1. Regression Inferential Methods

2. Height
Weight
Suppose you took many samples of the same size from this population & calculated the LSRL for each.
Using the slope from each of these LSRLs – we can create a sampling distribution for the slope of the true LSRL.
What is the standard deviation of the sampling distribution?
What is the mean of the sampling distribution equal?
What shape will this distribution have? b b b b b b b
mb = b

3. What would you expect for other heights?
Weight
What would you expect for other heights?
How much would an adult female weigh if she were 5 feet tall?
This distribution is normally distributed.
(we hope)
She could weigh varying amounts – in other words, there is a distribution of weights for adult females who are 5 feet tall.
What about the standard deviations of all these normal distributions?
We want the standard deviations of all these normal distributions to be the same.
Where would you expect the TRUE LSRL to be?

4. Regression Model
The mean response my has a straight-line relationship with x:
Where: slope b and intercept a are unknown parameters
For any fixed value of x, the response y varies according to a normal distribution. Repeated responses of y are independent of each other.
The standard deviation of y (sy) is the same for all values of x. (sy is also an unknown parameter)

5. What distribution does their weight have?
Person # Ht Wt 1 64
130 10
175 15
150 19
125 21
145 40
186 47
121 60
137 63
143 68
120 70
112 78
108 83
160
Suppose we look at part of a population of adult women. These women are all 64 inches tall.
What distribution does their weight have?

6. The slope b of the LSRL is an unbiased estimator of the true slope b.
We use to estimate
The slope b of the LSRL is an unbiased estimator of the true slope b.
The intercept a of the LSRL is an unbiased estimator of the true intercept a.
The standard error s is an unbiased estimator of the true standard deviation of y (sy).
Note:
df = n-2

7. Let’s review the regression model!
x & y have a linear relationship with the true LSRL going through the my
sy is the same for each x-value.
For a given x-value, the responses (y) are normally distributed

8. What is the slope of a horizontal line?
Height
Weight
Suppose the LSRL has a horizontal line –would height be useful in predicting weight?
A slope of zero – means that there is NO relationship between x & y!

9. Assumptions for inference on slope
The true relationship is Linear
Check the scatter plot & residual plot
The observations are Independent and random
Check that you have an SRS
For any fixed value of x, the response y varies Normally about the true regression line.
Check a histogram or boxplot of residuals
Equal variance about regression line. The standard deviation of the response is constant.
L I N E

10. Hypotheses Be sure to define b! H0: b = 0 1 Ha: b > 0 Ha: b < 0
This implies that there is no relationship between x & y
Or that x should not be used to predict y
What would the slope equal if there were a perfect relationship between x & y?
H0: b = 0
Ha: b > 0
Ha: b < 0
Ha: b ≠ 0 1
Be sure to define b!

11. Because there are two unknowns a & b
Formulas:
Confidence Interval:
Hypothesis test:
df = n -2
Because there are two unknowns a & b

12. Body fat = -27.376 + 0.250 weight r = 0.697 r2 = 0.485
Example: It is difficult to accurately determine a person’s body fat percentage without immersing him or her in water. Researchers hoping to find ways to make a good estimate immersed 20 male subjects, and then measured their weights.
Find the LSRL, correlation coefficient, and coefficient of determination.
Body fat = weight
r = 0.697
r2 = 0.485

13. b) Explain the meaning of slope in the context of the problem.
For each increase of 1 pound in weight, there is an approximate increase in .25 percent body fat.
c) Explain the meaning of the coefficient of determination in context.
Approximately 48.5% of the variation in body fat can be explained by the regression of body fat on weight.

14. a = -27.376 b = 0.25 s = 7.049 d) Estimate a, b, and s.
e) Create a scatter plot and residual plot for the data.
Weight
Body fat
Weight
Residuals

15. f) Is there sufficient evidence that weight can be used to predict body fat?
Assumptions:
Scatterplot and residual plot shows Linear association.
Have an Independent SRS of male subjects
Since the boxplot of residual is approximately symmetrical, the responses are approximately Normally distributed.
Since the points are evenly spaced across the LSRL on the scatterplot, sy is approximately Equal for all values of weight
H0: b = 0 Where b is the true slope of the LSRL of weight Ha: b ≠ 0 & body fat
Since the p-value < a, I reject H0. There is sufficient evidence to suggest that weight can be used to predict body fat.

16. Be sure to show all graphs!
g) Give a 95% confidence interval for the true slope of the LSRL.
Assumptions:
Scatter plot and residual plot show LINEAR association
Have an INDEPENDENT SRS of male subjects
Since the boxplot of residualS is approximately symmetrical, the responses are approximately NORMALLY distributed.
Since the points are evenly spaced across the LSRL on the scatterplot, sy is approximately EQUAL for all values of weight
We are 95% confident that the true slope of the LSRL of weight & body fat is between 0.12 and 0.38.
Be sure to show all graphs!

17. What does “s” represent (in context)?
h) Here is the computer-generated result from the data:
Sample size: 20
R-square = 48.5%
s =
df?
What does “s” represent (in context)?
Parameter
Estimate
Std. Err.
Intercept
Weight
Correlation coeficient?
Be sure to write as decimal first!
What does this number represent?
What do these numbers represent?