1. Introduction to Statistics for the Social Sciences SBS200 - Lecture Section 001, Fall 2017 Room 150 Harvill Building 10: :50 Mondays, Wednesdays & Fridays.
Welcome

2. Lecturer’s desk Projection Booth Screen Screen Harvill 150 renumbered
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Harvill 150
renumbered
table 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
Projection Booth
Left handed desk

3. Schedule of readings Before our fourth and final exam (December 4th)
OpenStax
Chapters 1 – 13 (Chapter 12 is emphasized)
Plous
Chapter 17: Social Influences
Chapter 18: Group Judgments and Decisions

4. Over next couple of lectures 11/20/17
Logic of hypothesis testing with Correlations
Interpreting the Correlations and scatterplots
Simple and Multiple Regression
Using correlation for predictions
r versus r2
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)
Coefficient of regression will “b” for each variable (like slope)

5. Lab sessions No Labs this week Everyone will want to be enrolled
in one of the lab sessions
No Labs this week

6. No class on Wednesday
Happy Holiday!

7. Homework Assignment

8. A note on doodling

9. Project 4 - Two Correlations - We will use these to create two regression analyses

11. 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

12. by height (centimeters)
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
Positive
Correlation
Negative
Correlation
Perfect
Correlation
Height of Mothers
by height of Daughters
Brushing teeth
by number cavities
Height (inches)
by height (centimeters)
Height in
Centimeters
inches
Height of Mothers
Brushing Teeth
Height of
Daughters
Number Cavities

13. Correlation - How do numerical values change?
Revisit this slide

14. Final results might look like this
Predicting One
positive correlation 15 12 9 6 3
“Passion for Gaming” Score
Time Studying

17. Regression: Predicting level of passion for gaming
Step 1: Draw prediction line
b = (slope)
a = 5.72 (intercept)
Right click on dots
Draw a regression line
and regression equation

18. Regression: Predicting level of passion for gaming
Step 1: Draw prediction line
b = (slope)
a = 5.72 (intercept)
Draw a regression line
and regression equation

19. Regression: Predicting level of passion for gaming
Step 1: Draw prediction line
b = (slope)
a = 5.72 (intercept)
Draw a regression line
and regression equation

20. Regression: Predicting level of passion for gaming
Step 1: Draw prediction line
b = (slope)
a = 5.72 (intercept)
Draw a regression line
and regression equation

21. Regression: Predicting level of passion for gaming
Describe relationship
Regression line (and equation)
r = .67
Correlation: This is a strong positive correlation. Passion tends to increase as number of hours increase
Predict using regression line
(and regression equation)
b = (slope)
Slope: for each additional hour spent studying, passion increase by points
Dependent
Variable
Intercept: suggests that we can assume each person starts with a baseline passion of 5.72
Independent
Variable
a = 5.72 (intercept)

22. Final results might look like this
Predicting One
positive correlation 80 60 40 20
“Number Systems Sold”
Number of
Sales Calls

25. Regression: Predicting sales from sales calls
Step 1: Draw prediction line
Right click on dots
Draw a regression line
and regression equation

26. Display Equation and Display R-squared
Regression: Predicting sales from sales calls
r =
b = (slope)
Step 1: Draw prediction line
a = (intercept)
Choose
Display Equation and Display R-squared
Draw a regression line
and regression equation

27. Regression: Predicting sales from sales calls
b = (slope)
Step 1: Draw prediction line
a = (intercept)
Draw a regression line
and regression equation

28. Regression: Predicting sales from sales calls
Describe relationship
Regression line (and equation)
r =
Correlation: This is a strong positive correlation. Sales tend to increase as number of sales calls increase
Predict using regression line
(and regression equation)
b = (slope)
Slope: for each additional sales call, sales increase by
Dependent
Variable
Intercept: suggests that we can assume each person starts with a baseline sales of
Independent
Variable
a = (intercept)

29. How to complete scatterplots, correlations and simple regressions using Excel
Real time demo

30. Review

31. Summary
Intercept: suggests that we can assume each salesperson will sell at least systems
Slope: as sales calls increase by one, more systems should be sold
Review

32. Writing Assignment

33. Thank you!
See you next time!!