1. Examples of Presentations  The following are examples of presentations of regression tables and their interpretations.  These interpretations target a “nonacademic” audience, and would be appropriate for presenting information to those not familiar with statistics.  These examples assume presenters have time constraints, and audiences have limited knowledge about regression analysis. The interpretations are succinct and simplified.  Please note. Slides are to be used only as simplified examples of how regression can be used and interpreted. They are not research presentations.

2. Example One Hypothesis: Regression How people feel about women becoming CEOs is impacted by the extent to which they were nontraditional in their political and gender role views.

3. Example One Measurement of Indicators Attitudes toward female CEOs was measured by using a statement to which respondents indicated their level of agreement. Gender role ideology was measured by asking respondents to (1) rank their political views from very conservative to very liberal, (2) indicate the extent to which they were independent from their parents, and (3) compare the value of career relative to family.

4. Regression: Support Women as CEOs by Political Views, Independence from Family, and Valuing Job Over their Family ____________________________________________________________ Independent Parameter Standardized Variable Estimate Estimate Political view.73**.31** Independence.63*.24* Job Over Family.24**.25** Intercept.00 7.31 F statistic 10.42*** R square 0.25 Adjusted R square 0.22 ______________________________________________________________ *p<.01 **p<.05 ***p<.001 ______________________________________________________________

5. FINDINGS: Regression Respondents who were (1) more liberal in their political views, (2) more likely to view themselves as independent thinkers, and (3) put a greater emphasis on work relative to family were more likely to believe women should seek CEO positions.. The R-square was.24 indicating a moderately strong relationship between the independent variables and the dependent variable. Political views had the biggest impact on the attitude toward women as CEOs (with a standardized estimate of.31).

6. Attitudes Toward The Death Penalty Presentation: Tandi McBride Modifications: Carol Albrecht

7. Example Two Hypothesis: Regression  The more traditional a student’s views, the greater their support for the death penalty.  Views on the perceived fairness of the current legal system, religious attendance, and political ideology are related to the extent to which respondents support the death penalty.

8. Example Two Measurement of Indicators  Students were asked to indicate the extent to which they agreed with the death penalty.  Students were asked to indicate the extent to which they agreed the current legal system is fair.  Students were asked to indicate the number of times per year they attended a religious service.  Students were asked to rank their political views from very conservative to very liberal.

9. Findings Regression Table Table 5 Regression: Support for the Death Penalty by Religious Attendance, Political Ideology, and Support for the Current Legal System. Table 5 Regression: Support for the Death Penalty by Religious Attendance, Political Ideology, and Support for the Current Legal System.  ___________________________________________________________________   Independent Variables Parameter Estimate Standardized Estimate ____________________________________________________________________ ____________________________________________________________________  Religious Attendance.01*.20*  Political Views -.19 -.11  Perceive Legal System as Fair.47***.45***   Intercept 0.00 5.36  F statistic 14.14***  R square.27  Adjusted R square.25 *p<.05 *p<.05***p<.001

10. Conclusions  As reported in Table 5, there is a significant relationship between the independent variables and the dependent variable – attitudes toward the death penalty.  Religious attendance was positively and significantly related to attitudes toward the death penalty although the relationship was not strong. Political ideology was not significantly related to death penalty when controlling for religious attendance and attitudes toward the current legal system. Perception that the current legal system is fair was strongly and positively related to attitudes toward the death penalty.

11. Attitudes on President Bush and the War in Iraq Constructed by Leesa Pettus Modified by Dr. Carol Albrecht

12. Example Three Hypothesis: Regression Political ideology, gender, and religious attendance have an effect on attitudes toward US involvement in war. Political ideology, gender, and religious attendance have an effect on attitudes toward US involvement in war.

13. Regression Table Table 2 Pro-War Attitudes by Political Affiliation, Gender and Religiosity Independent Variables Standardized Estimate Parameter Estimate Independent Variables Standardized Estimate Parameter Estimate Political Affiliation -0.76***-1.51*** Political Affiliation -0.76***-1.51*** Gender-0.07-0.47 Gender-0.07-0.47 Religiosity 0.01 0.00 Religiosity 0.01 0.00 Intercept 0.00 16.22 Intercept 0.00 16.22 F Statistic 47.92*** F Statistic 47.92*** R Square0.60 R Square0.60 Adjusted R Square0.59 Adjusted R Square0.59 ***p<.0001 ***p<.0001

14. Regression Analysis Political affiliation was my only significant independent variable and has a probability <.0001 suggesting a strong relationship between this variable and attitudes toward war. Political affiliation was my only significant independent variable and has a probability <.0001 suggesting a strong relationship between this variable and attitudes toward war. My F statistic is also significant with a probability <.0001 indicating that I can generalize my findings to the population. My F statistic is also significant with a probability <.0001 indicating that I can generalize my findings to the population. My R2 shows that I can increase my ability to predict the index score on pro-war by.60 (60%) if I know a person’s religious attendance, political affiliation and gender. My R2 shows that I can increase my ability to predict the index score on pro-war by.60 (60%) if I know a person’s religious attendance, political affiliation and gender. My parameter estimate for political affiliation shows that for every one point increase on the political affiliation index, there is a 1.5 point decrease on the pro-war index. As individuals become more liberal in their political affiliation, they are less “pro-war”. My parameter estimate for political affiliation shows that for every one point increase on the political affiliation index, there is a 1.5 point decrease on the pro-war index. As individuals become more liberal in their political affiliation, they are less “pro-war”.

15. Factors Influencing High School Students’ Perception of Current and Future Education. Constructed by Lorena Garcia Modified by Dr. Carol Albrecht

16. Example Four Hypothesis: Regression Race, predicted grade and perception of relationship with teacher is related to satisfaction with overall academic success.

17. Example Four Measurement of Indicators Respondents indicated whether or not they were Latino Respondents predicted their final grade in the class. Respondents reported the level to which they agreed that they had a good relationship with the teacher. Respondent reported their level of satisfaction with their academic success..

18. Satisfaction with Academic Performance by Race, Relationship with Teacher and Final Grade Independent ParameterStandardized Variables Estimate Estimate Race -0.14 -.04 Teacher Relationships 0.75***.44*** Final Grade in Class 0.23**.13** Intercept 3.43 0 F-Statistic 22.29*** R-Square 0.24 Adjusted R 0.23 **p<.001 ***p<.0001

19. In Conclusion… For my Regression model, I was able to reject the null hypothesis (with an F value of 22.29 and p<.0001),thus indicating that at least one of my independent variables was related to my dependent variable (satisfaction). Race was not related to satisfaction. Prediction of final grade and perception of relationship with the teacher were related to satisfaction. Students who predicted a higher grade and reported a more positive relationship with their teacher were more likely to report that they were satisfied.

20. Attitudes Towards the Legalization of Marijuana Attitudes Towards the Legalization of Marijuana Constructed by Chelsea Moore Modified by Dr. Carol Albrecht

21. Example Five Hypothesis: Regression There is a significant relationship between the index score measuring the degree of support for keeping marijuana illegal and at least one of the following independent variables: student’s political affiliation, the number of times a student skips class per month, and the students’ gender. There is a significant relationship between the index score measuring the degree of support for keeping marijuana illegal and at least one of the following independent variables: student’s political affiliation, the number of times a student skips class per month, and the students’ gender.

22. Example Five Measurement of Indicators Respondents were asked to indicate their gender. Respondents were asked to indicate their gender. Respondents were asked to report the number of times they skipped class in a “typical” month. Respondents were asked to report the number of times they skipped class in a “typical” month. Respondents were asked to rank their political ideology from very conservative to very liberal. Respondents were asked to rank their political ideology from very conservative to very liberal. Respondents were asked to indicate the extent to which they supported laws making possession of marijuana illegal. Respondents were asked to indicate the extent to which they supported laws making possession of marijuana illegal.

23. Regression Table Table 2. Regression: Degree of Support for Keeping Marijuana Illegal by Political Affiliation, the Number of Times a Student Skips Class Per Month, and Gender IndependentParameter EstimateStandardized Estimate Political Affiliation3.10***0.42*** Classes Skipped (per month)-0.78*-0.26* Gender Intercept 2.63 11.13 0.16 F Statistic R Square Adjusted R Square *p<0.05 ***p<0.001 14.22*** 0.32 0.30

24. Conclusions The R Square is 0.32 indicating a moderate to strong relationship. The R Square is 0.32 indicating a moderate to strong relationship. The F statistic is 14.22 and the probability is less than 0.001, so we can reject the null hypothesis. At least one of the independent variables is related to the dependent variable. The F statistic is 14.22 and the probability is less than 0.001, so we can reject the null hypothesis. At least one of the independent variables is related to the dependent variable. When looking at the independent variables individually, we see that: When looking at the independent variables individually, we see that: –Gender is NOT significantly related to marijuana legalization. –The more conservative a student’s political ideology, the more support for keeping marijuana illegal. Political ideology has the greatest impact on attitudes toward marijuana. –The more often students skip class, the less supportive of keeping marijuana illegal.

25. For More Information, Contact: Dr. Carol Albrecht –Utah State Extension Assessment Specialist –(979) 777-2421 –carol.albrecht@usu.edu