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- Ch8: Hypothesis Testing (2 Samples)
Содержание
- 2. 8.1 Two Sample Hypothesis Test Compares two parameters from two populations. Two types of sampling methods:
- 3. Stating a Hypotheses in 2-Sample Hypothesis Test Null hypothesis A statistical hypothesis H0 Statement of equality
- 4. Two Sample z-Test for the Difference Between Means (μ1 and μ2.) Three conditions are necessary The
- 5. Using a Two-Sample z-Test for the Difference Between Means (Independent Samples σ1 and σ2 known or
- 6. Example1: Two-Sample z-Test for the Difference Between Means A consumer education organization claims that there is
- 7. Example2: Using Technology to Perform a Two-Sample z-Test The American Automobile Association claims that the average
- 8. 8.2 Two Sample t-Test for the Difference Between Means (σ1 or σ2 unknown) If (σ1 or
- 9. The standard error for the sampling distribution of is Two Sample t-Test for the Difference Between
- 10. Normal or t-Distribution? .
- 11. Two-Sample t-Test for the Difference Between Means - Independent Samples (σ1 or σ2 unknown) State the
- 12. Example: Two-Sample t-Test for the Difference Between Means The braking distances of 8 Volkswagen GTIs and
- 13. Example: Two-Sample t-Test for the Difference Between Means A manufacturer claims that the calling range (in
- 14. The test statistic is the mean of these differences. 8.3 t-Test for the Difference Between Means
- 15. Symbols used for the t-Test for μd The number of pairs of data The difference between
- 16. t-Test for the Difference Between Means (Dependent Samples) State the claim mathematically. Identify the null and
- 17. Example: t-Test for the Difference Between Means A golf club manufacturer claims that golfers can lower
- 18. Solution: Two-Sample t-Test for the Difference Between Means d = (old score) – (new score) Larson/Farber
- 19. 8.4 Two-Sample z-Test for Proportions Used to test the difference between two population proportions, p1 and
- 20. Two-Sample z-Test for the Difference Between Proportions State the claim. Identify the null and alternative hypotheses.
- 21. Example1: Two-Sample z-Test for the Difference Between Proportions In a study of 200 randomly selected adult
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8.1 Two Sample Hypothesis Test
Compares two parameters from two populations.
Two types
8.1 Two Sample Hypothesis Test
Compares two parameters from two populations.
Two types
Independent (unrelated) Samples
Dependent Samples (paired or matched samples)
Each member of one sample corresponds to a member of the other sample.
Larson/Farber
Sample 1: Resting heart rates of 35 individuals before drinking coffee.
Sample 2: Resting heart rates of the same individuals after drinking two cups of coffee.
Sample 1: Test scores for 35 statistics students.
Sample 2: Test scores for 42 biology students who do not study statistics.
Stating a Hypotheses in 2-Sample Hypothesis Test
Null hypothesis
A statistical hypothesis
Stating a Hypotheses in 2-Sample Hypothesis Test
Null hypothesis
A statistical hypothesis
Statement of equality (≤, =, or ≥).
No difference between the parameters of two populations.
Alternative Hypothesis (Ha)
(Complementary to Null Hypothesis)
A statement of inequality (>, ≠, or <).
True when H0 is false.
Larson/Farber
OR
OR
H0: μ1 = μ2
Ha: μ1 ≠ μ2
H0: μ1 ≤ μ2
Ha: μ1 > μ2
H0: μ1 ≥ μ2
Ha: μ1 < μ2
Regardless of which hypotheses you use, you always assume there is no difference between the population means, or μ1 = μ2.
Two Sample z-Test for the Difference Between Means (μ1 and μ2.)
Three
Two Sample z-Test for the Difference Between Means (μ1 and μ2.)
Three
The samples must be randomly selected.
The samples must be independent.
Each population must have a normal distribution with a known population standard deviation OR each sample size must be at least 30.
Larson/Farber
Sampling distribution for (difference of sample means) is a normal with:
Mean:
Standard error:
Sampling
distribution
Test Statistic:
Standardized Test Statistic:
For large samples: use s1 and s2 in place of σ1 and σ2.
For small samples: use a two-sample z-test if populations are normally distributed & pop. std deviations are known.
Using a Two-Sample z-Test for the Difference Between Means (Independent Samples
Using a Two-Sample z-Test for the Difference Between Means (Independent Samples
State the claim mathematically. Identify the null and alternative hypotheses.
Specify the level of significance.
Sketch the sampling distribution.
Determine the critical value(s).
Determine the rejection region(s).
State H0 and Ha.
Identify α.
Use Table 4 in Appendix B.
In Words In Symbols
Larson/Farber
Find the standardized test statistic.
Make a decision to reject or fail to reject the null hypothesis.
Interpret the decision in the context of the original claim.
If z is in the rejection region, reject H0.
Example1: Two-Sample z-Test for the Difference Between Means
A consumer education organization
Example1: Two-Sample z-Test for the Difference Between Means
A consumer education organization
Larson/Farber
H0:
Ha:
α = .05
n1= 200 , n2 = 200
Rejection Region:
Decision: Fail to reject H0
At the 5% level of significance, there is not enough evidence to support the organization’s claim that there is a difference in the mean credit card debt of males and females.
Ti83/84
Stat-Tests
3:2-SampZTest
Example2: Using Technology to Perform a Two-Sample z-Test
The American Automobile Association
Example2: Using Technology to Perform a Two-Sample z-Test
The American Automobile Association
Larson/Farber
H0:
Ha:
TI-83/84set up:
Calculate
Decision: Fail to reject H0
At 1% level, Not enough
evidence to support AAA’s claim.
8.2 Two Sample t-Test for the Difference Between Means (σ1 or
8.2 Two Sample t-Test for the Difference Between Means (σ1 or
If (σ1 or σ2 is unknown and samples are taken from normally-distributed) OR
If (σ1 or σ2 is unknown and both sample sizes are greater than or equal to 30)
THEN a t-test may be used to test the difference between the population
means μ1 and μ2.
Three conditions are necessary to use a t-test for small independent samples.
The samples must be randomly selected.
The samples must be independent.
Each population must have a normal distribution.
Larson/Farber
Test Statistic:
The standard error for the sampling distribution of is
Two Sample
The standard error for the sampling distribution of is
Two Sample
Equal Variances
Information from the two samples is combined to calculate a pooled estimate of the standard deviation
.
d.f.= n1 + n2 – 2
Larson/Farber
The standard error and the degrees of freedom of the sampling distribution depend on whether the population variances and are equal.
UnEqual Variances
Standard Error is:
d.f = smaller of n1 – 1 or n2 – 1
Normal or t-Distribution?
.
Normal or t-Distribution?
.
Two-Sample t-Test for the Difference Between Means - Independent Samples
(σ1
Two-Sample t-Test for the Difference Between Means - Independent Samples (σ1
State the claim mathematically. Identify the null and alternative hypotheses.
Specify the level of significance.
Identify the degrees of freedom and sketch the sampling distribution.
Determine the critical value(s).
State H0 and Ha.
Identify α.
Use Table 5 in Appendix B.
d.f. = n1+ n2 – 2 or
d.f. = smaller of n1 – 1 or n2 – 1.
In Words In Symbols
Larson/Farber
Determine the rejection region(s).
Find the standardized test statistic.
Make a decision to reject or fail to reject the null hypothesis and interpret the decision in the context of the original claim
If t is in the rejection region, reject H0. Otherwise, fail to reject H0.
Example: Two-Sample t-Test for the Difference Between Means
The braking distances of
Example: Two-Sample t-Test for the Difference Between Means
The braking distances of
Larson/Farber 4th ed
H0:
Ha:
α =
d.f. =
Decision:
At the 1% level of significance, there is not enough evidence to conclude that the mean braking distances of the cars are different.
Fail to Reject H0
Stat-Test
4: 2-SampTTest
Pooled: No
Example: Two-Sample t-Test for the Difference Between Means
A manufacturer claims that
Example: Two-Sample t-Test for the Difference Between Means
A manufacturer claims that
Larson/Farber 4th ed
H0 =
Ha =
α = .05
d.f. = 14 + 16 – 2 = 28
μ1 ≤ μ2
μ1 > μ2
Decision: Reject H0
At 5% level of significance there is enough evidence to support the manufacturer’s claim.
(Claim)
Stat-Test
4: 2-SampTTest
Pooled: Yes
The test statistic is the mean of these differences.
8.3 t-Test for
The test statistic is the mean of these differences.
8.3 t-Test for
To perform a two-sample hypothesis test with dependent samples, the difference between each data pair is first found:
d = x1 – x2 Difference between entries for a data pair
Mean of the differences between paired data entries in the dependent samples
Larson/Farber
Three conditions are required to conduct the test.
The samples must be randomly selected.
The samples must be dependent (paired).
Both populations must be normally distributed.
If these conditions are met, then the sampling distribution for is approximated by a t-distribution with n – 1 degrees of freedom, where n is the number of data pairs.
Symbols used for the t-Test for μd
The number of pairs of
Symbols used for the t-Test for μd
The number of pairs of
The difference between entries for a data pair, d = x1 – x2
The hypothesized mean of the differences of paired data in the population
n
d
Larson/Farber
Mean of the differences between paired data entries in dependent samples
sd
The standard deviation of the differences between the paired data entries in the dependent samples
(Test Statistic)
(Standardized Test Statistic)
Degrees of Freedom (d.f.) = n - 1
t-Test for the Difference Between Means (Dependent Samples)
State the claim mathematically.
t-Test for the Difference Between Means (Dependent Samples)
State the claim mathematically.
Specify the level of significance.
Identify the degrees of freedom and sketch the sampling distribution.
Determine critical value(s) & rejection region
State H0 and Ha.
Identify α.
Use Table 5 in Appendix B
If n > 29 use the last row (∞) .
d.f. = n – 1
In Words In Symbols
Larson/Farber
Calculate and Use a table.
Find the standardized test statistic.
Make a decision to reject or fail to reject the
null hypothesis and interpret the decision in
the context of the original claim.
If t is in the rejection region, reject H0. Otherwise, fail to reject H0.
Example: t-Test for the Difference Between Means
A golf club manufacturer claims
Example: t-Test for the Difference Between Means
A golf club manufacturer claims
Larson/Farber 4th ed
H0:
Ha:
α =
d.f. = 8 – 1 = 7
Rejection
Region
μd ≤ 0
μd > 0
0.10
d = (old score) – (new score)
Decision: Reject H0
At the 10% level of significance, the results of this test indicate that after the golfers used the new clubs, their scores were significantly lower.
& Sd calculations on next slide
Solution: Two-Sample t-Test for the Difference Between Means
d = (old score)
Solution: Two-Sample t-Test for the Difference Between Means
d = (old score)
Larson/Farber
8.4 Two-Sample z-Test for Proportions
Used to test the difference between two
8.4 Two-Sample z-Test for Proportions
Used to test the difference between two
Three conditions are required to conduct the test.
The samples must be randomly selected.
The samples must be independent.
The samples must be large enough to use a normal sampling distribution. That is, n1p1 ≥ 5, n1q1 ≥ 5, n2p2 ≥ 5, and n2q2 ≥ 5.
Larson/Farber
If these conditions are met, then the sampling distribution for is normal
Mean:
Find weighted estimate of p1 and p2 using
Standard error:
(Test Statistic)
Standardized Test Statistic
where
Two-Sample z-Test for the Difference Between Proportions
State the claim. Identify the
Two-Sample z-Test for the Difference Between Proportions
State the claim. Identify the
Specify the level of significance.
Determine the critical value(s).
Determine the rejection region(s).
Find the weighted estimate of p1 and p2.
State H0 and Ha.
Identify α.
Use Table 4 in Appendix B.
In Words In Symbols
Larson/Farber
Find the standardized test statistic.
Make a decision to reject or fail to reject the null hypothesis and interpret decision in the context of the original claim.
If z is in the rejection region, reject H0. Otherwise, fail to reject H0.
Example1: Two-Sample z-Test for the Difference Between Proportions
In a study of
Example1: Two-Sample z-Test for the Difference Between Proportions
In a study of
Larson/Farber 4th ed
α = .10 n1 = 200 n2 = 250
H0 : p1 = p2
Ha : p1 ≠ p2
Decision: Reject H0
At the 10% level of significance, there is enough evidence to conclude that there is a difference between the proportion of female and the proportion of male Internet users who plan to shop online.
Stat-Tests
5: 2-PropZTest