answers into a Word document and includesupporting graphs or tables from the SPSS output for submission to the instructor.RES865.7P5CL_introduction_to_statistical_analysis_using_ibm_spss_statistics.pdf10.15 Learning ActivityThe overall goal of this learning activity is to visualize the relationship between two scalevariables creating scatterplots and to quantify this relationship with the correlation coefficient. Inthis 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 majorbank. Included is data on beginning and current salary position, time working, and demographicinformation.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 byproducing scatterplots of salbeg, sex, time, age, edlevel, and work with salnow. Add a fit line toeach plot. Check on the variable labels for time and work so you understand what these variablesare measuring.2. Describe the relationships based on the scatterplots. Do they all appear to be linear? Are anyrelationships negative? What is the strongest relationship?3. Now produce correlations with all these variables. Which correlations with salnow aresignificant? What is the largest correlation in absolute value with salnow? Did this match whatyou thought based on the scatterplots?4. Examine the correlations between the other variables? Which variables are most stronglyrelated? 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 anyemployees who are outliers—far from the fit line—in any of the scatterplots? How might they beaffecting the relationship?11.16 Learning ActivityThe 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 adultpopulation. Questions were included about various attitudes and demographic characteristics.Supporting MaterialsFor 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 ofeducation (educ). First, do a scatterplot of these two variables and superimpose a fit line. Doesthe relationship seem linear? How would you characterize the relationship?2. Now run the linear regression. What is the Adjusted R square value? Is the regressionsignificant? 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 brothersand sisters (sibs). Check the coding on born so you can interpret its coefficient. First, do ascatterplot of age and sibs with income06. Superimpose a fit line. Does the relationship seemlinear? How would you characterize the relationship? Why not do scatterplots of income06 withsex and born?4. Use all these variables to predict income06. Request residual statistics including the histogramof errors and the scatterplot of standardized values. Also request casewise diagnostics. What isthe Adjusted R square? How much has it increased from above?5. Which variables are significant predictors? What is the effect of each on income06? Whichvariable is the strongest predictor? The weakest?6. Examine the casewise diagnostics. Do you see any pattern? Are there more cases with largeerrors than we would expect?7. Examine the histogram and scatterplot. Are the errors normally distributed? Do you see anypattern 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 variablesthat are at least on an interval scale of measurement. Repeat the exercise above. Are the newvariables significant predictors? Does adding variables change the effects of the variables alreadyin the model from above?

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