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Lecturer’s desk Projection Booth Screen Screen Harvill 150 renumbered

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1 Lecturer’s desk Projection Booth Screen Screen Harvill 150 renumbered
Row A 15 14 Row A 13 3 2 1 Row A Row B 23 20 Row B 19 5 4 3 2 1 Row B Row C 25 21 Row C 20 6 5 1 Row C Row D 29 23 Row D 22 8 7 1 Row D Row E 31 23 Row E 23 9 8 1 Row E Row F 35 26 Row F 25 11 10 1 Row F Row G 35 26 Row G 25 11 10 1 Row G Row H 37 28 27 13 Row H 12 1 Row H 41 29 28 14 Row J 13 1 Row J 41 29 Row K 28 14 13 1 Row K Row L 33 25 Row L 24 10 9 1 Row L Row M 21 20 19 Row M 18 4 3 2 1 Row M Row N 15 1 Row P 15 1 Harvill 150 renumbered table 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Projection Booth Left handed desk

2 Kristina Lecturer’s desk Attila Sezen Hannah Michelle Projection Booth
Screen Screen Kristina Lecturer’s desk Row A 15 14 Row A 13 3 2 1 Row A Attila Row B 23 20 Row B 19 5 4 3 2 1 Row B Row C 25 21 Row C 20 6 5 1 Row C Row D 29 23 Row D 22 8 7 1 Row D Row E 31 23 Row E 23 9 8 1 Row E Michelle Row F 35 26 Row F 25 11 10 1 Row F Row G 35 26 Row G 25 11 10 1 Row G Row H 37 28 27 13 Row H 12 1 Row H 41 29 28 14 1 Row J Row J 13 41 29 Row K 28 14 13 1 Row K Row L 33 25 Row L 24 10 9 1 Row L Row M 21 20 19 Row M 18 4 3 2 1 Row M Row N 15 1 Row P 15 1 Sezen Hannah table 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Projection Booth Left handed desk Harvill 150 renumbered

3 Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Spring 2018 Room 150 Harvill Building 9:00 - 9:50 Mondays, Wednesdays & Fridays. Welcome 1/29/18

4 Lab sessions Labs continue this week Everyone will want to be enrolled
in one of the lab sessions Labs continue this week

5

6

7 writing assignment forms notebook and clickers to each lecture
Remember bring your writing assignment forms notebook and clickers to each lecture

8 In nearly every class we will use clickers to
answer questions in class and participate in interactive class demonstrations Even if you have not yet registered your clicker you can still participate

9 Schedule of readings Before next exam (February 9)
Please read chapters in OpenStax textbook Please read Appendix D, E & F online On syllabus this is referred to as online readings 1, 2 & 3 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

10 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

11 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

12 Positive correlation: as values on one variable go up, so do values
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 of Mothers Positive Correlation Height of Daughters

13 Positive correlation: as values on one variable go up, so do values
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 Brushing Teeth Negative Correlation Number Cavities

14 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

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

16 Correlation

17 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

18 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

19 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

20 Correlation - How do numerical values change?
Correlation - How do numerical values change? Let’s estimate the correlation coefficient for each of the following r = +.80 r = +1.0 r = -1.0 r = -.50 r = 0.0

21 This shows a strong positive relationship (r = 0
This shows a strong positive relationship (r = 0.97) between the price of the house and its eventual sales price r = +0.97 Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

22 r = +0.97 r = -0.48 This shows a moderate negative relationship (r = -0.48) between the amount of pectin in orange juice and its sweetness Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number)

23 r = -0.91 Description includes: Both variables
Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) This shows a strong negative relationship (r = -0.91) between the distance that a golf ball is hit and the accuracy of the drive r = -0.91

24 r = -0.91 r = 0.61 Description includes: Both variables
Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) This shows a moderate positive relationship (r = 0.61) between the price of the length of stay in a hospital and the number of services provided r = -0.91 r = 0.61

25 r = +0.97 r = -0.48 r = -0.91 r = 0.61

26 Height of Daughters (inches)
Height of Mothers (in) This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Both axes have real numbers listed Both axes and values are labeled Variable name is listed clearly

27 Height of Daughters (inches)
Height of Mothers (in) This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Both axes have real numbers listed Both axes and values are labeled Variable name is listed clearly

28 Height of Daughters (inches)
Height of Mothers (in) This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Both axes have real numbers listed Both axes and values are labeled Variable name is listed clearly

29 Height of Daughters (inches)
Height of Mothers (in) This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Both axes have real numbers listed Both axes and values are labeled Variable name is listed clearly

30 Height of Daughters (inches)
Height of Mothers (in) This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Both axes have real numbers listed Both axes and values are labeled Variable name is listed clearly

31 Use examples that are different from those is lecture
Break into groups of 2 or 3 Each person hand in own worksheet. Be sure to list your name and names of all others in your group Use examples that are different from those is lecture 1. Describe one positive correlation Draw a scatterplot (label axes) You have 12 minutes (approximately 2 minutes per example) 2. Describe one negative correlation Draw a scatterplot (label axes) 3. Describe one zero correlation Draw a scatterplot (label axes) 4. Describe one perfect correlation (positive or negative) Draw a scatterplot (label axes) 5. Describe curvilinear relationship Draw a scatterplot (label axes)

32 Height of Daughters (inches)
Height of Mothers (in) This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Both axes have real numbers listed Both axes and values are labeled Variable name is listed clearly 1. Describe one positive correlation Draw a scatterplot (label axes) 2. Describe one negative correlation Draw a scatterplot (label axes) Hand in Correlation worksheet 3. Describe one zero correlation Draw a scatterplot (label axes) 4. Describe one perfect correlation (positive or negative) Draw a scatterplot (label axes) 5. Describe curvilinear relationship Draw a scatterplot (label axes)

33 Thank you! See you next time!!

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