1. How good of guesser ARE YOU?????
The following contains pictures of famous (or not-so-famous) people. Without talking to anyone, write down your estimate of the age of each person (as of today) in the 2nd column. If you do not know the person, make a guess.

2. BARACK OBAMA

3. KENNY CHESNEY

4. MICHAEL JORDAN

5. JENNIFER LOPEZ

6. OPRAH WINFREY

7. KOBE BRYANT

8. TONY HAWK

9. BETTY WHITE

10. TIGER WOODS

11. LADY GAGA

12. DAVID BECKHAM

13. Dr. Phil

14. FLAVA FLAV

15. MRS. ASHBURN

16. MRS. CHILDREY!

17. In the first column, write down the person’s actual age.
TIME FOR THE ANSWERS!
In the first column, write down the person’s actual age.

18. BARACK OBAMA
Born: August 4th, 1961
Age:57 years old

19. KENNY CHESNEY
Born:
March 26, 1968
Age: 50 years old

20. MICHAEL JORDAN Born Feb 17, 1963, Age 55 years old

21. JENNIFER LOPEZ
Born:
July 24th , 1969
Age:
49 years old

22. OPRAH WINFREY
Born: January 29th , 1954
Age:
64 years old

23. KOBE BRYANT
Born: August 23, 1978
Age:
40 years old

24. TONY HAWK
Born:
May 12th , 1968
Age:
50 years old

25. BETTY WHITE
Born: January 17, 1922
Age:
96 years old

26. TIGER WOODS
Born: December 30th, 1974
Age:
43 years old

27. LADY GAGA
Born: March 28th, 1986
Age:
32 years old

28. DAVID BECKHAM
Born: May 2nd, 1975
Age:
43 years old

29. Dr. Phil
Born: September 1st, 1950
Age:
68 years old

30. FLAVA FLAV
Born: March 16th, 1959
Age:
59 years old

31. MRS. ASHBURN Born: October 7th, 1985 Age: 32 years old
Wish her a Happy Birthday before you leave on Friday!

32. Mrs. Childrey!
Born:
September 10,1970
Age:
29 years old—HAHA!

33. Is there a correlation???
After you have plotted all your points (there should be 15 total), describe the form, direction, strength, and unusual features of the scatter plot in question #1.

34. Find the best-fit line Put the points in your calculator
under the STAT Edit..
Calculate the best fit line (make sure your diagnostic is on)
CATALOG (2nd “0”  “DiagnosticOn”  ENTER)
STAT CALC  option 8: LinReg a+bx
VARS  YVARS  1: Function  1: Y1
ENTER
Look at the graph (make sure your scatter plot is on)
STAT PLOT  Plot 1  ON
ZOOM  ZoomStat (#9)
Write as an equation on your sheet (where it says “Algebraic (line of best fit)”
(correlation coefficient will be in question #5)

35. Answer the questions
On your paper, answer the rest of the questions.

36. How well does the LSRL fit the data?
Now let’s calculate the residual values.
Residuals show how far the points on the regression line are from the observed values.
We calculate this by taking the observed value minus the predicted value.
Residual = observed y – predicted y
(or data – model )

37. Fill in the table
Fill in the first column (actual age) and second column (your guessed age) of observed values
In column 3, Calculate the LSRL prediction value for each person. For example, Obama is 57 years old, so substitute 57 in for x into your LSRL.
In column 4, subtract column 2 – column 3

38. Residual Plot
The Residual plot is a scatterplot of residuals against the explanatory variable. Residual plots help us assess how well a regression line fits the data.

39. Draw your residual plot
Draw your residual plot on the given graph – remember the x-axis is the explanatory (or actual age) and the y-axis is the residual value.

40. Calc Tricks To plot the residuals:
Stat, Edit, highlight L4, 2nd Stat (list), 7:Resid
Stat Plot, 1st graph, X: L1, Y: L4
**Check your plot above in your calculator**

41. Two important things about residual plots:
The residual plot should show NO obvious pattern.
The graph should show a random scatter of points about the horizontal axis.
The residuals should be relatively small in size.
A regression line that fits the data well should come “close” to most of the points

42. Standard deviation of residuals (s)
Whenever we use a least-squares regression line, the mean should be zero. That’s because the positive and negative residuals “balance out”. But that doesn’t tell us how far off the predictions are, on average.
If we use a least-squares line to predict the values of a response variable y from an explanatory variable x, the standard deviation of the residuals (s) is given by: or
This value gives the approximate size of a “typical” or “average” prediction error (residual)

43. Calculate your standard deviation!
On your sheet, calculate the average prediction error of your LSRL
After seeing how your LSRL is “off”, do you feel comfortable making predictions with this regression line?

44. HOMEWORK Read Section 3.2—pp. 164-179
Do Sec 3.2 exercises #35, 38, 39, 40, 43, 47, 48, 53
Study for Quiz on 3.1