Top Banner
Dan Piett STAT 211-019 West Virginia University Lecture 11
16
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Dan PiettSTAT 211-019

West Virginia University

Lecture 11

Page 2: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Last WeekIntroduction to Hypothesis TestingHypothesis Tests for µ

Large SampleSmall Sample

Hypothesis Tests for p

Page 3: Dan Piett STAT 211-019 West Virginia University Lecture 11.

OverviewHypothesis Tests on a difference in means Hypothesis Tests on a difference in

proportionsThe 2-sided alternative

Page 4: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Section 11.1

Hypothesis Tests on the Difference in Means

Page 5: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Difference in MeansPreviously we created confidence intervals

for the difference in two population means.Male Scores vs Female Scores

This is the same idea we had when we did confidence intervals

Our same rules apply for determining large and small sample hypothesis tests

Page 6: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Large Sample Hyp. Test (n & m > 20)1. H0: µx - µy = 0 (Does not have to be 0, but almost always is)

2. HA: µx - µy < 0 (µy is bigger) or µx - µy > 0 (µx is bigger) or µx - µy ≠ 0

3. Alpha is .05 if not specified

4. Test Statistic = Z =

5. P-value will come from the normal dist. Table For > alternative: P(z>Z) For < alternative: P(z<Z) For ≠ alternative:2*P(z>|Z|)

6. Our decision rule will be to reject H0 if p-value < alpha

7. We have (do not have) enough evidence at the .05 level to conclude that the mean of group x is ______ (<, >, ≠) the mean of group y

Requires a large sample size for both groups and equal population standard deviations for both groups. Also requires independent random samples.

Page 7: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Example A college statistics professor conjectures that students

with good high school math backgrounds (2+ courses) perform better in a college statistics course than students with a poor high school math background (<2 courses). He randomly selects 35 students with a good math background and 45 students with a poor math background, and records exam scores from a college statistics course. Test the hypothesis that the mean score of the good background students will be higher than the mean score of the poor math background students. Use alpha = .10. The summary data is as follows:

Group Mean Standard Deviation

Sample Size

2+ 84.2 10.2 35

<2 73.1 14.3 45

Page 8: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Small Sample Hyp. Test (n or m < 20)1. H0: µx - µy = 0 (Does not have to be 0, but almost always is)

2. HA: µx - µy < 0 (µy is bigger) or µx - µy > 0 (µx is bigger) or µx - µy ≠ 0

3. Alpha is .05 if not specified

4. Test Statistic = T =

5. P-value will come from the t-dist. Table with df = n+m-2 For > alternative: P(t>|T|) For < alternative: P(t>|T|) For ≠ alternative: 2*P(t>|T|)

6. Our decision rule will be to reject H0 if p-value < alpha

7. We have (do not have) enough evidence at the .05 level to conclude that the mean of group x is ______ (<, >, ≠) the mean of group y

Requires both distributions are approximately normal with equal standard deviations. Also requires independent random samples.

Page 9: Dan Piett STAT 211-019 West Virginia University Lecture 11.

ExampleA researcher wishes to assess a “new”

teaching method for “slow learners”. A random sample of 8 students use the new method, and a random sample of 12 students use the “standard” teaching method. After 6 months, an exam is administered to each student. Does the data indicate that the new teaching method is preferable? Use alpha = .05. The summary statistics are as follows:

Group Mean Standard Deviation

Sample Size

New 77.125 4.853 8

Standard

72.333 6.344 12

Page 10: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Section 11.2

Hypothesis Tests for Two Independent Population Proportions

Page 11: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Difference in Pop. ProportionsWe are again interested in the difference in the

proportions of two populationsProportion of A’s on Exam 1 vs. Proportion of A’s

on Exam 2Much like all the other tests covered, the same

rules apply in Hypothesis Testing that were involved in Confidence Intervals

Also we will only be considering the case where the above is true, therefore we will only be interested in tests using Z as the test statistic.

Page 12: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Hypothesis Tests on the difference of Proportions1. H0: p1 – p2 = # (usually 0)

2. HA: p < # or p > # or p ≠ #

3. Alpha is .05 if not specified

4. Test Statistic = Z =

5. P-value will come from the normal dist. Table For > alternative: P(z>Z) For < alternative: P(z<Z) For ≠ alternative:2*P(z>|Z|)

6. Our decision rule will be to reject H0 if p-value < alpha

7. We have (do not have) enough evidence at the .05 level to conclude that the proportion of group x is ______ (<, >, ≠) the proportion of group y

Requires conditions on np’s. Also requires independent random samples

Page 13: Dan Piett STAT 211-019 West Virginia University Lecture 11.

ExamplesAmerican Cancer Society wants to

determine if the proportion of smokers in the population of Americans has decreased over the decade preceding 2002. In 1992, a random sample of 150 Americans showed 58 who smoked. In 2002, a random sample of 200 Americans included 64 who smoked. Does the data indicate that the proportion of smokers has decreased over the past decade? Use alpha = .05.

Page 14: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Section 11.3

The 2-sided alternative

Page 15: Dan Piett STAT 211-019 West Virginia University Lecture 11.

Notes on 2 Sided AlternativesUp until this point all of our examples have

had alternative hypotheses of the form < or >.

What about ≠?What we will do for this is take our previous

p-values times 2We take the value that makes sense

If our statistic is less than our null hypothesis value, we use a < probability

If our statistic is more than our null hypothesis value, we use a > probability

Page 16: Dan Piett STAT 211-019 West Virginia University Lecture 11.

ExampleThe quality control manager at a sugar

processing packaging plant must make sure that two-pound bags of sugar actually contain two pounds of sugar. He randomly selects 50 bags of sugar and weighs their contents. The sample mean is 1.962 pounds with a sample std. dev of 0.160 pounds. Does this data indicate that the mean weight of all bags of sugar is different from 2 pounds? Use alpha = .05.