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Statistics. Inferences About Population Variances. Contents. Inference about a Population Variance. Inferences about the Variances of Two Populations. STATISTICS in PRACTICE. The U.S. General Accounting Office (GAO) evaluators studied a Department of Interior program

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statistics

Statistics

Inferences About Population Variances

contents
Contents
  • Inference about a Population Variance
  • Inferences about the Variances of Two Populations
statistics in practice
STATISTICSin PRACTICE
  • The U.S. General Accounting

Office (GAO) evaluators studied

a Department of Interior program

established to help clean up the

nation’s rivers and lakes.

  • The audits reviewed sample data on the oxygen content, the pH level, and the amount of suspended solids in the effluent.
statistics in practice1
STATISTICSin PRACTICE
  • The hypothesis test was conducted about the variance in pH level for the population of effluent. The population variance in pH level expected at a properly functioning plant.
  • In this chapter you will learn how to conduct statistical inferences about the variances of one and two populations.
inferences about a population variance
Inferences About a Population Variance
  • Chi-Square Distribution(2)
  • Interval Estimation of 2
  • Hypothesis Testing
chi square distribution
Chi-Square Distribution
  • The chi-square distribution is the sum of
  • squared standardized normal random
  • variables such as
  • The chi-square distribution is based on
  • samplingfrom a normal population.
chi square distribution1
Chi-Square Distribution
  • Probability density function
  • where
  • Mean: k
  • Variance: 2k
chi square distribution2
Chi-Square Distribution
  • The sampling distribution of (n - 1)s2/ 2
  • has a chi-square distribution whenever a simple
  • random sample of sizenis selected from a
  • normal population.
  • We can use the chi-square distribution to
  • developinterval estimates and conduct hypothesis
  • tests about a population variance.
slide9

Examples of Sampling Distribution of (n - 1)s2/ 2

With 2 degrees

of freedom

With 5 degrees

of freedom

With 10 degrees

of freedom

0

slide10

We will use the notation to denote the value for the chi-square distribution that provides an area of a to the right of the stated value.

  • For example, there is a .95 probability of obtaining a (chi-square) value such that

Chi-Square Distribution

chi square distribution3
Chi-Square Distribution
  • A Chi-Square Distribution with 19 Degrees of Freedom
chi square distribution4
Chi-Square Distribution
  • Selected Values form the Chi-Square Distribution Table
slide14

Interval Estimation of 2

.025

.025

95% of the

possible 2 values

2

0

slide15

Interval Estimation of 2

  • There is a (1 –a) probability of obtaining a c2value such that
  • Substituting (n– 1)s2/s 2 for the c2 we get
  • Performing algebraic manipulation we get
interval estimation of 2
Interval Estimation of 2
  • Interval Estimate of a Population Variance

where thevalues are based on a chi-square

distribution withn - 1 degrees of freedom and

where 1 -is the confidence coefficient.

interval estimation of
Interval Estimation of 
  • Interval Estimate of a Population Standard Deviation

Taking the square root of the upper and lower

limits of the variance interval provides the

confidenceinterval for the population standard

deviation.

interval estimation of 21
Interval Estimation of 2
  • Example: Buyer’s Digest (A)

Buyer’s Digest rates thermostats manufactured

for home temperature control. In a recent test, 10

thermostats manufactured by The rmoRitewere

selected and placed in a test room that

was maintained at a temperature of 68oF.

The temperature readings of the ten thermostats

are shown on the next slide.

slide19

Interval Estimation of 2

  • Example: Buyer’s Digest (A)

We will use the 10 readings below to

develop a 95% confidence interval

estimate of the population variance.

Thermostat1 2 3 4 5 6 7 8 9 10

Temperature 67.4 67.8 68.2 69.3 69.5 67.0 68.1 68.6 67.9 67.2

slide20

Our value

Interval Estimation of 2

For n - 1 = 10 - 1 = 9 d.f. and a = .05

Selected Values from the Chi-Square Distribution Table

slide21

Interval Estimation of 2

For n - 1 = 10 - 1 = 9 d.f. and a =.05

.025

Area in

Upper Tail

= .975

2

0

2.700

slide22

Our value

Interval Estimation of 2

For n - 1 = 10 - 1 = 9 d.f. and a = .05

Selected Values from the Chi-Square Distribution Table

slide23

Interval Estimation of 2

n - 1 = 10 - 1 = 9 degrees of freedom and a = .05

Area in Upper

Tail = .025

.025

2

0

19.023

2.700

interval estimation of 22
Interval Estimation of 2
  • Sample variance s2 provides a point estimate of  2.
  • A 95% confidence interval for the population variance is given by:

.33 <2 < 2.33

hypothesis testing about a population variance

whereis the hypothesized value

for the population variance

Hypothesis TestingAbout a Population Variance
  • Left-Tailed Test
  • Hypotheses
  • Test Statistic
slide26

RejectH0if

whereis based on a chi-square

distribution withn- 1 d.f.

Hypothesis TestingAbout a Population Variance

  • Left-Tailed Test (continued)
  • Rejection Rule

Critical value approach:

p-Value approach:

Reject H0if p-value<a

slide27

whereis the hypothesized value

for the population variance

Hypothesis TestingAbout a Population Variance

  • Right-Tailed Test
  • Hypotheses
  • Test Statistic
slide28

RejectH0if

whereis based on a chi-square

distribution withn - 1 d.f.

Hypothesis TestingAbout a Population Variance

  • Right-Tailed Test (continued)
  • Rejection Rule

Critical value approach:

RejectH0if p-value<a

p-Value approach:

slide29

whereis the hypothesized value

for the population variance

Hypothesis TestingAbout a Population Variance

  • Two-Tailed Test
  • Hypotheses
  • Test Statistic
slide30

RejectH0if

whereandare based on a

chi-square distribution withn - 1 d.f.

Hypothesis TestingAbout a Population Variance

  • Two-Tailed Test (continued)
  • Rejection Rule

Critical value approach:

p-Valueapproach:

RejectH0ifp-value<a

hypothesis testing about a population variance1
Hypothesis TestingAbout a Population Variance
  • Example: Buyer’s Digest (B)

Recall that Buyer’s Digest is rating

ThermoRite thermostats. Buyer’s Digest

gives an “acceptable” rating to a thermo-

stat with a temperature variance of 0.5

or less.

We will conduct a hypothesis test (with

a= .10) to determine whether the ThermoRite

thermostat’s temperature variance is “acceptable”.

slide32

Hypothesis TestingAbout a Population Variance

  • Example: Buyer’s Digest (B)

Using the 10 readings, we will

conduct a hypothesis test (witha= .10)

to determine whether the ThermoRite

thermostat’s temperature variance is

“acceptable”.

Thermostat1 2 3 4 5 6 7 8 9 10

Temperature67.4 67.8 68.2 69.3 69.5 67.0 68.1 68.6 67.9 67.2

hypothesis testing about a population variance2
Hypothesis TestingAbout a Population Variance
  • Hypotheses
  • Rejection Rule

RejectH0ifc 2>14.684

slide34

Our value

Hypothesis TestingAbout a Population Variance

For n - 1 = 10 - 1 = 9 d.f. and a = .10

Selected Values from the Chi-Square

Distribution Table

slide35

Hypothesis TestingAbout a Population Variance

  • Rejection Region

Area in Upper

Tail = .10

2

14.684

0

Reject H0

hypothesis testing about a population variance3
Hypothesis TestingAbout a Population Variance
  • Test Statistic

The sample variances2= 0.7

  • Conclusion

Becausec2= 12.6 is less than 14.684, we cannotrejectH0. The sample variances2= .7 is insufficientevidence to conclude that the temperature variancefor ThermoRitethermostats is unacceptable.

using excel to conduct a hypothesis test about a population variance
Using Excel to Conduct a Hypothesis Testabout a Population Variance
  • Using the p-Value
  • The rejection region for the ThermoRite
  • thermostat example is in the upper tail; thus, the
  • appropriate p-value is less than .90 (c2 = 4.168)
  • and greater than .10 (c2 = 14.684).
  • Because the p –value > a = .10, we cannot
  • reject the null hypothesis.
  • The sample variance of s2 = .7 is insufficient
  • evidence to conclude that the temperature
  • variance is unacceptable (>.5).
slide39

Hypothesis Testing About theVariances of Two Populations

  • One-Tailed Test
  • Hypotheses

Denote the population providing the

larger sample variance as population 1.

  • Test Statistic
slide40

Hypothesis Testing About theVariances of Two Populations

  • One-Tailed Test (continued)
  • Rejection Rule

Critical value approach:

Reject H0if F>F

where the value ofFis based on an

Fdistribution withn1- 1 (numerator)

andn2 - 1 (denominator) d.f.

p-Valueapproach:

RejectH0 ifp-value<a

slide41

Hypothesis Testing About theVariances of Two Populations

  • Two-Tailed Test
  • Hypotheses

Denote the population providing the

larger sample variance as population 1.

  • Test Statistic
slide42

Hypothesis Testing About theVariances of Two Populations

  • Two-Tailed Test (continued)
  • Rejection Rule

Critical value approach:

RejectH0if F>F/2

where the value ofF/2 is based on an

Fdistribution withn1- 1 (numerator)

andn2 - 1 (denominator) d.f.

p-Valueapproach:

RejectH0ifp-value<a

slide44

F Distribution

  • Probability density function
  • where
slide45

F Distribution

  • mean
  • for d2 > 2
  • Variance
  • ford2 > 2
f distribution
F Distribution
  • Selected Values From the F Distribution Table
slide49

Hypothesis Testing About theVariances of Two Populations

  • Example: Buyer’s Digest (C)

Buyer’s Digest has conducted the

same test, as was described earlier, on

another 10 thermostats, this time

manufactured by TempKing. The

temperature readings of the ten

thermostats are listed on the next slide.

slide50

Hypothesis Testing About theVariances of Two Populations

  • Example: Buyer’s Digest (C)

We will conduct a hypothesis test with= .10 to see if the variances are equal for ThermoRite’s thermostats and TempKing’s thermostats.

slide51

Hypothesis Testing About theVariances of Two Populations

  • Example: Buyer’s Digest (C)

ThermoRite Sample

Thermostat 1 2 3 4 5 6 7 8 9 10

Temperature 67.4 67.8 68.2 69.3 69.5 67.0 68.1 68.6 67.9 67.2

TempKing Sample

Thermostat 1 2 3 4 5 6 7 8 9 10

Temperature 67.7 66.4 69.2 70.1 69.5 69.7 68.1 66.6 67.3 67.5

slide52

Hypothesis Testing About theVariances of Two Populations

  • Hypotheses

(TempKing and ThermoRite

thermostats have thesame

temperature variance)

(Their variances are not equal)

  • Rejection Rule
  • TheFdistribution table (on next slide) shows that withwith= .10, 9 d.f. (numerator), and 9 d.f. (denominator),F.05= 3.18.

Reject H0 if F> 3.18

slide53

Hypothesis Testing About theVariances of Two Populations

Selected Values from the F Distribution Table

slide54

= 1.768/.700 = 2.53

Hypothesis Testing About theVariances of Two Populations

  • Test Statistic
  • TempKing’s sample variance is 1.768
  • ThermoRite’s sample variance is .700
slide55

Hypothesis Testing About theVariances of Two Populations

  • Conclusion
  • We cannot rejectH0. F= 2.53 < F.05= 3.18.
  • There is insufficient evidence to conclude that
  • the population variances differ for the two
  • thermostat brands.
slide56

Hypothesis Testing About theVariances of Two Populations

  • Determining and Using the p-Value

Area in Upper Tail .10 .05 .025 .01

FValue (df1 = 9, df2 = 9) 2.44 3.18 4.03 5.35

  • BecauseF= 2.53 is between 2.44 and 3.18, the area
  • in the upper tail of the distribution is between .10
  • and .05.
  • But this is a two-tailed test; after doubling the
  • upper-tail area, thep-valueis between .20 and .10.
  • Becausea= .10, we have p-value > aand therefore
  • we cannot reject the null hypothesis.
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