Lecturer’s desk

1. Screen Cabinet Cabinet Lecturer’s desk Table Computer Storage Cabinet Row A 3 4 5 19 6 18 7 17 16 8 15 9 10 11 14 13 12 Row B 1 2 3 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row C 1 2 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row D 1 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row E 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row F 27 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 28 Row G 27 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 29 10 19 11 18 16 15 13 12 17 14 28 Row H 27 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row I 1 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 1 Row J 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 28 27 1 Row K 26 2 25 3 24 4 23 5 6 22 21 7 20 8 9 10 19 11 18 16 15 13 12 17 14 Row L 20 1 19 2 18 3 17 4 16 5 15 6 7 14 13 INTEGRATED LEARNING CENTER ILC 120 9 8 10 12 11 broken desk

2. Introduction to Statistics for the Social SciencesSBS200, COMM200, GEOG200, PA200, POL200, SOC200Lecture Section 001, Spring, 2012Room 120 Integrated Learning Center (ILC)9:00 - 9:50 Mondays, Wednesdays & Fridays+ Lab Session. Welcome http://www.youtube.com/watch?v=oSQJP40PcGI

3. Use this as your study guide By the end of today 4/25/12 Simple and Multiple Regression Evaluations

4. Homework: No more homework!! Please click in My last name starts with a letter somewhere between A. A – D B. E – L C. M – R D. S – Z

5. Schedule of readings Before next exam (Monday April 30th) Please read chapters 10 – 12 Please read Chapters 17, and 18 in Plous Chapter 17: Social Influences Chapter 18: Group Judgments and Decisions

6. Exam 4 – Optional Final Time • Two options for completing Exam 4 • Monday (4/30/12) • Wednesday (5/2/12) • Must sign up to take Exam 4 on Friday (4/27) • Only need to take one exam – these are two optional times Study guide is online now Bring 2 calculators Bring 2 pencils (with good erasers) Bring ID

8. Some useful terms • Regression uses the predictor variable (independent) to make predictions about the predicted variable (dependent) • Coefficient of correlation is name for “r” • Coefficient of determination is name for “r2”(remember it is always positive – no direction info) • Standard error of the estimate is our measure of the variability of the dots around the regression line(average deviation of each data point from the regression line – like standard deviation)

9. YearlyIncome Expenses per year Multiple regression will use multiple independent variables to predict the single dependent variable You probably make this much The predicted variable goes on the “Y” axis and is called the dependent variable. The predictor variable goes on the “X” axis and is called the independent variable You probably make this much Dependent Variable (Predicted) If you spend this much If you save this much Independent Variable 1 (Predictor) If you spend this much Independent Variable 2 (Predictor)

10. Multiple regression equations 1 How many independent variables? How many dependent variables? Prediction line Y’ = b1X 1+ b0 • We can predict amount of crime in a city from • the number of bathrooms in city Prediction line Y’ = b1X 1+ b2X 2+ b0 • We can predict amount of crime in a city from • the number of bathrooms in city • the amount spent on education in city 3 How many independent variables? 1 How many dependent variables? Prediction line Y’ = b1X 1+ b2X 2+ b3X 3+ b0 • We can predict amount of crime in a city from • the number of bathrooms in city • the amount spent on education in city • the amount spent on after-school programs

11. Multiple regression • Used to describe the relationship between several independent variables and a dependent variable. Can we predict amount of crime in a city from the number of bathrooms and the amount of spent on education and on after-school programs? Prediction line Y’ = b1X 1+ b2X 2+ b3X 3+ b0 • X1 X2 and X3are the independent variables. • Y is the dependent variable (amount of crime) • b0is the Y-intercept • b1is the net change in Y for each unit change in X1 holding X2and X3 constant. It is called a regressioncoefficient.

12. Multiple Linear Regression – Example from Lab Can we predict heating cost? Three variables are thought to relate to the heating costs: (1) the mean daily outside temperature, (2) the number of inches of insulation in the attic, and (3) the age in years of the furnace. To investigate, Salisbury's research department selected a random sample of 20 recently sold homes. It determined the cost to heat each home last January

13. Multiple Linear Regression - Example

14. The Multiple Regression Equation – Interpreting the Regression Coefficients b1 = The regression coefficient for mean outside temperature (b1) is -4.583. The coefficient is negative and shows a negative correlation between heating cost and temperature. As the outside temperature increases, the cost to heat the home decreases. The numeric value of the regression coefficient provides more information. If we increase temperature by 1 degree and hold the other two independent variables constant, we can estimate a decrease of $4.583 in monthly heating cost.

15. The Multiple Regression Equation – Interpreting the Regression Coefficients b2 = The regression coefficient for mean attic insulation (b2) is -14.831. The coefficient is negative and shows a negative correlation between heating cost and insulation. The more insulation in the attic, the less the cost to heat the home. So the negative sign for this coefficient is logical. For each additional inch of insulation, we expect the cost to heat the home to decline $14.83 per month, regardless of the outside temperature or the age of the furnace.

16. The Multiple Regression Equation – Interpreting the Regression Coefficients b3 = The regression coefficient for mean attic insulation (b3) is 6.101 The coefficient is positive and shows a negative correlation between heating cost and insulation. As the age of the furnace goes up, the cost to heat the home increases. Specifically, for each additional year older the furnace is, we expect the cost to increase $6.10 per month.

23. Applying the Model for Estimation What is the estimated heating cost for a home if: • the mean outside temperature is 30 degrees, • there are 5 inches of insulation in the attic, and • the furnace is 10 years old?

24. Let’s try one Which of the following correlations would allow you the most accurate predictions? a. r = + 0.01b. r = - 0.10 c. r = + 0.40d. r = - 0.65

25. Let’s try one After duplicate correlations have been discarded and trivial correlations have been ignored, there remain a. two correlationsb. three correlationsc. six correlationsd. nine correlations

26. Let’s try one Which of the following conclusions can not be made from the data in the matrix? a. There is a significant correlation between Science and Reading b. There is a significant correlation between Math and Reading c. There is a significant correlation between Math and Science

27. Let’s try one Which of these correlations would be most likely to have the highest positive value for r?a. Scatterplot Ab. Scatterplot Bc. Scatterplot Cd. Can not be determined from the information given

28. Let’s try one Which of the these scatterplots will have the smallest “y intercept”?a. Scatterplot Ab. Scatterplot Bc. Scatterplot Cd. Can not be determined from the information given

29. Let’s try one Which of the these correlations would be most likely to representthe correlation between salary and expenses? a. Scatterplot Ab. Scatterplot Bc. Scatterplot Cd. Can not be determined from the information given

30. Just one quick favor… Please take just a minute to fill these out…..

31. Please take just a minute to fill these out I'll be sitting outside Thank you for a wonderful semester! and good luck with your studies See you Friday and at the final exam .

32. Thank you! Good luck with your studies!