chapter 10 chi-square tests and the f- distribution 1
TRANSCRIPT
Chapter 10
Chi-Square Tests and the F-Distribution
1
Chapter Outline
• 10.1 Goodness of Fit
• 10.2 Independence
• 10.3 Comparing Two Variances
• 10.4 Analysis of Variance
2
Section 10.1
Goodness of Fit
3
Section 10.1 Objectives
• Use the chi-square distribution to test whether a frequency distribution fits a claimed distribution
4
Multinomial Experiments
Multinomial experiment
• A probability experiment consisting of a fixed number of trials in which there are more than two possible outcomes for each independent trial.
• A binomial experiment had only two possible outcomes.
• The probability for each outcome is fixed and each outcome is classified into categories.
5
Multinomial Experiments
Example:• A radio station claims that the distribution of music
preferences for listeners in the broadcast region is as shown below.
Distribution of music PreferencesClassical 4% Oldies 2%Country 36% Pop 18%Gospel 11% Rock 29%
Each outcome is classified into categories.
The probability for each possible outcome is fixed.
6
Chi-Square Goodness-of-Fit Test
Chi-Square Goodness-of-Fit Test
• Used to test whether a frequency distribution fits an expected distribution.
• The null hypothesis states that the frequency distribution fits the specified distribution.
• The alternative hypothesis states that the frequency distribution does not fit the specified distribution.
7
Chi-Square Goodness-of-Fit Test
Example:
• To test the radio station’s claim, the executive can perform a chi-square goodness-of-fit test using the following hypotheses.
H0: The distribution of music preferences in the broadcast region is 4% classical, 36% country, 11% gospel, 2% oldies, 18% pop, and 29% rock. (claim)
Ha: The distribution of music preferences differs from the claimed or expected distribution.
8
Chi-Square Goodness-of-Fit Test
• To calculate the test statistic for the chi-square goodness-of-fit test, the observed frequencies and the expected frequencies are used.
• The observed frequency O of a category is the frequency for the category observed in the sample data.
9
Chi-Square Goodness-of-Fit Test
• The expected frequency E of a category is the calculated frequency for the category. Expected frequencies are obtained assuming the
specified (or hypothesized) distribution. The expected frequency for the ith category is
Ei = npi
where n is the number of trials (the sample size) and pi is the assumed probability of the ith category.
10
Example: Finding Observed and Expected Frequencies
A marketing executive randomly selects 500 radio music listeners from the broadcast region and asks each whether he or she prefers classical, country, gospel, oldies, pop, or rock music. The results are shown at the right. Find the observed frequencies and the expected frequencies for each type of music.
Survey results
(n = 500)Classical 8Country 210Gospel 72Oldies 10Pop 75Rock 125
11
Solution: Finding Observed and Expected Frequencies
Observed frequency: The number of radio music listeners naming a particular type of music
Survey results(n = 500)
Classical 8Country 210Gospel 72Oldies 10Pop 75Rock 125
observed frequency
12
Solution: Finding Observed and Expected Frequencies
Expected Frequency: Ei = npi
Type of music
% of listeners
Observed frequency
Expected frequency
Classical 4% 8Country 36% 210Gospel 11% 72Oldies 2% 10Pop 18% 75Rock 29% 125
n = 500
500(0.04) = 20500(0.36) = 180500(0.11) = 55500(0.02) = 10500(0.18) = 90500(0.29) = 145
13
Distribution of music PreferencesClassical 4% Oldies 2%Country 36% Pop 18%Gospel 11% Rock 29%
Chi-Square Goodness-of-Fit Test
For the chi-square goodness-of-fit test to be used, the following must be true.
1.The observed frequencies must be obtained by using a random sample.
2.Each expected frequency must be greater than or equal to 5.
14
Chi-Square Goodness-of-Fit Test
• If these conditions are satisfied, then the sampling distribution for the goodness-of-fit test is approximated by a chi-square distribution with k – 1 degrees of freedom, where k is the number of categories.
• The test statistic for the chi-square goodness-of-fit test is
where O represents the observed frequency of each category and E represents the expected frequency of each category.
22 ( )O E
E The test is always
a right-tailed test.
15
Chi-Square Goodness-of-Fit Test
1. Identify the claim. State the null and alternative hypotheses.
2. Specify the level of significance.
3. Identify the degrees of freedom.
4. Determine the critical value.
State H0 and Ha.
Identify .
Use Table 6 in Appendix B.
d.f. = k – 1
In Words In Symbols
16
Chi-Square Goodness-of-Fit Test
If χ2 is in the rejection region, reject H0. Otherwise, fail to reject H0.
5. Determine the rejection region.
6. Calculate the test statistic.
7. Make a decision to reject or fail to reject the null hypothesis.
8. Interpret the decision in the context of the original claim.
22 ( )O E
E
In Words In Symbols
17
Example: Performing a Goodness of Fit Test
Use the music preference data to perform a chi-square goodness-of-fit test to test whether the distributions are different. Use α = 0.01.
Survey results (n = 500)
Classical 8Country 210Gospel 72Oldies 10Pop 75Rock 125
Distribution of music preferences
Classical 4%Country 36%Gospel 11%Oldies 2%Pop 18%Rock 29%
18
Solution: Performing a Goodness of Fit Test
• H0:
• Ha:
• α =
• d.f. =
• Rejection Region
• Test Statistic:
• Decision:
• Conclusion:
0.01
6 – 1 = 5
0.01
χ2
0 15.086
music preference is 4% classical, 36% country, 11% gospel, 2% oldies, 18% pop, and 29% rock
music preference differs from the claimed or expected distribution
19
Solution: Performing a Goodness of Fit Test
22 ( )O E
E
Type of music
Observed frequency
Expected frequency
Classical 8 20Country 210 180Gospel 72 55Oldies 10 10Pop 75 90Rock 125 145
2 2 2 2 2 2(8 20) (210 180) (72 55) (10 10) (75 90) (125 145)
20 180 55 10 90 14522.713
20
Solution: Performing a Goodness of Fit Test
• H0:
• Ha:
• α =
• d.f. =
• Rejection Region
• Test Statistic:
• Decision:
0.01
6 – 1 = 5
0.01
χ2
0 15.086
music preference is 4% classical, 36% country, 11% gospel, 2% oldies, 18% pop, and 29% rock
music preference differs from the claimed or expected distribution
χ2 = 22.713
22.713
There is enough evidence to conclude that the distribution of music preferences differs from the claimed distribution.
Reject H0
21
Example: Performing a Goodness of Fit Test
The manufacturer of M&M’s candies claims that the number of different-colored candies in bags of dark chocolate M&M’s is uniformly distributed. To test this claim, you randomly select a bag that contains 500 dark chocolate M&M’s. The results are shown in the table on the next slide. Using α = 0.10, perform a chi-square goodness-of-fit test to test the claimed or expected distribution. What can you conclude? (Adapted from Mars Incorporated)
22
Example: Performing a Goodness of Fit Test
Color FrequencyBrown 80Yellow 95Red 88Blue 83Orange 76Green 78
Solution:•The claim is that the distribution is uniform, so the expected frequencies of the colors are equal. •To find each expected frequency, divide the sample size by the number of colors.• E = 500/6 ≈ 83.3
23
n = 500
Solution: Performing a Goodness of Fit Test
• H0:
• Ha:
• α =
• d.f. =
• Rejection Region
• Test Statistic:
• Decision:
• Conclusion:
0.10
6 – 1 = 5
0.10
χ2
0 9.236
Distribution of different-colored candies in bags of dark chocolate M&Ms is uniform
Distribution of different-colored candies in bags of dark chocolate M&Ms is not uniform
24
Solution: Performing a Goodness of Fit Test
2 2 2 2 2 2(80 83.3) (95 83.3) (88 83.3) (83 83.3) (76 83.3) (78 83.3)
83.3 83.3 83.3 83.3 83.3 83.33.016
ColorObserve
d frequen
cy
Expected
frequency
Brown 80 83.3Yellow 95 83.3Red 88 83.3Blue 83 83.3Orange 76 83.3Green 78 83.3
22 ( )O E
E
25
Solution: Performing a Goodness of Fit Test
• H0:
• Ha:
• α =
• d.f. =
• Rejection Region
• Test Statistic:
• Decision:
0.01
6 – 1 = 5
0.10
χ2
0 9.236
χ2 = 3.016
3.016
There is not enough evidence to dispute the claim that the distribution is uniform.
Distribution of different-colored candies in bags of dark chocolate M&Ms is uniform
Distribution of different-colored candies in bags of dark chocolate M&Ms is not uniform
Fail to Reject H0
26
Section 10.1 Summary
• Used the chi-square distribution to test whether a frequency distribution fits a claimed distribution
27
Section 10.2
Independence
28
Section 10.2 Objectives
• Use a contingency table to find expected frequencies
• Use a chi-square distribution to test whether two variables are independent
29
Contingency Tables
r c contingency table
• Shows the observed frequencies for two variables.
• The observed frequencies are arranged in r rows and c columns.
• The intersection of a row and a column is called a cell.
30
Contingency Tables
Example:• The contingency table shows the results of a random
sample of 550 company CEOs classified by age and size of company.(Adapted from Grant Thornton LLP, The Segal Company)
Age
Company size
39 and under
40 - 49 50 - 59 60 - 6970 and over
Small / Midsize
42 69 108 60 21
Large 5 18 85 120 22
31
Finding the Expected Frequency• Assuming the two variables are independent, you can
use the contingency table to find the expected frequency for each cell.
• The expected frequency for a cell Er,c in a contingency table is
,(Sum of row ) (Sum of column )Expected frequency
Sample sizer cr cE
32
Age
Company size39 and under
40 - 49 50 - 59 60 - 6970 and over
Small / Midsize
42 69 108 60 21
Large 5 18 85 120 22
,
r and c are independent, then:
frequency of E (r AND c)*sample size = p(r)*p(c)*sample size
of row r of column c=( )(
size size
r c
If
Expected p
Total Total
Sample Sample
)* Sample size( of row r)(Total of column c)
= size
Total
Sample
Example: Finding Expected Frequencies
Find the expected frequency for each cell in the contingency table. Assume that the variables, age and company size, are independent.
Age
Company size
39 and under
40 - 49 50 - 59 60 - 6970 and over
Total
Small / Midsize
42 69 108 60 21 300
Large 5 18 85 120 22 250
Total 47 87 193 180 43 550
marginal totals33
Solution: Finding Expected Frequencies
Age
Company size
39 and under
40 - 49 50 - 59 60 - 6970 and over
Total
Small / Midsize
42 69 108 60 21 300
Large 5 18 85 120 22 250
Total 47 87 193 180 43 550
,(Sum of row ) (Sum of column )
Sample sizer cr cE
1,1
300 4725.64
550E
34
Solution: Finding Expected Frequencies
Age
Company size39 and under
40 - 49 50 - 59 60 - 6970 and over
Total
Small / Midsize
42 69 108 60 21 300
Large 5 18 85 120 22 250
Total 47 87 193 180 43 550
1,2
300 8747.45
550E
1,3
300 193105.27
550E
1,4
300 18098.18
550E
1,5
300 4323.45
550E
1,2
300 8747.45
550E
1,3
300 193105.27
550E
1,4
300 18098.18
550E
35
Solution: Finding Expected Frequencies
Age
Company size39 and under
40 - 49 50 - 59 60 - 6970 and over
Total
Small / Midsize
42 69 108 60 21 300
Large 5 18 85 120 22 250
Total 47 87 193 180 43 550
2,2
250 8739.55
550E
2,4
250 18081.82
550E
2,5
250 4319.55
550E
2,1
250 4721.36
550E
2,3
250 19387.73
550E
36
Chi-Square Independence Test
Chi-square independence test
• Used to test the independence of two variables.
• Can determine whether the occurrence of one variable affects the probability of the occurrence of the other variable.
37
Chi-Square Independence Test
For the chi-square independence test to be used, the following must be true.
1.The observed frequencies must be obtained by using a random sample.
2.Each expected frequency must be greater than or equal to 5.
38
Chi-Square Independence Test
• If these conditions are satisfied, then the sampling distribution for the chi-square independence test is approximated by a chi-square distribution with (r – 1)(c – 1) degrees of freedom, where r and c are the number of rows and columns, respectively, of a contingency table.
• The test statistic for the chi-square independence test is
where O represents the observed frequencies and E represents the expected frequencies.
22 ( )O E
E The test is always a
right-tailed test.
39
Chi-Square Independence Test
1. Identify the claim. State the null and alternative hypotheses.
2. Specify the level of significance.
3. Identify the degrees of freedom.
4. Determine the critical value.
State H0 and Ha.
Identify .
Use Table 6 in Appendix B.
d.f. = (r – 1)(c – 1)
In Words In Symbols
40
Chi-Square Independence Test
If χ2 is in the rejection region, reject H0. Otherwise, fail to reject H0.
5. Determine the rejection region.
6. Calculate the test statistic.
7. Make a decision to reject or fail to reject the null hypothesis.
8. Interpret the decision in the context of the original claim.
22 ( )O E
E
In Words In Symbols
41
Example: Performing a χ2 Independence Test
Using the age/company size contingency table, can you conclude that the CEOs ages are related to company size? Use α = 0.01. Expected frequencies are shown in parentheses.
Age
Company size39 and under
40 - 49 50 - 59 60 - 6970 and over
Total
Small / Midsize
42(25.64)
69(47.45)
108(105.27
)
60(98.18)
21(23.45)
300
Large5
(21.36)18
(39.55)85
(87.73)120
(81.82)22
(19.55)250
Total 47 87 193 180 43 55042
Solution: Performing a Goodness of Fit Test
• H0:
• Ha:
• α =
• d.f. =
• Rejection Region
• Test Statistic:
• Decision:
0.01
(2 – 1)(5 – 1) = 4
0.01
χ2
0 13.277
CEOs’ ages are independent of company size
CEOs’ ages are dependent on company size
43
Solution: Performing a Goodness of Fit Test
2 2 2 2 2
2 2 2 2 2
(42 25.64) (69 47.45) (108 105.27) (60 98.18) (21 23.45)
25.64 47.45 105.27 98.18 23.45
(5 21.36) (18 39.55) (85 87.73) (120 81.82) (22 19.55)
21.36 39.55 87.73 81.82 19.5577.9
22 ( )O E
E
44
Solution: Performing a Goodness of Fit Test
• H0:
• Ha:
• α =
• d.f. =
• Rejection Region
• Test Statistic:
• Decision:
0.01
(2 – 1)(5 – 1) = 4
0.01
χ2
0 13.277
CEOs’ ages are independent of company size
CEOs’ ages are dependent on company size
χ2 = 77.9
There is enough evidence to conclude CEOs’ ages are dependent on company size.
77.9
Reject H0
45
Age
Company size39 and under
40 - 49 50 - 59 60 - 6970 and over
Small / Midsize
42 69 108 60 21
Large 5 18 85 120 22
Section 10.2 Summary
• Used a contingency table to find expected frequencies
• Used a chi-square distribution to test whether two variables are independent
47
Section 10.3
Comparing Two Variances
48
Section 10.3 Objectives
• Interpret the F-distribution and use an F-table to find critical values
• Perform a two-sample F-test to compare two variances
49
F-Distribution
• Let represent the sample variances of two different populations.
• If both populations are normal and the population variances are equal, then the sampling distribution of
is called an F-distribution.
2 21 2 and s s
2 21 2 and σ σ
2122
sF
s
50
Properties of the F-Distribution
1. The F-distribution is a family of curves each of which is determined by two types of degrees of freedom: The degrees of freedom corresponding to the
variance in the numerator, denoted d.f.N
The degrees of freedom corresponding to the variance in the denominator, denoted d.f.D
2. F-distributions are positively skewed.
3. The total area under each curve of an F-distribution is equal to 1.
51
Properties of the F-Distribution
4. F-values are always greater than or equal to 0.5. For all F-distributions, the mean value of F is
approximately equal to 1.
d.f.N = 1 and d.f.D = 8
d.f.N = 8 and d.f.D = 26
d.f.N = 16 and d.f.D = 7
d.f.N = 3 and d.f.D = 11
F1 2 3 4
52
Critical Values for the F-Distribution
1. Specify the level of significance .
2. Determine the degrees of freedom for the numerator, d.f.N.
3. Determine the degrees of freedom for the denominator, d.f.D.
4. Use Table 7 in Appendix B to find the critical value. If the hypothesis test is
a. one-tailed, use the F-table.
b. two-tailed, use the ½ F-table.
53
Example: Finding Critical F-Values
Find the critical F-value for a right-tailed test when α = 0.05, d.f.N = 6 and d.f.D = 29.
The critical value is F0 = 2.43.
54
Solution:
Example: Finding Critical F-Values
Find the critical F-value for a two-tailed test when α = 0.05, d.f.N = 4 and d.f.D = 8.
Solution:•When performing a two-tailed hypothesis test using the F-distribution, you need only to find the right-tailed critical value. •You must remember to use the ½α table.
1(0.05) 0.025
2
1
2
55
Solution: Finding Critical F-Values
½α = 0.025, d.f.N = 4 and d.f.D = 8
The critical value is F0 = 5.05.
56
Two-Sample F-Test for Variances
To use the two-sample F-test for comparing two population variances, the following must be true.
1.The samples must be randomly selected.
2.The samples must be independent.
3.Each population must have a normal distribution.
57
Two-Sample F-Test for Variances
• Test Statistic2122
sF
s
where represent the sample variances with
• The degrees of freedom for the numerator is d.f.N = n1 – 1 where n1 is the size of the sample having variance
• The degrees of freedom for the denominator is d.f.D = n2 – 1, and n2 is the size of the sample having variance
2 21 2 and s s
2 21 2.s s
21 .s
22.s
58
Two-Sample F-Test for Variances
1. Identify the claim. State the null and alternative hypotheses.
2. Specify the level of significance.
3. Identify the degrees of freedom.
4. Determine the critical value.
State H0 and Ha.
Identify .
Use Table 7 in Appendix B.
d.f.N = n1 – 1 d.f.D = n2 – 1
In Words In Symbols
59
Two-Sample F-Test for Variances
If F is in the rejection region, reject H0. Otherwise, fail to reject H0.
5. Determine the rejection region.
6. Calculate the test statistic.
7. Make a decision to reject or fail to reject the null hypothesis.
8. Interpret the decision in the context of the original claim.
2122
sF
s
In Words In Symbols
60
Example: Performing a Two-Sample F-Test
A restaurant manager is designing a system that is intended to decrease the variance of the time customers wait before their meals are served. Under the old system, a random sample of 10 customers had a variance of 400. Under the new system, a random sample of 21 customers had a variance of 256. At α = 0.10, is there enough evidence to convince the manager to switch to the new system? Assume both populations are normally distributed.
61
Solution: Performing a Two-Sample F-Test
• H0:
• Ha:
• α =
• d.f.N= d.f.D=
• Rejection Region:
• Test Statistic:
• Decision:
σ12 ≤ σ2
2
σ12 > σ2
2
0.10
9 20
0 F1.96
0.10
Because 400 > 256, 2 21 2400 and 256s s
21
22
4001.56
256
sF
s
There is not enough evidence to convince the manager to switch to the new system.
1.961.56
Fail to Reject H0
62
Example: Performing a Two-Sample F-Test
You want to purchase stock in a company and are deciding between two different stocks. Because a stock’s risk can be associated with the standard deviation of its daily closing prices, you randomly select samples of the daily closing prices for each stock to obtain the results. At α = 0.05, can you conclude that one of the two stocks is a riskier investment? Assume the stock closing prices are normally distributed.
Stock A Stock Bn2 = 30 n1 = 31
s2 = 3.5 s1 = 5.7
64
Solution: Performing a Two-Sample F-Test
• H0:
• Ha:
• ½α =
• d.f.N= d.f.D=
• Rejection Region:
• Test Statistic:
• Decision:
σ12 = σ2
2
σ12 ≠ σ2
2
0. 025
30 29
0 F2.09
0.025
Because 5.72 > 3.52, 2 2 2 21 25.7 and 3.5s s
2 21
2 22
5.72.65
3.5
sF
s
There is enough evidence to support the claim that one of the two stocks is a riskier investment.
2.092.65
Reject H0
65
Section 10.3 Summary
• Interpreted the F-distribution and used an F-table to find critical values
• Performed a two-sample F-test to compare two variances
66
Section 10.4
Analysis of Variance
67
Section 10.4 Objectives
• Use one-way analysis of variance to test claims involving three or more means
• Introduce two-way analysis of variance
68
One-Way ANOVA
One-way analysis of variance
• A hypothesis-testing technique that is used to compare means from three or more populations.
• Analysis of variance is usually abbreviated ANOVA.
• Hypotheses: H0: μ1 = μ2 = μ3 =…= μk (all population means are
equal) Ha: At least one of the means is different from the
others.
69
One-Way ANOVA
In a one-way ANOVA test, the following must be true.
1. Each sample must be randomly selected from a normal, or approximately normal, population.
2. The samples must be independent of each other.
3. Each population must have the same variance.
70
One-Way ANOVA
1. The variance between samples MSB measures the differences related to the treatment given to each sample and is sometimes called the mean square between.
2. The variance within samples MSW measures the differences related to entries within the same sample. This variance, sometimes called the mean square within, is usually due to sampling error.
Variance between samplesVariance
T
est statiwithin sa
stics
mple
71
One-Way Analysis of Variance Test
• If the conditions for a one-way analysis of variance are satisfied, then the sampling distribution for the test is approximated by the F-distribution.
• The test statistic is B
W
MSF
MS
• The degrees of freedom for the F-test are
d.f.N = k – 1 and d.f.D = N – k
where k is the number of samples and N is the sum of the sample sizes.
72
Test Statistic for a One-Way ANOVA
1. Find the mean and variance of each sample.
2. Find the mean of all entries in all samples (the grand mean).
3. Find the sum of squares between the samples.
4. Find the sum of squares within the samples.
22 ( )
1x x xx s
n n
xxN
2( ) B i iSS n x x
2( 1) W i iSS n s
In Words In Symbols
73
Test Statistic for a One-Way ANOVA
5. Find the variance between the samples.
6. Find the variance within the samples
7. Find the test statistic. B
W
MSF
MS
N1 d.f.B B
BSS SS
MSk
Dd.f.W W
WSS SS
MSN k
In Words In Symbols
74
Performing a One-Way ANOVA Test
1. Identify the claim. State the null and alternative hypotheses.
2. Specify the level of significance.
3. Identify the degrees of freedom.
4. Determine the critical value.
State H0 and Ha.
Identify .
Use Table 7 in Appendix B.
d.f.N = k – 1 d.f.D = N – k
In Words In Symbols
75
Performing a One-Way ANOVA Test
If F is in the rejection region, reject H0. Otherwise, fail to reject H0.
5. Determine the rejection region.
6. Calculate the test statistic.
7. Make a decision to reject or fail to reject the null hypothesis.
8. Interpret the decision in the context of the original claim.
B
W
MSF
MS
In Words In Symbols
76
ANOVA Summary Table
• A table is a convenient way to summarize the results in a one-way ANOVA test.
d.f.DSSWWithin
d.f.NSSBBetween
FMean squares
Degrees of freedom
Sum of squares
Variation
. .B
BN
SSMS
d f
. .W
WD
SSMS
d f
B
W
MSMS
77
Example: Performing a One-Way ANOVA
A medical researcher wants to determine whether there is a difference in the mean length of time it takes three types of pain relievers to provide relief from headache pain. Several headache sufferers are randomly selected and given one of the three medications. Each headache sufferer records the time (in minutes) it takes the medication to begin working. The results are shown on the next slide. At α = 0.01, can you conclude that the mean times are different? Assume that each population of relief times is normally distributed and that the population variances are equal.
78
Example: Performing a One-Way ANOVA
Medication 1 Medication 2 Medication 3
12 16 14
15 14 17
17 21 20
12 15 15
19
1
5614
4x 2
8517
5x 3
6616.5
4x
21 6s 2
2 8.5s 23 7s
Solution: k = 3 (3 samples)N = n1 + n2 + n3 = 4 + 5 + 4 = 13 (sum of sample sizes)
79
Solution: Performing a One-Way ANOVA
• H0:
• Ha:
• α =
• d.f.N=
• d.f.D=
• Rejection Region:
• Test Statistic:
• Decision:
μ1 = μ2 = μ3
At least one mean is different
0. 01
3 – 1 = 2
13 – 3 = 10
0 F7.56
0.01
80
Solution: Performing a One-Way ANOVA
To find the test statistic, the following must be calculated.
xxN 56 85 66 15.92
13
2
N
( ) d.f. 1
i iBB
n x xSSMS
k
2 2 24(14 15.92) 5(17 15.92) 4(16.5 15.92)
3 121.92 10.962
81
Solution: Performing a One-Way ANOVA
To find the test statistic, the following must be calculated.
2
D
( 1) d.f.
W i iW
SS n sMS
N k
(4 1)(6) (5 1)(8.5) (4 1)(7)13 3
73 7.310
B
W
MSF
MS 10.96
1.507.3
82
Solution: Performing a One-Way ANOVA
• H0:
• Ha:
• α =
• d.f.N=
• d.f.D=
• Rejection Region:
• Test Statistic:
• Decision:
μ1 = μ2 = μ3
At least one mean is different
0. 01
3 – 1 = 2
13 – 3 = 10
0 F7.56
0.01
1.50B
W
MSF
MS
There is not enough evidence at the 1% level of significance to conclude that there is a difference in the mean length of time it takes the three pain relievers to provide relief from headache pain.
1.50
Fail to Reject H0
83
Example: Using the TI-83/84 to Perform a One-Way ANOVA
Three airline companies offer flights between Corydon and Lincolnville. Several randomly selected flight times (in minutes) between the towns for each airline are shown on the next slide. Assume that the populations of flight times are normally distributed, the samples are independent, and the population variances are equal. At α = 0.01, can you conclude that there is a difference in the means of the flight times? Use a TI-83/84.
84
Example: Using the TI-83/84 to Perform a One-Way ANOVA
Airline 1 Airline 2 Airline 3
122 119 120
135 133 158
126 143 155
131 149 126
125 114 147
116 124 164
120 126 134
108 131 151
142 140 131
113 136 141
85
Solution: Using the TI-83/84 to Perform a One-Way ANOVA
• H0:
• Ha:
• Store data into lists L1, L2, and L3
μ1 = μ2 = μ3
At least one mean is different
• Decision:There is enough evidence to support the claim. You can conclude that there is a difference in the means of the flight times.
P-value < α Reject H0
86
Two-Way ANOVA
Two-way analysis of variance
• A hypothesis-testing technique that is used to test the effect of two independent variables, or factors, on one dependent variable.
87
Two-Way ANOVA
Example:
• Suppose a medical researcher wants to test the effect of gender and type of medication on the mean length of time it takes pain relievers to provide relief.
Males taking type I Females taking type I
Males taking type II Females taking type II
Males taking type III Females taking type III
GenderMale Female
I
II
III
88
Two-Way ANOVA Hypotheses
Main effect
• The effect of one independent variable on the dependent variable.
Interaction effect
• The effect of both independent variables on the dependent variable.
89
Two-Way ANOVA Hypotheses
Hypotheses for main effects:
• H0: Gender has no effect on the mean length of time it takes a pain reliever to provide relief.
• Ha: Gender has an effect on the mean length of time it takes a pain reliever to provide relief.
• H0: Type of medication has no effect on the mean length oftime it takes a pain reliever to provide relief.
• Ha: Type of medication has an effect on the mean length of time it takes a pain reliever to provide relief.
90
Two-Way ANOVA Hypotheses
Hypotheses for interaction effects:
• H0: There is no interaction effect between gender and typeof medication on the mean length of time it takes a pain reliever to provide relief.
• Ha: There is an interaction effect between gender and typeof medication on the mean length of time it takes a pain reliever to provide relief.
91
Two-Way ANOVA
• A two-way ANOVA test calculates an F-test statistic for each hypothesis.
• It is possible to reject none, one, two, or all of the null hypotheses.
• Can use a technology tool such as MINITAB to perform a two-way ANOVA test.
92
Section 10.4 Summary
• Used one-way analysis of variance to test claims involving three or more means
• Introduced two-way analysis of variance
93
Slide 4- 94© 2012 Pearson Education, Inc.
Elementary Statistics:
Picturing the World
Fifth Edition
by Larson and Farber
Chapter 10: Chi-Square Tests and the F-Distribution
Slide 10- 95© 2012 Pearson Education, Inc.
The distribution of favorite ice cream flavor is shown in the graph. The results from a random sample of college students is shown below. Find the expected frequency for the Vanilla category.
A. 33.75
B. 22.5
C. 8.1
D. 111.11
Chocolate Vanilla Strawberry Other
62 30 21 12
Slide 10- 96© 2012 Pearson Education, Inc.
The distribution of favorite ice cream flavor is shown in the graph. The results from a random sample of college students is shown below. Find the expected frequency for the Vanilla category.
A. 33.75
B. 22.5
C. 8.1
D. 111.11
Chocolate Vanilla Strawberry Other
62 30 21 12
Slide 10- 97© 2012 Pearson Education, Inc.
The distribution of favorite ice cream flavor is shown in the graph. The results from a random sample of college students is shown below. Find the critical value for α = 0.05.
A. 9.488
B. 7.815
C. 2.353
D. 1.645
Chocolate Vanilla Strawberry Other
62 30 21 12
Slide 10- 98
The distribution of favorite ice cream flavor is shown in the graph. The results from a random sample of college students is shown below. Find the critical value for α = 0.05.
A. 9.488
B. 7.815
C. 2.353
D. 1.645
Chocolate Vanilla Strawberry Other
62 30 21 12: categories
df = k-1
k number of
Slide 10- 99© 2012 Pearson Education, Inc.
The distribution of favorite ice cream flavor is shown in the graph. The results from a random sample of college students is shown below. Find the value of the test statistic.
A. 0.949
B. 0.919
C. 37.125
D. 0.010
Chocolate Vanilla Strawberry Other
62 30 21 12
Slide 10- 100
The distribution of favorite ice cream flavor is shown in the graph. The results from a random sample of college students is shown below. Find the value of the test statistic.Chocolate Vanilla Strawberry Other
62 30 21 12
Slide 10- 101© 2012 Pearson Education, Inc.
Calculate the expected frequency for the highlighted cell E2,3.
A. 20.62
B. 18.33
C. 16.92
D. 15.38
Favorite Pastime
Gender Reading Television Music
Male 12 32 19
Female 10 20 17
Slide 10- 102© 2012 Pearson Education, Inc.
Calculate the expected frequency for the highlighted cell E2,3.
A. 20.62
B. 18.33
C. 16.92
D. 15.38
Favorite Pastime
Gender Reading Television Music
Male 12 32 19
Female 10 20 17
( )( )rowTotal columnTotal
TotalAllCells
OR
Slide 10- 103© 2012 Pearson Education, Inc.
Find the critical value for α = 0.05.
A. 5.991
B. 12.592
C. 2.920
D. 1.645
Favorite Pastime
Gender Reading Television Music
Male 12 32 19
Female 10 20 17
Slide 10- 104© 2012 Pearson Education, Inc.
Find the critical value for α = 0.05.
A. 5.991
B. 12.592
C. 2.920
D. 1.645
Favorite Pastime
Gender Reading Television Music
Male 12 32 19
Female 10 20 17
( 1)( 1) (1)(2) 2df r c
Slide 10- 105© 2012 Pearson Education, Inc.
Find the value of the test statistic.
A. 16.437
B. 15.798
C. 0.751
D. 0.018
Favorite Pastime
Gender Reading Television Music
Male 12 32 19
Female 10 20 17
Slide 10- 106© 2012 Pearson Education, Inc.
Find the value of the test statistic.
A. 16.437
B. 15.798
C. 0.751
D. 0.018
Favorite Pastime
Gender Reading Television Music
Male 12 32 19
Female 10 20 17
Slide 10- 107© 2012 Pearson Education, Inc.
Find the critical F-value for the following situation:
Claim: α = 0.10
n1 = 9; n2 = 8
A. 2.75
B. 3.73
C. 3.39
D. 3.50
2 21 2
. ,s21 25 32 . ,s2
2 19 78
Slide 10- 108
Find the critical F-value for the following situation:
Claim: α = 0.10
n1 = 9; n2 = 8
A.A. 2.752.75
B. 3.73
C. 3.39
D. 3.50
2 21 2
. ,s21 25 32 . ,s2
2 19 78
d.f.N = n1 – 1 d.f.D = n2 – 1 d.f.N = 9 – 1=8 d.f.D = 8 – 1=7
Slide 10- 109© 2012 Pearson Education, Inc.
Find the value of the test statistic.
Claim: α = 0.10
n1 = 9; n2 = 8
A. 1.64
B. 1.13
C. 0.78
D. 1.28
2 21 2
. ,s21 25 32 . ,s2
2 19 78
Slide 10- 110
Find the value of the test statistic.
Claim: α = 0.10
n1 = 9; n2 = 8
A. 1.64
B. 1.13
C. 0.78
D. 1.28
2 21 2
. ,s21 25 32 . ,s2
2 19 78
Slide 10- 111© 2012 Pearson Education, Inc.
A professor wants to test if the mean score on an exam is the same in all three of his elementary statistics classes.
True or False. The appropriate hypotheses would be
H0:Ha:
A. True
B. False
1 2 3
1 2 3
Slide 10- 112© 2012 Pearson Education, Inc.
A professor wants to test if the mean score on an exam is the same in all three of his elementary statistics classes.
True or False. The appropriate hypotheses would be
H0:Ha:
A. True
B. False
1 2 3
1 2 3