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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

The accompanying summary data on total cholesterol level (mmol/l) was obtained from a sample of Asian postmenopausal women who were vegans and another sample of such women who were omnivores (鈥淰egetarianism, Bone Loss, and Vitamin D: A Longitudinal Study in Asian Vegans and Non-Vegans,鈥 European J. of Clinical Nutr., 2012: 75鈥82)

Diet sample sample sample

Size mean SD

\(\overline {\underline {\begin{array}{*{20}{l}}{ Vegan }&{88}&{5.10}&{1.07}\\{ Omnivore }&{93}&{5.55}&{1.10}\\{}&{}&{}&{}\end{array}} } \)

Calculate and interpret a \(99\% \) \(CI\) for the difference between population mean total cholesterol level for vegans and population mean total cholesterol level for omnivores (the cited article included a \(95\% \)\(CI\)). (Note: The article described a more sophisticated statistical analysis for investigating bone density loss taking into account other characteristics (鈥渃ovariates鈥) such as age, body weight, and various nutritional factors; the resulting CI included 0, suggesting no diet effect.

As the population ages, there is increasing concern about accident-related injuries to the elderly. The article "Age and Gender Differences in Single-Step Recovery from a Forward Fall" (J. of Gerontology, \(1999: M44 - M50\)) reported on an experiment in which the maximum lean angle-the farthest a subject is able to lean and still recover in one step-was determined for both a sample of younger females (\(21 - 29\)years) and a sample of older females (\(67 - 81\)years). The following observations are consistent with summary data given in the article:

YF:\(29,34,33,27,28,32,31,34,32,27\)

OF:\(18,15,23,13,12\)

Does the data suggest that true average maximum lean angle for older females is more than\(10\)degrees smaller than it is for younger females? State and test the relevant hypotheses at significance level . \(10\).

Antipsychotic drugs are widely prescribed for conditions such as schizophrenia and bipolar disease. The article "Cardiometabolic Risk of SecondGeneration Antipsychotic Medications During First-Time Use in Children and Adolescents"\((J\). of the Amer. Med. Assoc., 2009) reported on body composition and metabolic changes for individuals who had taken various antipsychotic drugs for short periods of time.

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).

b. The article also reported that for a sample of 36 individuals who had taken quetiapine, the sample mean cholesterol level change and estimated standard error were\(9.05\)and\(4.256\), respectively. Making any necessary assumptions about the distribution of change in cholesterol level, does the choice of significance level impact your conclusion as to whether true average cholesterol level increases? Explain. (Note: The article included a\(P\)-value.)

c. For the sample of 45 individuals who had taken olanzapine, the article reported\(99\% CI\)as a\(95\% \)CI for true average weight gain\((kg)\). What is a $\(99\% CI\)?

1. An article in the November \(1983\) Consumer Reports compared various types of batteries. The average lifetimes of Duracell Alkaline \(AA\)batteries and Eveready Energizer Alkaline \(AA\) batteries were given as \(4.1\) hours and \(4.5\) hours, respectively. Suppose these are the population average lifetimes.

a. Let \(\bar X\) be the sample average lifetime of \(100\) Duracell batteries and \(\bar Y\) be the sample average lifetime of \(100\) Eveready batteries. What is the mean value of \(\bar X - \bar Y\) (i.e., where is the distribution of \({\bf{\bar X - \bar Y}}\) centered)? How does your answer depend on the specified sample sizes?

b. Suppose the population standard deviations of lifetime are \(1.8\) hours for Duracell batteries and \(2.0\) hours for Eveready batteries. With the sample sizes given in part (a), what is the variance of the statistic \(\bar X - \bar Y\), and what is its standard deviation?

c. For the sample sizes given in part (a), draw a picture of the approximate distribution curve of \(\bar X - \bar Y\) (include a measurement scale on the horizontal axis). Would the shape of the curve necessarily be the same for sample sizes of \(10\) batteries of each type? Explain

The accompanying data consists of prices (\$) for one sample of California cabernet sauvignon wines that received ratings of 93 or higher in the May 2013 issue of Wine Spectator and another sample of California cabernets that received ratings of 89 or lower in the same issue.

\(\begin{array}{*{20}{c}}{ \ge 93:}&{100}&{100}&{60}&{135}&{195}&{195}&{}\\{}&{125}&{135}&{95}&{42}&{75}&{72}&{}\\{ \le 89:}&{80}&{75}&{75}&{85}&{75}&{35}&{85}\\{}&{65}&{45}&{100}&{28}&{38}&{50}&{28}\end{array}\)

Assume that these are both random samples of prices from the population of all wines recently reviewed that received ratings of at least 93 and at most 89 , respectively.

a. Investigate the plausibility of assuming that both sampled populations are normal.

b. Construct a comparative boxplot. What does it suggest about the difference in true average prices?

c. Calculate a confidence interval at the\(95\% \)confidence level to estimate the difference between\({\mu _1}\), the mean price in the higher rating population, and\({\mu _2}\), the mean price in the lower rating population. Is the interval consistent with the statement "Price rarely equates to quality" made by a columnist in the cited issue of the magazine?

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