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The following observations are on time (h) for a AA 1.5volt alkaline battery to reach a \(0.8\)voltage ("Comparing the Lifetimes of Two Brands of Batteries," J. of Statistical Educ., 2013, online):

\(\begin{array}{*{20}{l}}{ Energizer: }&{8.65}&{8.74}&{8.91}&{8.72}&{8.85}\\{ Ultracell: }&{8.76}&{8.81}&{8.81}&{8.70}&{8.73}\\{ Energizer: }&{8.52}&{8.62}&{8.68}&{8.86}&{}\\{ Ultracell: }&{8.76}&{8.68}&{8.64}&{8.79}&{}\end{array}\)

Normal probability plots support the assumption that the population distributions are normal. Does the data suggest that the variance of the Energizer population distribution differs from that of the Ultra cell population distribution? Test the relevant hypotheses using a significance level of .05. (Note: The two-sample \(t\)test for equality of population means gives a \(P - \)value of .763.) The Energizer batteries are much more expensive than the Ultra cell batteries. Would you pay the extra money?

Short Answer

Expert verified

There is sufficient evidence to support the claim that the variance of the Energizer population distribution differs from that of the Ultra cell population distribution. I would not pay the extra money, because the Energizer batteries contain also a lot more variability.

Step by step solution

01

Given information

The sample size

\(\begin{array}{l}{n_1} = {n_2} = 9\\\alpha = 0.05\end{array}\)

The mean is the sum of all values divided by the number of values:

\(\begin{array}{c}{{\bar x}_1} = \frac{{8.65 + 8.74 + 8.91 + \ldots + 8.62 + 8.68 + 8.86}}{9}\\ \approx 8.7278\end{array}\)

\(\begin{array}{c}{{\bar x}_2} = \frac{{8.76 + 8.81 + 8.81 + \ldots + 8.68 + 8.64 + 8.79}}{9}\\ \approx 8.7422\end{array}\)

The variance is the sum of squared deviations from the mean divided by \(n - 1.\)The standard deviation is the square root of the variance:

\(\begin{array}{c}{s_1} = \sqrt {\frac{{{{(8.65 - 8.7278)}^2} + \ldots . + {{(8.86 - 8.7278)}^2}}}{{9 - 1}}} \\ \approx 0.1270\end{array}\)

\(\begin{array}{c}{s_2} = \sqrt {\frac{{{{(8.76 - 8.7422)}^2} + \ldots . + {{(8.79 - 8.7422)}^2}}}{{9 - 1}}} \\ \approx 0.0595\end{array}\)

02

Writing hypothesis

Minimum of two steps are required.

Given claim: differs

The claim is either the null hypothesis or the alternative hypothesis. The null hypothesis and the alternative hypothesis state the opposite of each other. The null hypothesis needs to contain an equality.

\(\begin{array}{l}{H_0}:\sigma _1^2 = \sigma _2^2\\{H_a}:\sigma _1^2 \ne \sigma _2^2\end{array}\)

03

Finding test statistic

Compute the value of the test statistic:

\(\begin{array}{c}F = \frac{{s_1^2}}{{s_2^2}}\\ = \frac{{{{0.1270}^2}}}{{{{0.0595}^2}}}\\ \approx 4.556\end{array}\)

04

Finding P value

The P-value is the probability of obtaining the value of the test statistic, or a value more extreme, assuming that the null hypothesis is true. The Pvalue is the number (or interval) in the column title of the F-distribution table containing the $F$-value in the column \(dfn = {n_1} - 1 = 9 - 1 = 8\)and in the row \(d{\rm{ }}f{\rm{ }}d = {n_2} - 1 = 9 - 1 = 8{\rm{ }}:\)

\(0.010 < P < 0.050\)

05

Decision rule and Conclusion

If the P-value is less than the significance level, then reject the null hypothesis.

\(P < 0.05 \Rightarrow {\rm{ Reject }}{H_0}\)

There is sufficient evidence to support the claim that the variance of the Energizer population distribution differs from that of the Ultra cell population distribution.

I would not pay the extra money, because the Energizer batteries contain also a lot more variability.

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Most popular questions from this chapter

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Motor

1 2 3 4 5 6

Commutator 211 273 305 258 270 209

Pinion 266 278 259 244 273 236

Motor

7 8 9 10 11 12

Commutator 223 288 296 233 262 291

Pinion 290 287 315 242 288 242

Motor

13 14 15 16 17

Commutator 278 275 210 272 264

Pinion 278 208 281 274 268

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a. The sample of 41 individuals who had taken aripiprazole had a mean change in total cholesterol (mg/dL) of\(3.75\), and the estimated standard error\({s_D}/\sqrt n \)was\(3.878\). Calculate a confidence interval with confidence level approximately\(95\% \)for the true average increase in total cholesterol under these circumstances (the cited article included this CI).

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\(\begin{array}{*{20}{c}}{ Type }&{}&N&{ Mean }&{ Std Dev }&{ Std Error }\\1&{20}&{17.49900000}&{0.55012821}&{0.12301241}&{}\\2&{20}&{16.90000000}&{0.48998389}&{0.10956373}&{}\\{}&{ Variances }&T&{ DF }&{Prob > |T|}&{}\\{ Unequal }&{3.6362}&{37.5}&{0.0008}&{}&{}\\{}&{ Equal }&{3.6362}&{38.0}&{0.0008}&{}\end{array}\)

Two different types of alloy, A and B, have been used to manufacture experimental specimens of a small tension link to be used in a certain engineering application. The ultimate strength (ksi) of each specimen was determined, and the results are summarized in the accompanying frequency distribution.

\(A\)

\({\bf{B}}\)

\(26 - < 30\)

6

4

\(30 - < 34\)

12

9

\(34 - < 38\)

15

19

\(38 - < 42\)

7

10

\(m = 40\)

\(m = 42\)

Compute a 95 % CI for the difference between the true proportions of all specimens of alloys A and B that have an ultimate strength of at least\(34ksi\).

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