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Degrees of FreedomIn Exercise 20 鈥淏lanking Out on Tests,鈥 using the 鈥渟maller of\({n_1} - 1\) and \({n_2} - 1\)鈥 for the number of degrees of freedom results in df = 15. Find the number of degrees of freedom using Formula 9-1. In general, how are hypothesis tests and confidence intervals affected by using Formula 9-1 instead of the 鈥渟maller of \({n_1} - 1\)and \({n_2} - 1\)鈥?

Short Answer

Expert verified

The value of the degrees of freedom using the formula is equal to 39.

In general, the conclusion of the hypothesis test and the result of the confidence interval are rarely affected by the choice of the method of computing the degrees of freedom.

Step by step solution

01

Given information

Data are given on the anxiety scores for the two different arrangements of questions on a test.

02

Formula of the degrees of freedom

The following formula is used to compute the value of the degrees of freedom:

\(df = \frac{{{{\left( {A + B} \right)}^2}}}{{\frac{{{A^2}}}{{{n_1} - 1}} + \frac{{{B^2}}}{{{n_2} - 1}}}}\)

Here,

\(A = \frac{{s_1^2}}{{{n_1}}}\)

\(B = \frac{{s_2^2}}{{{n_2}}}\)

03

Sample sizes, sample means, and sample standard deviations

The sample size\(\left( {{n_1}} \right)\)is equal to 25.

The sample size\(\left( {{n_2}} \right)\)is equal to 16.

The sample mean of the first sample\(\left( {{{\bar x}_1}} \right)\)is equal to:

\(\begin{array}{c}{{\bar x}_1} = \frac{{\sum\limits_{i = 1}^{{n_1}} {{x_i}} }}{{{n_1}}}\\ = \frac{{26.64 + 39.29 + ..... + 30.72}}{{25}}\\ = 27.12\end{array}\)

The sample mean of the second sample\(\left( {{{\bar x}_2}} \right)\)is equal to:

\(\begin{array}{c}{{\bar x}_2} = \frac{{\sum\limits_{i = 1}^{{n_2}} {{x_i}} }}{{{n_2}}}\\ = \frac{{33.62 + 34.02 + ..... + 32.54}}{{16}}\\ = 31.73\end{array}\)

The sample variance for the first sample \(\left( {s_1^2} \right)\) is equal to:

\(\begin{array}{c}{s_1}^2 = \frac{{\sum\limits_{i = 1}^{{n_1}} {{{({x_i} - {{\bar x}_1})}^2}} }}{{{n_1} - 1}}\\ = \frac{{{{\left( {24.64 - 27.12} \right)}^2} + {{\left( {39.29 - 27.12} \right)}^2} + ....... + {{\left( {30.72 - 27.11} \right)}^2}}}{{25 - 1}}\\ = 47.06\end{array}\)

The sample variance for the second sample \(\left( {s_2^2} \right)\)

\(\begin{array}{c}s_2^2 = \frac{{\sum\limits_{i = 1}^{{n_2}} {{{({x_i} - {{\bar x}_2})}^2}} }}{{{n_2} - 1}}\\ = \frac{{{{\left( {33.62 - 31.73} \right)}^2} + {{\left( {34.02 - 31.73} \right)}^2} + ........ + {{\left( {32.54 - 31.73} \right)}^2}}}{{16 - 1}}\\ = 18.15\end{array}\)

04

Value of the degrees of freedom

Thevalue of A is equal to:

\(\begin{array}{c}A = \frac{{{s_1}^2}}{{{n_1}}}\\ = \frac{{47.06}}{{25}}\\ = 1.88\end{array}\)

Thevalue of B is equal to:

\(\begin{array}{c}B = \frac{{s_2^2}}{{{n_2}}}\\ = \frac{{18.15}}{{16}}\\ = 1.13\end{array}\)

The value of the degrees of freedom is equal to:

\(\begin{array}{c}df = \frac{{{{\left( {A + B} \right)}^2}}}{{\frac{{{A^2}}}{{{n_1} - 1}} + \frac{{{B^2}}}{{{n_2} - 1}}}}\\ = \frac{{{{\left( {1.88 + 1.13} \right)}^2}}}{{\frac{{{{\left( {1.88} \right)}^2}}}{{25 - 1}} + \frac{{{{\left( {1.13} \right)}^2}}}{{16 - 1}}}}\\ = 38.99\\ \approx 39\end{array}\)

Thus, the value of the degrees of freedom is equal to 39.

05

Comparison

The result of the test using the method of 鈥渟maller of\(\left( {{n_1} - 1} \right)\)and\(\left( {{n_2} - 1} \right)\)鈥 to compute the degrees of freedom is approximately the same as the result obtained when the formula is used to compute the degrees of freedom.

In general, the conclusion of the hypothesis test is rarely affected by the choice of the method of computing the degrees of freedom.

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Most popular questions from this chapter

Family Heights. In Exercises 1鈥5, use the following heights (in.) The data are matched so that each column consists of heights from the same family.

1. a. Are the three samples independent or dependent? Why?

b. Find the mean, median, range, standard deviation, and variance of the heights of the sons.

c. What is the level of measurement of the sample data (nominal, ordinal, interval, ratio)?

d. Are the original unrounded heights discrete data or continuous data?

In Exercises 5鈥20, assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. (Note: Answers in Appendix D include technology answers based on Formula 9-1 along with 鈥淭able鈥 answers based on Table A-3 with df equal to the smaller of\({n_1} - 1\)and\({n_2} - 1\).)

BMI We know that the mean weight of men is greater than the mean weight of women, and the mean height of men is greater than the mean height of women. A person鈥檚 body mass index (BMI) is computed by dividing weight (kg) by the square of height (m). Given below are the BMI statistics for random samples of females and males taken from Data Set 1 鈥淏ody Data鈥 in Appendix B.

a. Use a 0.05 significance level to test the claim that females and males have the same mean BMI.

b. Construct the confidence interval that is appropriate for testing the claim in part (a).

c. Do females and males appear to have the same mean BMI?

Female BMI: n = 70, \(\bar x\) = 29.10, s = 7.39

Male BMI: n = 80, \(\bar x\) = 28.38, s = 5.37

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Are Seat Belts Effective? A simple random sample of front-seat occupants involved in car crashes is obtained. Among 2823 occupants not wearing seat belts, 31 were killed. Among 7765 occupants wearing seat belts, 16 were killed (based on data from 鈥淲ho Wants Airbags?鈥 by Meyer and Finney, Chance, Vol. 18, No. 2). We want to use a 0.05 significance level to test the claim that seat belts are effective in reducing fatalities.

a. Test the claim using a hypothesis test.

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b. Test the claim by constructing an appropriate confidence interval.

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a. Identify the null hypothesis and alternative hypothesis

b. If the p-value for test is reported as 鈥渓ess than 0.001,鈥 what should we conclude about the original claim?

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