Chapter 12 Statistical Inference: Other One-Sample Test Statistics
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Transcript of Chapter 12 Statistical Inference: Other One-Sample Test Statistics
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Chapter 12
Statistical Inference: Other One-Sample Test Statistics
I One-Sample z Test for a Population Proportion, p
A. Introduction to z Test for a Population Proportion
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1. The binomial function rule
can be used to determine the probability of r
successes in n independent trials.
2. When n is large, the normal distribution can be
used to approximate the probability of r or more
successes. The approximation is excellent if
(a) the population is at least 10 times larger than
the sample and (b) np0 > 15 and n(1 – p0) > 15,
where p0 is the hypothesized proportion.
p( X r) nCr pr ( p 1)n r
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B. z Test Statistic for a Proportion
= sample estimator of the population
proportion
p0 = hypothesized population proportion
n = size of the sample used to compute
number of successes in the random sample
number of observations in the random sample
p̂
p̂
1. npp
ppz
00
0
1
ˆ
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H0 : p p0
H0 : p p0 H0 : p p0
H1 : p p0
H1 : p p0 H1 : p p0
C. Statistical Hypotheses for a Proportion
population standard error of a proportion,
p p(1 p / n,
where p denotes the population proportion.
2. is an estimator of the nppp 00 1ˆ
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D. Computational Example
1. Student Congress believes that the proportion of
parking tickets issued by the campus police this
year is greater than last year. Last year the
proportion was p0 = .21.
2. To test the hypotheses
they obtained a random sample of n = 200
students and found that the proportion who
received tickets this year was
H0 : p .21
H1 : p .21
.27.ˆ p
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z.05 = 1.645
3. The null hypothesis can be rejected; the campus
police are issuing more tickets this year.
08.2
20021.121.
21.27.
1
ˆ
00
0
npp
ppz
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E. Assumptions of the z Test for a Population Proportion
1. Random sampling from the population
2. Binomial population
3. np0 > 15 and n(1 – p0) > 15
4. The population is at least 10 times larger than the
sample
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II One-Sample Confidence Interval for a Population Proportion, p
A. Two-Sided Confidence Interval
1. p̂ z /2p̂(1 p̂)
n p p̂ z /2
p̂(1 p̂)
n
population standard error of a proportion.
nppp ˆ1ˆˆ 2. is an estimator of the
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B. One-Sided Confidence Interval
1. Lower confidence interval
2. Upper confidence interval
p̂ zp̂(1 p̂)
n p
p p̂ zp̂(1 p̂)
n
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C. Computational Example Using the Parking Ticket Data
1. Two-sided 100(1 – .05)% = 95% confidence
interval
p̂ z /2p̂(1 p̂)
n p p̂ z /2
p̂(1 p̂)
n
.27 1.96
.27(1 .27)
200 p .27 1.96
.27(1 .27)
200
.208 p .332
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2. One-sided 100(1 – .05)% = 95% confidence
interval
p̂ zp̂(1 p̂)
n p
.27 1.645
.27(1 .27)
200 p
.218 p
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3. Comparison of the one- and two-sided confidence
intervals
Two-sided interval
One-sided interval
.30
L2 = .332 L1 = .208
.35.25.20p
.30
L1 = .218
.35.25.20p
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D. Assumptions of the Confidence Interval for a Population Proportion
1. Random sampling from the population
2. Binomial population
3. np0 > 15 and n(1 – p0) > 15
4. The population is at least 10 times larger than the
sample
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III Selecting a Sample Size, n
A. Information needed to specify n
1. Acceptable margin of error, m*, in
estimating p. m* is usually between .02
and .04.
2. Acceptable confidence level: usually .95
for z.05 or z.05/2
3. Educated guess, denoted by p*, of the
likely value of p
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B. Computational Example for the Traffic Ticket Data
1. One-sided confidence interval, let m* = .04,
z.05 = 1.645, and p* = .27
n z.05
m *
2
p * (1 p*)
n
1.645
.04
2
.27(1 .27) 333
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C. Conservative Estimate of the Required Sample Size
1. If a researcher is unable to provide an educated
guess for m*, a conservative estimate of n is
obtained by letting p* = .50.
n z.05
m *
2
p * (1 p*)
n
1.645
.04
2
.50(1 .50) 423
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IV One-Sample t Test for Pearson’s Population Correlation
A. t Test for 0 = 0 (Population Correlation Is Equal to Zero)
1. Values of | r | that lead to rejecting one of the
following null hypotheses are obtained from
Appendix Table D.6.
H0 : 0
H1 : 0
H0 : 0
H1 : 0
H0 : 0
H1 : 0
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Appendix Table D.6. Critical Values of the Pearson r
Degrees of
Freedom
n 2
Level of Significance for a One-Tailed Test
Level of Significance for a Two-Tailed Test
.05
.05
.025
.10
.01
.02 .01
.005
8 0.549 0.632 0.716 0.765
10 0.497 0.576 0.658 0.708
20 0.360 0.423 0.492 0.537
30 0.296 0.349 0.409 0.449
60 0.211 0.250 0.274 0.325
100 0.164 0.195 0.230 0.254
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1. Table D.6 is based on the t distribution and t
statistic
B. Computational Example Using the Girl’s Basketball Team Data (Chapter 5)
1. r = .84, n = 10, and r.05, 8 = .549
2. r.05, 8 = .549 is the one-tailed critical value from
Appendix Table D.6.
H0 : 0,
freedom of degrees 2 with 1
22
n
r
nrt
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1. Because r = .84 > r.05, 8 = .549, reject the null
hypothesis and conclude that player’s height and
weight are positively correlated.
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C. Assumptions of the t Test for Pearson’s Population Correlation Coefficient
1. Random sampling
2. Population distributions of X and Y are
approximately normal.
3. The relationship between X and Y is linear.
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4. The distribution of Y for any value of X is
normal with variance that does not depend on the
X value selected and vice versa.
V One-Sample Confidence Interval for Pearson’s Population Correlation
A. Fisher’s r to Z Transformation
1. r is bounded by –1 and +1; Fisher’s Z can
exceed –1 and +1.
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Appendix Table D.7 Transformation of r to Z
0.200 0.203 0.400 0.424 0.600 0.693 0.800 1.099
0.225 0.229 0.425 0.454 0.625 0.733 0.825 1.172
0.250 0.255 0.450 0.485 0.650 0.775 0.850 1.256
0.275 0.282 0.475 0.517 0.675 0.820 0.875 1.354
0.300 0.310 0.500 0.549 0.700 0.867 0.900 1.472
0.325 0.337 0.525 0.583 0.725 0.918 0.925 1.623
0.350 0.365 0.550 0.618 0.750 0.973 0.950 1.832
0.375 0.394 0.575 0.655 0.775 1.033 0.975 2.185
r r r r Z Z Z Z
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B. Two Sided Confidence Interval for Using Fisher’s Z Transformation
1. Begin by transforming r to Z. Then obtain
a confidence interval for ZPop
Z z.05/2
1
n 3 ZPop Z z.05/2
1
n 3
2. A confidence interval for r is obtained by
transforming the lower and upper confidence
limits for ZPop into r using Appendix Table D.6 .
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C. One-Sided Confidence Interval for
1. Lower confidence limit
Z z.05
1
n 3 ZPop
2. Upper confidence limit
ZPop Z z.05
1
n 3
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D. Computational Example Using the Girl’s Basketball Team Data (Chapter 5)
1. r = .84, n = 10, and Z = 1.221
Z z.05
1
n 3 ZPop
1.221 1.645
1
10 3 ZPop
.599 ZPop
.54
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3. A confidence interval can be used to test
hypotheses for any hypothesized value of 0.
For example, any hypothesis for which 0 ≤ .54
could be rejected.
.60
L1 = .54
.65.55.50
2. Graph of the confidence interval for
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E. Assumptions of the Confidence Interval for Pearson’s Correlation Coefficient
1. Random sampling
2. is not too close to 1 or –1
3. Population distributions of X and Y are
approximately normal
4. The relationship between X and Y is linear
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5. The distribution of Y for any value of X is
normal with variance that does not depend on the
X value selected and vice versa.
VI Practical Significance of Pearson’s Correlation
A. Cohen’s Guidelines for Effect Size
r = .10 is a small strength of association
r = .30 is a medium strength of association
r = .50 is a large strength of association