Two populations are described in each of the following cases. In which cases would it be appropriate to apply the small-sample t-test to investigate the difference between the population means?

a.Population 1: Normal distribution with variance σ12. Population 2: Skewed to the right with varianceσ22=σ12.

b. Population 1: Normal distribution with variance σ12. Population 2: Normal distribution with variance σ22σ12.

c. Population 1: Skewed to the left with variance σ12. Population 2: Skewed to the left with varianceσ22=σ12.

d. Population 1: Normal distribution with varianceσ12 . Population 2: Normal distribution with varianceσ22=σ12 .

e. Population 1: Uniform distribution with varianceσ12 . Population 2: Uniform distribution with variance σ22=σ12.

Short Answer

Expert verified

A t-test is an inference statistic that is used to see if there is a substantial difference in the means of two categories that are connected in some way.

Step by step solution

01

Step-by-Step Solution Step 1: Definition of t-test.

The t-teststatistical examination was used to contrast the two groups' means. It assists us in determining whether or not there is a substantial disparity between the means of the two groups. When doing a t-test, basic assumptions include the measurement scale, accidental selection, normality of distribution of data, the sufficiency of sample size, as well as equality of variation in standard deviation.

The formula of the t-test is:

t=x¯μσn

02

(a) State whether the t-test is appropriate when Population 1: Normal distribution with variance σ12 . Population 2: Skewed to the right with variance σ22=σ12 .

Population 2 is not normally distributed, so it will not be appropriate to apply the small-Sample t-test to investigate the difference between the population means.

03

(b) State whether the t-test is appropriate when Population 1: Normal distribution with variance σ12 . Population 2: Normal distribution with variance σ22≠σ12 .

The variances of Population1 and Population 2 are unequal, so it will not be appropriate to apply the small-Sample t-test to investigate the difference between the population means.

04

(c) State whether the t-test is appropriate when Population 1: Skewed to left with variance σ12 . Population 2: Skewed to left with variance σ22=σ12 .

The Populations are not normally distributed, so it will not be appropriate to apply the small-Sample t-test to investigate the difference between the population means.

05

(d) State whether the t-test is appropriate when Population 1: Normal distribution with variance σ12. Population 2: Normal distribution with variance σ22=σ12.

The Populations are normally distributed and the variances are also equal, so it will be appropriate to apply the small-Sample t-test to investigate the difference between the population means.

06

(e) State whether the t-test is appropriate when Population 1: Uniform distribution with variance σ12. Population 2: Uniform distribution with variance σ22=σ12.

The Populations are not normally distributed, so it will not be appropriate to apply the small-Sample t-test to investigate the difference between the population means.

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Most popular questions from this chapter

Question: Impact of race on football card values. Refer to the Electronic Journal of Sociology (2007) study of the Impact of race on the value of professional football players’ “rookie” cards, Exercise 12.72 (p. 756). Recall that the sample consisted of 148 rookie cards of NFL players who were inducted into the Football Hall of Fame (HOF). The researchers modelled the natural logarithm of card price (y) as a function of the following independent variables:

Race:x1=1ifblack,0ifwhiteCardavailability:x2=1ifhigh,0iflowCardvintage:x3=yearcardprintedFinalist:x4=naturallogarithmofnumberoftimesplayeronfinalHOFballotPosition-QB::x5=1ifquarterback,0ifnotPosition-RB:x7=1ifrunningback,0ifnotPosition-WR:x8=1ifwidereceiver,0ifnotPosition-TEx9=1iftightend,0ifnotPosition-DL:x10=1ifdefensivelineman,0ifnotPosition-LB:x11=1iflinebacker,0ifnotPosition-DB:x12=1ifdefensiveback,0ifnot

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where,

y = Daily admission

x1 = 1 if weekend

0 otherwise

X2 = 1 if sunny

0 if overcast

X3 = predicted daily high temperature (°F)

These data were recorded for a random sample of 30 days, and a regression model was fitted to the data.

The least squares analysis produced the following results:

with

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First 9

Letters: A–I

Last 9

Letters: R–Z

Sample size

25

25

Mean response time (minutes)

25.08

19.38

Standard deviation (minutes)

10.41

7.12

Source: Based on K. A. Carlson and J. M. Conrad, “The Last Name Effect: How Last Name Influences Acquisition Timing,” Journal of Consumer Research, Vol. 38, No. 2, August 2011.

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Test and CI for two Variances: Content vs Site

Method

Null hypothesis α1α2=1

Alternative hypothesis α1α21

F method was used. This method is accurate for normal data only.

Statistics

Site N St Dev Variance 95% CI for St Devs

1 25 3.067 9.406 (2.195,4.267)

2 25 3.339 11.147 (2.607,4.645)

Ratio of standard deviation =0.191

Ratio of variances=0.844

95% Confidence Intervals

Method CI for St Dev Ratio CI Variance Ratio

F (0.610, 1.384) (0.372, 1.915)

Tests

Method DF1 DF2 Test statistic p-value

F 24 24 0.84 0.681

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