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1. Introduction to Statistics for the Social SciencesSBS200, COMM200, GEOG200, PA200, POL200, SOC200Lecture Section 001, Fall, 2011Room 201 Physics-Atmospheric Sciences (PAS)10:00 - 10:50 Mondays & Wednesdays + Lab Session Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

2. Use this as your study guide By the end of lecture today9/12/11 Process of Peer Review Descriptive vs inferential analyses Time series design vs. Cross sectional design Correlational methodology

3. Homework due - (September 14th) On class website: please print and complete homework worksheet #3 Please double check – Allcell phones other electronic devices are turned off and stowed away

4. Schedule of readings • Before next exam: • Please read chapters 1 - 4 & • Appendix D & E in Lind Please read Chapters 1, 5, 6 and 13 in Plous • Chapter 1: Selective Perception Chapter 5: Plasticity Chapter 6: Effects of Question Wording and Framing Chapter 13: Anchoring and Adjustment

5. Exam 1 – One week from today – Monday (9/19/11) Study guide will be online before Wednesday Bring 2 calculators (remember only simple calculators,we can’t use calculators with programming functions) Bring 2 pencils (with good erasers) Bring ID

6. Review of Homework Worksheet Must be complete and must be stapled

7. Peer review Please exchange questionnaires with someone and complete the peer review handed out in class You have 10 minutes Peer review is an important skill in nearly all areas of business and science. Please strive to provide productive, useful and kind feedback as you complete your peer review

8. Review of Homework Worksheet Hand in the peer review with the questionnaire *Hand them in together*

9. Descriptive vs inferential statistics Descriptive statistics - organizing and summarizing data Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected

10. Descriptive or inferential? Descriptive statistics - organizing and summarizing data Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected What is the average height of the basketball team? Measured all of the players and reported the average height Measured only a sample of the players and reported the average height for team In this class, percentage of students who support the death penalty? Measured all of the students in class and reported percentage who said “yes” Measured only a sample of the students in class and reported percentage who said “yes” Based on the data collected from the students in this class we can conclude that 60% of the students at this university support the death penalty Measured all of the students in class and reported percentage who said “yes”

11. Descriptive or inferential? Descriptive statistics - organizing and summarizing data Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected Men are in general taller than women Measured all of the citizens of Arizona and reported heights Shoe size is not a good predictor of intelligence Measured all of the shoe sizes and IQ of students of 20 universities Blondes have more fun Asked 500 actresses to complete a happiness survey The average age of students at the U of A is 21 Asked all students in the fraternities and sororities their age

12. Descriptive vs inferential statistics Descriptive statistics - organizing and summarizing data Inferential statistics - generalizing beyond actual observations making “inferences” based on data collected To determine this we have to consider the methodologies used in collecting the data

13. Simple Frequency Table – Qualitative Data We asked 100 Republicans “Who is your favorite candidate?” • Number expected to vote • 6,380,000 • 3,740,000 • 2,860,000 • 2,200,000 • 880,000 • 880,000 • 5,060,000 Who is your favorite candidate Candidate Frequency Rick Perry 29 Mitt Romney 17 Ron Paul 13 Michelle Bachman 10 Herman Cain 4 Newt Gingrich 4 No preference 23 Relative Frequency .2900 .1700 .1300 .1000 .0400 .0400 .2300 • Percent • 29% • 17% • 13% • 10% • 4% • 4% • 23% If 22 million Republicans voted today how many would vote for each candidate? Just divide each frequency by total number Just multiply each relative frequency by 22 million Just multiply each relative frequency by 100 Please note: 29 /100 = .2900 17 /100 = .1700 13 /100 = .1300 4 /100 = .0400 Please note: .2900 x 22m = 6,380k .1700 x 22m = 3,740k .1300 x 22m = 2,860k .0400 x 22m= 880k Please note: .2900 x 100 = 29% .1700 x 100 = 17% .1300 x 100 = 13% .0400 x 100 = 4% Data based on Gallup poll on 8/24/11

18. Designed our study / observation / questionnaire Collected our data Organize and present our results

19. Scatterplot displays relationships between two continuous variables Correlation: Measure of how two variables co-occur and also can be used for prediction Range between -1 and +1 The closer to zero the weaker the relationship and the worse the prediction Positive or negative

20. Correlation Range between -1 and +1 +1.00 perfect relationship = perfect predictor +0.80 strong relationship = good predictor +0.20 weak relationship = poor predictor 0 no relationship = very poor predictor -0.20 weak relationship = poor predictor -0.80 strong relationship = good predictor -1.00 perfect relationship = perfect predictor

21. Positive correlation: as values on one variable go up, so do values for the other variable Negative correlation: as values on one variable go up, the values for the other variable go down Height of Mothers by Height of Daughters Height ofMothers Positive Correlation Height of Daughters

22. Positive correlation: as values on one variable go up, so do values for the other variable Negative correlation: as values on one variable go up, the values for the other variable go down Brushing teeth by number cavities BrushingTeeth Negative Correlation NumberCavities

23. Perfect correlation = +1.00 or -1.00 One variable perfectly predicts the other Height in inches and height in feet Speed (mph) and time to finish race Positive correlation Negative correlation

24. Correlation The more closely the dots approximate a straight line,(the less spread out they are) the stronger the relationship is. Perfect correlation = +1.00 or -1.00 One variable perfectly predicts the other No variability in the scatterplot The dots approximate a straight line

25. Correlation

26. Correlation does not imply causation Is it possible that they are causally related? Yes, but the correlational analysis does not answer that question What if it’s a perfect correlation – isn’t that causal? No, it feels more compelling, but is neutral about causality Number of Birthdays Number of Birthday Cakes

27. Positive correlation: as values on one variable go up, so do values for other variable Negative correlation: as values on one variable go up, the values for other variable go down Number of bathrooms in a city and number of crimes committed Positive correlation Positive correlation

28. Linear vs curvilinear relationship Linear relationship is a relationship that can be described best with a straight line Curvilinear relationship is a relationship that can be described best with a curved line

29. Correlation - How do numerical values change? http://neyman.stat.uiuc.edu/~stat100/cuwu/Games.html http://argyll.epsb.ca/jreed/math9/strand4/scatterPlot.htm Let’s estimate the correlation coefficient for each of the following r = +.80 r = +1.0 r = -1.0 r = -.50 r = 0.0

30. This shows the strong positive (.8) relationship between the heights of daughters (measured in inches) with heights of their mothers (measured in inches). 48 52 5660 64 68 72 Both axes and values are labeled Both axes and values are labeled Both variables are listed, as are direction and strength Height of Mothers (in) 48 52 56 60 64 68 72 76 Height of Daughters (inches)

31. Thank you! See you next time!!