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Stage. Screen. Lecturer’s desk. 12. 11. 10. 13. 9. 8. 7. 6. 5. 4. 15. 17. 14. 16. Row A. Row A. 1. 3. 2. Row A. Left handed. 12. 11. 10. 13. Row B. 9. 8. 5. 15. 14. 1. 2. 7. 6. 4. 3. Row B. 20. 19. 18. 17. 16. Row B. 10. 13. 12. 11. Row C. 9.

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  1. Stage Screen Lecturer’s desk 12 11 10 13 9 8 7 6 5 4 15 17 14 16 Row A Row A 1 3 2 Row A Left handed 12 11 10 13 Row B 9 8 5 15 14 1 2 7 6 4 3 Row B 20 19 18 17 16 Row B 10 13 12 11 Row C 9 8 5 15 14 1 19 18 17 16 Row C 4 3 7 6 Row C 2 13 12 11 10 Row D 9 8 5 15 14 1 2 7 6 Row D 4 3 20 19 18 17 16 Row D 10 4 3 2 1 13 12 11 Row E 9 8 5 15 14 7 6 Row E 19 18 17 16 Row E 1 2 13 12 11 10 Row F 9 8 5 4 3 15 14 7 6 20 19 18 17 Row F 16 Row F 1 13 12 11 10 Row G 4 3 9 8 5 2 15 14 19 18 17 16 Row G 7 6 Row G 13 12 11 10 Row H 9 8 5 15 14 1 2 7 6 4 3 20 19 18 17 Row H 16 Row H 1 13 12 11 10 Row I 4 3 9 8 5 2 15 14 19 18 17 16 Row I 7 6 Row I 1 2 13 12 11 10 Row J 9 8 5 4 3 15 14 7 6 20 19 18 17 Row J 16 Row J 1 4 3 2 13 12 11 10 Row K 9 8 5 15 14 19 18 17 16 Row K 7 6 Row K 7 1 2 13 12 11 10 Row L 9 8 5 20 4 3 15 14 16 6 19 18 17 Row L Row L 1 4 3 2 13 12 11 10 Row M 9 8 5 15 14 19 18 17 16 Row M 7 6 Row M 1 2 13 12 11 10 Row N 9 8 7 5 4 3 15 14 16 6 20 19 18 17 Row N Row N 4 3 2 1 13 12 11 10 Row O 9 8 5 15 14 19 18 17 16 Row O 7 6 Row O 1 2 13 12 11 10 Row P 9 8 7 5 4 3 15 14 16 6 20 19 18 17 Row P Row P Need Labels B5, E1, I16, J17, K8, M4, O1, P16 16 15 14 1 2 Row Q 13 12 11 10 Row Q 3 9 8 7 5 4 6 Row Q Gallagher Theater 3 2 Row R Row R 4 26Left-Handed Desks A14, B16, B20, C19, D16, D20, E15, E19, F16, F20, G19, H16, H20, I15, J16, J20, K19, L16, L20, M15, M19, N16, P20, Q13, Q16, S4 5 Broken Desks B9, E12, G9, H3, M17 Row S 10 1 2 9 8 7 Row S 4 3

  2. Stage Screen Social Sciences 100 Lecturer’s desk R/L handed 16 13 17 15 14 12 Row A 26 25 24 12 11 10 27 23 22 13 Row B Row B 21 20 19 18 17 16 15 14 28 27 26 23 25 24 12 11 10 Row C 22 13 9 8 6 5 Row C 21 20 19 18 17 16 15 14 7 4 Row C 9 8 6 5 27 2 29 28 26 25 24 23 12 11 10 7 4 3 22 13 Row D Row D 30 21 20 19 18 17 16 15 14 Row D 12 11 10 5 2 1 22 13 9 8 7 6 4 3 Row E 23 21 20 19 18 17 16 15 14 31 30 29 28 27 26 25 24 Row E Row E 5 2 1 12 11 10 9 8 7 6 4 3 Row F 22 13 21 20 19 18 17 16 15 14 Row F 23 31 30 29 28 27 26 25 24 Row F Row G 23 31 30 29 28 27 26 25 24 12 11 10 22 13 Row G Row G 5 21 20 19 18 17 16 15 14 2 1 9 8 7 6 4 3 5 2 1 Row H 9 8 7 6 4 3 12 11 10 22 13 23 21 20 19 18 17 16 15 14 31 30 29 28 27 26 25 24 Row H Row H 12 11 10 5 2 1 22 13 Row I 9 8 7 6 4 3 21 20 19 18 17 16 15 14 Row I 23 31 30 29 28 27 26 25 24 Row I 5 2 1 12 11 10 9 8 7 6 4 3 Row J 22 13 21 20 19 18 17 16 15 14 Row J 23 31 30 29 28 27 26 25 24 Row J 5 2 1 9 8 7 6 4 3 Row K 12 11 10 Row K 22 13 23 21 20 19 18 17 16 15 14 31 30 29 28 27 26 25 24 Row K Row L 5 1 9 8 7 6 4 3 12 11 10 Row L 22 13 23 21 20 19 18 17 16 15 14 31 30 29 28 27 26 25 24 Row L 2 Row M 5 2 1 9 8 7 6 4 3 23 31 30 29 28 27 26 25 24 12 11 10 Row M 22 13 21 20 19 18 17 16 15 14 Row M Row N 5 2 1 12 11 10 9 8 7 6 4 3 22 13 21 20 19 18 17 16 15 14 Row N 23 31 30 29 28 27 26 25 24 Row N 5 2 1 9 8 7 6 4 3 12 11 10 Row O 23 31 30 29 28 27 26 25 24 22 13 21 20 19 18 17 16 15 14 Row O Row O 5 2 1 9 8 7 6 4 3 Row P 12 11 10 Row P 23 31 30 29 28 27 26 25 24 22 13 21 20 19 18 17 16 15 14 Row P 5 2 1 Row Q 9 8 7 6 4 3 12 11 10 Row Q 22 13 23 21 20 19 18 17 16 15 14 31 30 29 28 27 26 25 24 Row Q Row R 5 2 1 9 8 7 6 4 3 12 11 10 23 Row R 31 30 29 28 27 26 25 24 22 13 21 20 19 18 17 16 15 14 Row R table broken desk 5 2 1 9 8 7 6 4 3 Projection Booth

  3. MGMT 276: Statistical Inference in ManagementFall, 2014 Welcome Green sheets

  4. Just a reminder A noteon doodling Talking or whispering to your neighbor can be a problem for us – please consider writing short notes. Complete this by today and receive extra credit! (By September 16th 2014)

  5. Homework due - (September 18th) Assignment 4Describing Data Visually using MS Excel Due: Thursday, September 18th

  6. 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

  7. Use this as your study guide By the end of lecture today9/16/14 Process of Peer Review Descriptive vs inferential analyses Time series design vs. Cross sectional design Correlational methodology

  8. Preview of Questionnaire Homework • There are four parts: • Statement of Objectives • Questionnaire itself (which is the operational definitions of the objectives) • Data collection and creation of database • Creation of graphs representing results

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

  10. Iterative design process 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

  11. Peer review Please exchange questionnaires with someone (who has same TA as you) 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

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

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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. Correlation

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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)

  26. 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)

  27. 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

  28. 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 = 0.61 r = -0.91

  29. r = +0.97 r = -0.48 r = 0.61 r = -0.91

  30. Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Variable name is listed clearly Both axes have real numbers listed Both axes and values are labeled This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). 48 52 5660 64 68 72 Height of Mothers (in) 48 52 56 60 64 68 72 76 Height of Daughters (inches)

  31. Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Variable name is listed clearly Both axes have real numbers listed Both axes and values are labeled This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). 48 52 5660 64 68 72 Height of Mothers (in) 48 52 56 60 64 68 72 76 Height of Daughters (inches)

  32. Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Variable name is listed clearly Both axes have real numbers listed Both axes and values are labeled This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). 48 52 5660 64 68 72 Height of Mothers (in) 48 52 56 60 64 68 72 76 Height of Daughters (inches)

  33. Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Variable name is listed clearly Both axes have real numbers listed Both axes and values are labeled This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). 48 52 5660 64 68 72 Height of Mothers (in) 48 52 56 60 64 68 72 76 Height of Daughters (inches)

  34. Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Variable name is listed clearly Both axes have real numbers listed Both axes and values are labeled This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). 48 52 5660 64 68 72 Height of Mothers (in) 48 52 56 60 64 68 72 76 Height of Daughters (inches)

  35. 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) 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)

  36. Variable name is listed clearly Description includes: Both variables Strength (weak,moderate,strong) Direction (positive, negative) Estimated value (actual number) Variable name is listed clearly Both axes have real numbers listed Both axes and values are labeled This shows the strong positive (r = +0.8) relationship between the heights of daughters (in inches) with heights of their mothers (in inches). 48 52 5660 64 68 72 1. Describe one positive correlation Draw a scatterplot (label axes) Height of Mothers (in) 2. Describe one negative correlation Draw a scatterplot (label axes) 48 52 56 60 64 68 72 76 Height of Daughters (inches) 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)

  37. Thank you! See you next time!!

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