var MathJaxMap = {}; MathJaxMap['math_1'] = ''; MathJaxMap['math_2'] = ''; MathJaxMap['math_3'] = ''; MathJaxMap['math_4'] = ''; MathJaxMap['math_5'] = ''; MathJaxMap['math_6'] = ''; MathJaxMap['math_7'] = ''; MathJaxMap['math_8'] = ''; MathJaxMap['math_9'] = ''; MathJaxMap['math_10'] = ''; MathJaxMap['math_11'] = ''; MathJaxMap['math_12'] = ''; MathJaxMap['math_13'] = ''; MathJaxMap['math_14'] = ''; MathJaxMap['math_15'] = ''; MathJaxMap['math_16'] = ''; MathJaxMap['math_17'] = ''; MathJaxMap['math_18'] = ''; MathJaxMap['math_19'] = ''; MathJaxMap['math_20'] = ''; MathJaxMap['math_21'] = ''; MathJaxMap['math_22'] = ''; MathJaxMap['math_23'] = ''; MathJaxMap['math_24'] = ''; MathJaxMap['math_25'] = ''; MathJaxMap['math_26'] = ''; MathJaxMap['math_27'] = ''; MathJaxMap['math_28'] = ''; MathJaxMap['math_29'] = ''; MathJaxMap['math_30'] = ''; MathJaxMap['math_31'] = ''; MathJaxMap['math_32'] = ''; MathJaxMap['math_33'] = ''; MathJaxMap['math_34'] = ''; MathJaxMap['math_35'] = ''; MathJaxMap['math_36'] = ''; MathJaxMap['math_37'] = ''; MathJaxMap['math_38'] = ''; MathJaxMap['math_39'] = ''; MathJaxMap['math_40'] = ''; MathJaxMap['math_41'] = ''; MathJaxMap['math_42'] = ''; MathJaxMap['math_43'] = ''; MathJaxMap['math_44'] = ''; MathJaxMap['math_45'] = ''; MathJaxMap['math_46'] = ''; MathJaxMap['math_47'] = ''; MathJaxMap['math_48'] = ''; MathJaxMap['math_49'] = ''; MathJaxMap['math_50'] = ''; MathJaxMap['math_51'] = ''; MathJaxMap['math_52'] = ''; MathJaxMap['math_53'] = ''; MathJaxMap['math_54'] = ''; MathJaxMap['math_55'] = ''; MathJaxMap['math_56'] = ''; MathJaxMap['math_57'] = ''; MathJaxMap['math_58'] = ''; MathJaxMap['math_59'] = ''; MathJaxMap['math_60'] = ''; MathJaxMap['math_61'] = ''; MathJaxMap['math_62'] = ''; MathJaxMap['math_63'] = ''; MathJaxMap['math_64'] = ''; MathJaxMap['math_65'] = ''; MathJaxMap['math_66'] = ''; MathJaxMap['math_67'] = ''; MathJaxMap['math_68'] = ''; MathJaxMap['math_69'] = ''; MathJaxMap['math_70'] = ''; MathJaxMap['math_71'] = ''; MathJaxMap['math_72'] = ''; MathJaxMap['math_73'] = ''; MathJaxMap['math_74'] = ''; MathJaxMap['math_75'] = ''; MathJaxMap['math_76'] = ''; MathJaxMap['math_77'] = ''; MathJaxMap['math_78'] = ''; MathJaxMap['math_79'] = ''; MathJaxMap['math_80'] = ''; MathJaxMap['math_81'] = ''; MathJaxMap['math_82'] = ''; MathJaxMap['math_83'] = ''; MathJaxMap['math_84'] = ''; MathJaxMap['math_85'] = ''; MathJaxMap['math_86'] = ''; MathJaxMap['math_87'] = ''; MathJaxMap['math_88'] = ''; MathJaxMap['math_89'] = ''; MathJaxMap['math_90'] = ''; MathJaxMap['math_91'] = ''; MathJaxMap['math_92'] = ''; MathJaxMap['math_93'] = ''; MathJaxMap['math_94'] = ''; MathJaxMap['math_95'] = ''; MathJaxMap['math_96'] = ''; MathJaxMap['math_97'] = ''; MathJaxMap['math_98'] = ''; MathJaxMap['math_99'] = ''; MathJaxMap['math_100'] = ''; MathJaxMap['math_101'] = ''; MathJaxMap['math_102'] = ''; MathJaxMap['math_103'] = ''; MathJaxMap['math_104'] = ''; MathJaxMap['math_105'] = ''; MathJaxMap['math_106'] = ''; MathJaxMap['math_107'] = ''; MathJaxMap['math_108'] = ''; MathJaxMap['math_109'] = ''; MathJaxMap['math_110'] = ''; MathJaxMap['math_111'] = ''; MathJaxMap['math_112'] = ''; MathJaxMap['math_113'] = ''; MathJaxMap['math_114'] = ''; MathJaxMap['math_115'] = ''; MathJaxMap['math_116'] = ''; MathJaxMap['math_117'] = ''; MathJaxMap['math_118'] = ''; MathJaxMap['math_119'] = ''; MathJaxMap['math_120'] = ''; MathJaxMap['math_121'] = ''; MathJaxMap['math_122'] = ''; MathJaxMap['math_123'] = ''; MathJaxMap['math_124'] = ''; MathJaxMap['math_125'] = ''; MathJaxMap['math_126'] = ''; MathJaxMap['math_127'] = ''; MathJaxMap['math_128'] = ''; MathJaxMap['math_129'] = ''; MathJaxMap['math_130'] = ''; MathJaxMap['math_131'] = ''; MathJaxMap['math_132'] = ''; MathJaxMap['math_133'] = ''; MathJaxMap['math_134'] = ''; MathJaxMap['math_135'] = ''; MathJaxMap['math_136'] = ''; MathJaxMap['math_137'] = ''; MathJaxMap['math_138'] = ''; MathJaxMap['math_139'] = ''; MathJaxMap['math_140'] = ''; MathJaxMap['math_141'] = ''; MathJaxMap['math_142'] = ''; MathJaxMap['math_143'] = ''; MathJaxMap['math_144'] = ''; MathJaxMap['math_145'] = ''; MathJaxMap['math_146'] = ''; MathJaxMap['math_147'] = ''; MathJaxMap['math_148'] = ''; MathJaxMap['math_149'] = ''; MathJaxMap['math_150'] = ''; MathJaxMap['math_151'] = ''; MathJaxMap['math_152'] = ''; MathJaxMap['math_153'] = ''; MathJaxMap['math_154'] = ''; MathJaxMap['math_155'] = ''; MathJaxMap['math_156'] = ''; MathJaxMap['math_157'] = ''; MathJaxMap['math_158'] = ''; MathJaxMap['math_159'] = ''; MathJaxMap['math_160'] = ''; MathJaxMap['math_161'] = ''; MathJaxMap['math_162'] = ''; MathJaxMap['math_163'] = ''; MathJaxMap['math_164'] = ''; MathJaxMap['math_165'] = ''; MathJaxMap['math_166'] = ''; MathJaxMap['math_167'] = ''; MathJaxMap['math_168'] = ''; MathJaxMap['math_169'] = ''; MathJaxMap['math_170'] = ''; MathJaxMap['math_171'] = ''; MathJaxMap['math_172'] = ''; MathJaxMap['math_173'] = ''; MathJaxMap['math_174'] = ''; MathJaxMap['math_175'] = ''; MathJaxMap['math_176'] = ''; MathJaxMap['math_177'] = ''; MathJaxMap['math_178'] = ''; MathJaxMap['math_179'] = ''; MathJaxMap['math_180'] = ''; MathJaxMap['math_181'] = ''; MathJaxMap['math_182'] = ''; //MathJaxMap['math_183'] = ''; MathJaxMap['math_183'] = ''; MathJaxMap['math_184'] = ''; MathJaxMap['math_185'] = ''; MathJaxMap['math_186'] = ''; MathJaxMap['math_187'] = ''; MathJaxMap['math_188'] = ''; MathJaxMap['math_189'] = ''; MathJaxMap['math_190'] = ''; MathJaxMap['math_191'] = ''; MathJaxMap['math_192'] = ''; MathJaxMap['math_193'] = ''; MathJaxMap['math_194'] = ''; MathJaxMap['math_195'] = ''; MathJaxMap['math_196'] = ''; MathJaxMap['math_197'] = ''; MathJaxMap['math_198'] = ''; MathJaxMap['math_199'] = ''; MathJaxMap['math_200'] = ''; MathJaxMap['math_201'] = ''; MathJaxMap['math_202'] = ''; MathJaxMap['math_203'] = ''; MathJaxMap['math_204'] = ''; MathJaxMap['math_205'] = ''; MathJaxMap['math_206'] = ''; MathJaxMap['math_207'] = ''; MathJaxMap['math_208'] = ''; MathJaxMap['math_209'] = ''; MathJaxMap['math_210'] = ''; MathJaxMap['math_211'] = ''; MathJaxMap['math_212'] = ''; MathJaxMap['math_213'] = ''; MathJaxMap['math_214'] = ''; MathJaxMap['math_215'] = ''; MathJaxMap['math_216'] = ''; MathJaxMap['math_217'] = ''; MathJaxMap['math_218'] = ''; MathJaxMap['math_219'] = ''; MathJaxMap['math_220'] = ''; MathJaxMap['math_221'] = ''; MathJaxMap['math_222'] = ''; MathJaxMap['math_223'] = ''; MathJaxMap['math_224'] = ''; MathJaxMap['math_225'] = ''; MathJaxMap['math_226'] = ''; MathJaxMap['math_227'] = ''; MathJaxMap['math_228'] = ''; MathJaxMap['math_229'] = ''; MathJaxMap['math_230'] = ''; MathJaxMap['math_231'] = ''; MathJaxMap['math_232'] = ''; MathJaxMap['math_233'] = ''; MathJaxMap['math_234'] = ''; MathJaxMap['math_235'] = ''; MathJaxMap['math_236'] = ''; MathJaxMap['math_237'] = ''; MathJaxMap['math_238'] = ''; MathJaxMap['math_239'] = ''; MathJaxMap['math_240'] = ''; MathJaxMap['math_241'] = ''; MathJaxMap['math_242'] = ''; MathJaxMap['math_243'] = ''; MathJaxMap['math_244'] = ''; MathJaxMap['math_245'] = ''; MathJaxMap['math_246'] = ''; MathJaxMap['math_247'] = ''; MathJaxMap['math_248'] = ''; MathJaxMap['math_249'] = ''; MathJaxMap['math_250'] = ''; MathJaxMap['math_251'] = ''; MathJaxMap['math_252'] = ''; MathJaxMap['math_253'] = ''; MathJaxMap['math_254'] = ''; MathJaxMap['math_255'] = ''; MathJaxMap['math_256'] = ''; MathJaxMap['math_257'] = ''; MathJaxMap['math_258'] = ''; MathJaxMap['math_259'] = ''; MathJaxMap['math_260'] = ''; MathJaxMap['math_261'] = ''; MathJaxMap['math_262'] = ''; MathJaxMap['math_263'] = ''; MathJaxMap['math_264'] = ''; MathJaxMap['math_265'] = ''; MathJaxMap['math_266'] = ''; MathJaxMap['math_267'] = ''; MathJaxMap['math_268'] = ''; MathJaxMap['math_269'] = ''; MathJaxMap['math_270'] = ''; MathJaxMap['math_271'] = ''; MathJaxMap['math_272'] = ''; MathJaxMap['math_273'] = ''; MathJaxMap['math_274'] = ''; MathJaxMap['math_275'] = ''; MathJaxMap['math_276'] = ''; MathJaxMap['math_277'] = ''; MathJaxMap['math_278'] = ''; MathJaxMap['math_279'] = ''; MathJaxMap['math_280'] = ''; MathJaxMap['math_281'] = ''; MathJaxMap['math_282'] = ''; MathJaxMap['math_283'] = ''; MathJaxMap['math_284'] = ''; MathJaxMap['math_285'] = ''; MathJaxMap['math_286'] = ''; MathJaxMap['math_287'] = ''; MathJaxMap['math_288'] = ''; MathJaxMap['math_289'] = ''; MathJaxMap['math_290'] = ''; MathJaxMap['math_291'] = ''; MathJaxMap['math_292'] = ''; MathJaxMap['math_293'] = ''; MathJaxMap['math_294'] = ''; MathJaxMap['math_295'] = ''; MathJaxMap['math_296'] = ''; MathJaxMap['math_297'] = ''; MathJaxMap['math_298'] = ''; MathJaxMap['math_299'] = ''; MathJaxMap['math_300'] = ''; MathJaxMap['math_301'] = ''; MathJaxMap['math_302'] = ''; MathJaxMap['math_303'] = ''; MathJaxMap['math_304'] = ''; MathJaxMap['math_305'] = ''; MathJaxMap['math_306'] = ''; MathJaxMap['math_307'] = ''; MathJaxMap['math_308'] = ''; //MathJaxMap['math_309'] = ''; //MathJaxMap['math_309'] = ''; MathJaxMap['math_309'] = ''; MathJaxMap['math_310'] = ''; MathJaxMap['math_311'] = ''; MathJaxMap['math_312'] = ''; MathJaxMap['math_313'] = ''; //MathJaxMap['math_314'] = ''; MathJaxMap['math_314'] = ''; MathJaxMap['math_315'] = ''; MathJaxMap['math_316'] = ''; MathJaxMap['math_317'] = ''; MathJaxMap['math_318'] = ''; MathJaxMap['math_319'] = ''; MathJaxMap['math_320'] = ''; MathJaxMap['math_321'] = ''; MathJaxMap['math_322'] = ''; MathJaxMap['math_323'] = ''; MathJaxMap['math_324'] = ''; MathJaxMap['math_325'] = ''; for (var key in MathJaxMap) { if (MathJaxMap.hasOwnProperty(key)) { $('[data-math=' + key + ']').html(MathJaxMap[key]); } } $.ajaxSetup({ cache: true }); //configure the mathjax engine window.MathJax = { "HTML-CSS": { mtextFontInherit: true, scale: 98, minScaleAdjust: 100, noReflows:false }, MathML: { useMathMLspacing: false }, menuSettings: { zoom: "Click" }, MathMenu: { showFontMenu: true } }; //$.getScript( "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"); //$.getScript("https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"); $.getScript( "http://prod-cdn-packages.macmillan.cloud/media/MathJax/MathJax.js?config=TeX-MML-AM_CHTML"); xBookUtils.showAnswers['ips9e-ch07-quest-001'] = "
7.1 (a) $45. (b) 15.
"; xBookUtils.showAnswers['ips9e-ch07-quest-003'] = "7.3 ($670.105, $861.895).
"; xBookUtils.showAnswers['ips9e-ch07-quest-005'] = "7.5 (a) df = 22, 0.04 < P-value < 0.05, which is significant at the 5% level. (b) df = 8, 0.05 < P-value < 0.10, which is not significant at the 5% level.
"; xBookUtils.showAnswers['ips9e-ch07-quest-007'] = "7.7 From software, the 95% confidence interval is (2.0817, 26.9183).
"; xBookUtils.showAnswers['ips9e-ch07-quest-009'] = "7.9 A paired t-test is appropriate because the number of receptivity displays was recorded twice for each female when exposed to the two different videos.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0011'] = "7.11 df = 4, t* = 2.776. The 95% confidence interval is ( − 5.32, 2.12).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0013'] = "7.13 Although the sample size is quite large, n = 518, there are potential outliers, so we should not use t procedures with these data.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0015'] = "7.15 (a) df = 14, t* = 2.145. (b) df = 27, t* = 2.052. (c) df = 27, t* = 1.703. (d) As sample size increases, the margin of error decreases. As confidence increases, the margin of error increases.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0017'] = "7.17 The 5% critical value for a t distribution with df = 23 is 1.714, reject H0 when t > 1.714; the other is simply the mirror image, so reject when t < − 1.714.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0019'] = "7.19 Yes, because we want > 0, P-value = 0.9625.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0021'] = "7.21 (a) df = 12. (b) 2.681 < t < 3.055. (c) 0.01 < P-value < 0.02. (d) t = 2.78 is significant at the 5% level but not at the 1% level. (e) 0.0167.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0023'] = "7.23 It depends on if is on the appropriate side of 0.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0025'] = "7.25 df = 15. The 95% confidence interval is (27.37, 28.95).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0027'] = "7.27 (a) The histogram shows the data are Normally distributed, so t procedures are appropriate. (b) The 95% confidence interval is 36.157 ± 2.603. (c) (33.55, 38.76). (d) Inference from the confidence interval only applies to the mean, not the median. Other tools would need to be used for inference on the median.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0029'] = "7.29 (a) H0: = 4.7%, Ha: ≠ 4.7%. = 4.9767. s = 0.03215. t = 14.907. df = 2, 0.002 < P-value < 0.005. (b) (4.8968%, 5.0566%). (c) For the cans and bottles to be within 0.3% of the advertised level, they need to be between 4.7% and 5.3%; it appears that Budweiser is within the standards.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0031'] = "7.31 (a) H0: = 10, Ha: < 10. (b) t = − 5.2603, df = 33, P-value < 0.0005.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0033'] = "7.33 (a) The distribution has two peaks, so the distribution is not Normal. The five-number summary is 2.2, 10.95, 28.5, 41.9, 69.3. (b) Maybe: We have a large enough sample to overcome the non-Normal distribution, but we are sampling from a small population. (c) = 27.29, s = 17.7058, df = 39; the 95% confidence interval is (21.57, 33.01). (d) Answers may vary.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0035'] = "7.35 (a) 15.833. (b) df = 2499; the 95% confidence interval is (1.93, 3.17). (c) The large number of observations eliminates any concern of skewness, but the outliers pose a risk for using t procedures.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0037'] = "7.37 H0: = 45, Ha: > 45. t = 5.457. df = 49, P-value < 0.0005.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0039'] = "7.39 (a) H0: = 0, Ha: ≠ 0. t = 5.125, df = 15, P-value = 0.00012. (b) (191.6, 464.4).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0041'] = "7.41 (a) H0: = 0, Ha: ≠ 0. (b) 2.73, s = 2.8015, t = 4.358, df = 19, P-value < 0.001.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0043'] = "7.43 (a) H0: = 925, Ha: > 925. t = 3.27, df = 35, P-value = 0.0012. (b) H0: = 935, Ha: > 935, t = 0.80, df = 35, P-value = 0.2146. (c) The interval is 931.3 to 945.0, which includes 935, but not 925.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0045'] = "7.45 (a) The differences are spread from − 0.018 to 0.020. A Normal quantile plot reveals the data are approximately Normal and, therefore, t methods are appropriate. (b) H0: = 0, Ha: ≠ 0. t = − 0.347, df = 7, P-value = 0.7388. (c) (−0.0117 to 0.0087). (d) The subjects from this sample may be representative of future subjects, but the test results and confidence interval are suspect because this is not a random sample.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0047'] = "7.47 (a) H0: = 0, Ha: > 0. (b) Left-skewed, = 2.5, s = 2.8928. (c) t = 3.8649, df = 19, P-value = 0.0005. (d) (1.15, 3.85).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0049'] = "7.49 (2.65, 24.29). With the smaller sample sizes, the confidence interval is wider.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0051'] = "7.51 H0: = 5, Ha: > 5. The confidence interval is (5.48, 21.46). Because 5 is not in this interval, we reject H0. The data show evidence that the improvement is greater than five points.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0053'] = "7.53 SPSS and SAS give t = 2.279, P-value = 0.052.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0055'] = "7.55 (a) Hypotheses should involve and . (b) The samples are not independent. (c) We need the P-value to be small to reject H0. (d) Assuming the researcher computed the t statistic using − , a positive value of t does not support Ha.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0057'] = "7.57 (a) We cannot reject H0: = in favor of the two-sided alternative at the 5% level. (b) We could reject H0 in favor of Ha: < is the t statistic is negative.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0059'] = "7.59 (a) Both distributions are Normally distributed, except the low-intensity class has a low outlier. (b) H0: H = L, Ha: H ≠ L. t = 5.30. df = 14. P-value < 0.001. (c) Because the low-intensity class has an outlier, the t-test is not appropriate. (d) t = 6.31. df = 13. P-value < 0.001. Removing the outlier didn’t change the results. (e) Because the outlier is not affecting the results, it is probably okay to report both tests.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0061'] = "7.61 (a) The t procedure is robust. Because n1 + n2 ≥ 40, we can use the t procedures on skewed data.
(b)
df = 89. 0.01 < P-value < 0.02.
7.63 .t = 2.59, df = 39.
0.005 < P-value < 0.01.
7.65 52.4%. We don’t know if these students were not Facebook users; because of this, the results should be viewed with caution.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0067'] = "7.67 (a) The data are not Normally distributed, but because neither distribution is strongly skewed or has outliers, t procedures are still appropriate. (b) For N group: = 0.5714, s = 0.73, n = 14. For S group: = 2.1176, s = 1.24, n = 17. (c) H0: = , : ≠ . (d) t = − 4.31, df = 13, P-value < 0.001. (e) ( − 2.32, − 0.77).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0069'] = "7.69 (a) Taking averages on ratings is likely not appropriate. (b) The data are integers but the samples are large, the t procedures can be used. (c) McDonald’s: = 3.9937, s = 0.8930. Taco Bell: = 4.2208, s = 0.7331. (d) H0: M = T, Hα: M ≠ T. t = − 3.48. df = 307. P-value < 0.001.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0071'] = "7.71 (a) Assuming we have SRSs from each population, this seems reasonable. (b) .Hα: Early ≠ Late.
(c) = 1.0534, t = 1.614, df = 199, P-value = 0.1081. (d) ( − 0.39, 3.79).
7.73 You need sample sizes and standard deviations or df and a more accurate P-value. The confidence interval could give us useful information about the magnitude of the difference.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0075'] = "7.75 This is a matched pairs design.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0077'] = "7.77 There could be things that are similar about the next eight employees who need new computers as well as the following eight, which could bias the results.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0079'] = "7.79 (a) The north distribution is right-skewed, while the south distribution is left-skewed. (b) The methods of this section seem to be appropriate because the sample sizes are relatively large, and there are no outliers. (c) H0: = , Ha: ≠ . (d) and . t = − 2.629, df = 29, 0.01 < P-value < 0.02. (e) ( − 19.2614, − 2.4053).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0081'] = "7.81 (a) ( − 1.07, 7.07). (b) With 95% confidence, the mean change in sales from last year to this year is between − 1.07 and 7.07. Because the interval covers 0 and includes some negative values, it is possible sales have actually decreased.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0083'] = "7.83 (a) H0: = F, Ha: > F. t = 1.654, df = 18, 0.05 < P-value < 0.10. (b) (−0.2434, 2.0434). (c) We need two independent SRSs from Normal populations.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0085'] = "7.85 t = 4.17, df = 63, P-value < 0.001. Confidence interval: (14.57, 41.43). The results are similar.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0087'] = "7.87 15.9617, = 4.1213. t = − 2.629, df = 58, P-value = 0.0110. The 95% confidence interval is (−19.113, − 2.554).
"; xBookUtils.showAnswers['ips9e-ch07-quest-0089'] = "7.89 df = 55.725.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0091'] = "7.91 (a) df = 137.066. (b) which is slightly closer to . (c) With no assumption of equality, With the pooled method, (d) t = 18.74 and df = 333, for which P < 0.0001, and the 95% confidence interval is (10.2827, 12.7173). The t value is larger, the confidence interval is narrower, and the P-value is smaller. (e) df = 121.503. ; the standard errors are 0.2653 and 0.1995; t = 24.56, df = 333, P-value < 0.0001, and the 95% confidence interval is (4.5042, 5.2958). With the pooled procedure, t is larger, and the interval is narrower.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0093'] = "7.93 n = 24 guarantees the margin of error is less than 5000.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0095'] = "7.95 Higher; if the alternative µ is farther away from 18.5 then we will have more power.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0097'] = "7.97 Decrease.
"; xBookUtils.showAnswers['ips9e-ch07-quest-0099'] = "7.99 0.72. 0.72.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00101'] = "7.101 (a) To halve the margin of error the sample size needs to be quadrupled. (b) The sign test is less powerful than the t test when the differences are close to Normal. (c) For a two-sided alternative, the power would be the same. (d) Increasing the sample size has no effect on the probability of a Type I error, this is determined by the choice of .
"; xBookUtils.showAnswers['ips9e-ch07-quest-00103'] = "7.103 No, the confidence interval is for the mean monthly rate, not the individual apartment rates.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00105'] = "7.105 (a) n = 104. (b) We would need to use the bigger sample to make sure both margin of error conditions are met.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00107'] = "7.107 (a) n = 18. (b) For n = 10, the power will be less than 90%. (c) For n = 10 the power is 0.80.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00109'] = "7.109 Answers will vary based on the choice of . For = 0.015, power = 0.09.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00111'] = "7.111 (a) Increase. A larger alpha gives more power. (b) 0.89.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00113'] = "7.113 Using a larger for planning the study is advisable because it provides a conservative (safe) estimate of the power.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00115'] = "7.115 H0: median = 0, Ha: median > 0; P-value = 0.0898. In Exercise 7.38, we were able to reject H0; here, we cannot.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00117'] = "7.117 H0: median = 0, Ha: median > 0; P-value = 0.0002. Using a t test, we found the same conclusion.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00119'] = "7.119 = 153.5, s = 14.708, It would not be appropriate to construct a confidence interval because we cannot consider these four scores to be an SRS.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00121'] = "7.121 (b) The plot shows that t* approaches z* = 1.96 as the df increases. (c) The plots would be similar, but t* would approach z* = 1.645 as the df increases.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00123'] = "7.123 (a) Use two independent samples. (b) Use a matched pairs design. (c) Take a single sample of college students, and ask them to rate the appeal of the product.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00125'] = "7.125 (a) H0: = 1.5, Ha: < 1.5; t = − 9.974, df = 199, P-value ≈ 0. (b) (0.697, 0.963). (d) We have a large sample, so t procedures should be safe.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00127'] = "7.127 (a) ( − 3.008 to 1.302). (b) ( − 1.761, 0.055). (c) The centers of the intervals are the same, but the margin of error for the independent samples interval is much larger.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00129'] = "7.129 (a) We are looking at the average proportions across both samples. (b) H0: = , Ha: ≠ . (c) For the first year: t = 0.982, df = 52.3, P-value = 0.3305. For the third year: t = 2.126, df = 46.9, P-value = 0.0388.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00131'] = "7.131 (a) = − 0.7, = 2.298; = 14, = 56.125.
(b) df = 13, = − 0.305 ( = 0.7655), and = 0.249 ( = 0.8069).
(c) ( − 5.66 to 4.26) and ( − 107.23, 135.23).
7.133 (a) Because the same mockingbird responded on each day. (b) 6.9774. (c) H0: = , Ha: ≠ ; t = 6.319, df = 23, P-value < 0.001. (d) t = − 0.973, P-value = 0.3407. (e) There is a significant difference between day 1 and day 4 but not day 1 and day 5.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00135'] = "7.135 How much a person eats or drinks may depend on how many people he or she is with.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00137'] = "7.137 A two-sample t procedure was used. We assumed the data are approximately Normal. H0: = , Hα: > ; t = 0.95, df = 89. 0.15 < P-value < 0.20.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00139'] = "7.139 A two-sample t procedure was used. H0: = μN, Hα: > ; t = − 0.16, df = 89, P-value > 0.25.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00141'] = "7.141 = 77.76%, s = 32.6768%, (64.27% to 91.25%). This seems to support the retailer’s claim.
"; xBookUtils.showAnswers['ips9e-ch07-quest-00143'] = "7.143 The distributions appear similar. GPA: t = − 0.91, df = 30, 0.15 < P-value < 0.20. Confidence interval: ( − 1.35, 0.52). IQ: t = 1.64, 0.05 < P-value < 0.10 (df = 30). Confidence interval: ( − 1.24, 11.48).
"; xBookUtils.showAnswers['ips9e-ch07-quest-00145'] = "7.145 H0: = , Hα: > ; t = 3.65, P-value < 0.0005. Confidence interval: (0.7714, 2.6086).
"; xBookUtils.showAnswers['ips9e-ch07-quest-00147'] = "7.147 (a) H0: = , Hα: < ; t = 2.87, P-value < 0.005. Confidence interval: (1.7, 9.7). (b) H0: = , Hα: < ; t = 1.88, P-value < 0.05. Confidence interval: ( − 0.24, 6.7).
"; xBookUtils.showAnswers['ips9e-ch07-quest-00149'] = "7.149 No: What we have is nothing like an SRS of the population of school corporations; we have census data for your state.
"; xBookUtils.terms['fn_ch7_fn1'] = "1. Average hours per week obtained from “The Total Audience Report, 4th Quarter 2014,’’ Nielsen Company (2015).
"; xBookUtils.terms['fn_ch7_fn2'] = "2. C. Don Wiggins, “The legal perils of ‘underdiversification’—a case study,’’ Personal Financial Planning, 1, No. 6 (1999), pp. 16–18.
"; xBookUtils.terms['fn_ch7_fn3'] = "3. Data provided by Bill Berezowitz and James Malloy of GE Healthcare.
"; xBookUtils.terms['fn_ch7_fn4'] = "4. Brent Stoffer and George W. Uetz, “The effects of social experience with varying male availability on female mate preferences in a wolf spider,’’ Behavioral Ecology Sociobiology, 69 (2015), pp. 927–937.
"; xBookUtils.terms['fn_ch7_fn5'] = "5. Go to www.futurity.org/fried-food-taste-without-all-the-fat/ for more information.
"; xBookUtils.terms['fn_ch7_fn6'] = "6. These recommendations are based on extensive computer work. See, for example, Harry O. Posten, “The robustness of the one-sample t-test over the Pearson system,’’ Journal of Statistical Computation and Simulation, 9 (1979), pp. 133–149; and E. S. Pearson and N. W. Please, “Relation between the shape of population distribution and the robustness of four simple test statistics,’’ Biometrika, 62 (1975), pp. 223–241.
"; xBookUtils.terms['fn_ch7_fn7'] = "7. The standard reference here is Bradley Efron and Robert J. Tibshirani, An Introduction to the Bootstrap, Chapman Hall, 1993. A less technical overview is in Bradley Efron and Robert J. Tibshirani, “Statistical data analysis in the computer age,’’ Science 253 (1991), pp. 390–395.
"; xBookUtils.terms['fn_ch7_fn8'] = "8. From “Insolvency Statistics in Canada 2013—Annual report’’ available at www.ic.gc.ca/eic/site/bsf-osb.nsf/eng/br03221.html.
"; xBookUtils.terms['fn_ch7_fn9'] = "9. This announcement can be found at epa.gov/fueleconomy/labelchange.htm.
"; xBookUtils.terms['fn_ch7_fn10'] = "10. Based on the scatterplot found at newsroom.uber.com/nyc/what-does-a-typical-new-york-uberx-partner-earn-in-a-week/.
"; xBookUtils.terms['fn_ch7_fn11'] = "11. Statistics are from the article “6 new facts about Facebook,’’ posted February 3, 2014, on www.pewresearch.org/.
"; xBookUtils.terms['fn_ch7_fn12'] = "12. A description of the lawsuit can be found at www.cnn.com/2013/02/26/business/california-anheuser-busch-lawsuit/index.html.
"; xBookUtils.terms['fn_ch7_fn13'] = "13. See Note 1.
"; xBookUtils.terms['fn_ch7_fn14'] = "14. Christine L. Porath and Amir Erez, “Overlooked but not untouched: How rudeness reduces onlookers’ performance on routine and creative tasks,’’ Organizational Behavior and Human Decision Processes, 109 (2009), pp. 29–44.
"; xBookUtils.terms['fn_ch7_fn15'] = "15. The vehicle is a 2002 Toyota Prius previously owned by the third author.
"; xBookUtils.terms['fn_ch7_fn16'] = "16. Information regarding Instagram can be found at locowise.com/tools.php.
"; xBookUtils.terms['fn_ch7_fn17'] = "17. Sujata Sethi et al., “Study of level of stress in the parents of children with attention-deficit/hyperactivity disorder,’’ Journal of Indian Association for Child and Adolescent Mental Health, 8, No. 2 (2012), pp. 25–37.
"; xBookUtils.terms['fn_ch7_fn18'] = "18. James A. Levine, Norman L. Eberhardt, and Michael D. Jensen, “Role of nonexercise activity thermogenesis in resistance to fat gain in humans,’’ Science, 283 (1999), pp. 212–214. Data for this study are available from the Science website, www.sciencemag.org.
"; xBookUtils.terms['fn_ch7_fn19'] = "19. These data were collected in connection with a bone health study at Purdue University and were provided by Linda McCabe.
"; xBookUtils.terms['fn_ch7_fn20'] = "20. Based on Praveetha Patalay et al., “Equivalence of paper and computer formats of a child self-report mental health measure,’’ European Journal of Psychological Assessment, advance online publication, doi:10.1027/1015-5759/a000206.
"; xBookUtils.terms['fn_ch7_fn21'] = "21. Data provided by Joseph A. Wipf, Department of Foreign Languages and Literatures, Purdue University.
"; xBookUtils.terms['fn_ch7_fn22'] = "22. Summary information can be found at the National Center for Health Statistics website, www.cdc.gov/nchs/nhanes.htm.
"; xBookUtils.terms['fn_ch7_fn23'] = "23. Detailed information about the conservative t procedures can be found in Paul Leaverton and John J. Birch, “Small sample power curves for the two sample location problem,’’ Technometrics, 11 (1969), pp. 299–307; in Henry Scheffé, “Practical solutions of the Behrens-Fisher problem,’’ Journal of the American Statistical Association, 65 (1970), pp. 1501–1508; and in D. J. Best and J. C. W. Rayner, “Welch’s approximate solution for the Behrens-Fisher problem,’’ Technometrics, 29 (1987), pp. 205–210.
"; xBookUtils.terms['fn_ch7_fn24'] = "24. This example is adapted from Maribeth C. Schmitt, “The effects of an elaborated directed reading activity on the metacomprehension skills of third graders,’’ PhD dissertation, Purdue University, 1987.
"; xBookUtils.terms['fn_ch7_fn25'] = "25. See the extensive simulation studies in Harry O. Posten, “The robustness of the two-sample t test over the Pearson system,’’ Journal of Statistical Computation and Simulation, 6 (1978), pp. 295–311.
"; xBookUtils.terms['fn_ch7_fn26'] = "26. M. Garaulet et al., “Timing of food intake predicts weight loss effectiveness,’’ International Journal of Obesity, advance online publication, January 29, 2013, doi:10.1038/ijo.2012.229.
"; xBookUtils.terms['fn_ch7_fn27'] = "27. This study is reported in Roseann M. Lyle et al., “Blood pressure and metabolic effects of calcium supplementation in normotensive white and black men,’’ Journal of the American Medical Association, 257 (1987), pp. 1772–1776. The individual measurements in Table 7.5 were provided by Dr. Lyle.
"; xBookUtils.terms['fn_ch7_fn28'] = "28. J.D. Vescovi and T. Goodale, “Physical demands of womens Rugby Sevens matches: Female athletes in motion (FAiM) study,’’ International Journal of Sports Medicine, advance online publication, doi:10.1055/s-0035-1548940.
"; xBookUtils.terms['fn_ch7_fn29'] = "29. Elizabeth F Beach and Valerie Nie, “Noise levels in fitness classes are still too high: Evidence from 1997–1998 and 2009–2011,’’ Archives of Environmental & Occupational Health 69, No. 4 (2014), pp. 223–230.
"; xBookUtils.terms['fn_ch7_fn30'] = "30. Y. Charles Zhang and Norbert Schwarz, “How and why 1 year differs from 365 days: A conversational logic analysis of inferences from the granularity of quantitative expressions,’’ Journal of Consumer Research 39 (August 2012), pp. S212–S223.
"; xBookUtils.terms['fn_ch7_fn31'] = "31. Karel Kleisner et al., “Trustworthy-looking face meets brown eyes,’’ PLoS ONE 8, No. 1 (2013), e53285, doi:10.1371/journal.pone.0053285.
"; xBookUtils.terms['fn_ch7_fn32'] = "32. Reynol Junco, “Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance,’’ Computers in Human Behavior, 28, No. 1 (2012), pp. 187–198.
"; xBookUtils.terms['fn_ch7_fn33'] = "33. C. E. Cryfer et al., “Misery is not miserly: Sad and self-focused individuals spend more,’’ Psychological Science, 19 (2008), pp. 525–530.
"; xBookUtils.terms['fn_ch7_fn34'] = "34. Grant D. Brinkworth et al., “Long-term effects of a very low-carbohydrate diet and a low-fat diet on mood and cognitive function,’’ Archives of Internal Medicine, 169 (2009), pp. 1873–1880.
"; xBookUtils.terms['fn_ch7_fn35'] = "35. These reports can be found at www.qsrmagazine.com/reports.
"; xBookUtils.terms['fn_ch7_fn36'] = "36. Samara Joy Nielsen and Barry M. Popkin, “Patterns and trends in food portion sizes, 1977–1998,’’ Journal of the American Medical Association, 289 (2003), pp. 450–453.
"; xBookUtils.terms['fn_ch7_fn37'] = "37. Gordana Mrdjenovic and David A. Levitsky, “Nutritional and energetic consequences of sweetened drink consumption in 6- to 13-year-old children,’’ Journal of Pediatrics, 142 (2003), pp. 604–610.
"; xBookUtils.terms['fn_ch7_fn38'] = "38. David Han-Kuen Chu, “A test of corporate advertising using the elaboration likelihood model,’’ MS thesis, Purdue University, 1993.
"; xBookUtils.terms['fn_ch7_fn39'] = "39. M. F. Picciano and R. H. Deering, “The influence of feeding regimens on iron status during infancy,’’ American Journal of Clinical Nutrition, 33 (1980), pp. 746–753.
"; xBookUtils.terms['fn_ch7_fn40'] = "40. Average starting salary taken from the spring 2015 salary survey by the National Association of Colleges and Employers.
"; xBookUtils.terms['fn_ch7_fn41'] = "41. The data were obtained on August 24, 2006, from an iPod owned by George McCabe, Jr.
"; xBookUtils.terms['fn_ch7_fn42'] = "42. The method is described in Xiao-Hua Zhou and Sujuan Gao,“Confidence intervals for the log-normal mean,’’ Statistics in Medicine, 16 (1997), pp. 783–790.
"; xBookUtils.terms['fn_ch7_fn43'] = "43. See the 2015 press release from the Student Monitor, at www.studentmonitor.com.
"; xBookUtils.terms['fn_ch7_fn44'] = "44. Data from Wayne Nelson, Applied Life Data Analysis, Wiley, 1982, p. 471.
"; xBookUtils.terms['fn_ch7_fn45'] = "45. This city’s restaurant inspection data can be found at www.jsonline.com/watchdog/dataondemand/.
"; xBookUtils.terms['fn_ch7_fn46'] = "46. Braz Camargo et al., “Interracial friendships in college,’’ Journal of Labor Economics, 28 (2010), pp. 861–892.
"; xBookUtils.terms['fn_ch7_fn47'] = "47. Based on Loren Cordain et al., “Influence of moderate daily wine consumption on body weight regulation and metabolism in healthy free-living males,’’ Journal of the American College of Nutrition, 16 (1997), pp. 134–139.
"; xBookUtils.terms['fn_ch7_fn48'] = "48. B. Wansink et al., “Fine as North Dakota wine: Sensory expectations and the intake of companion foods,’’ Physiology & Behavior, 90 (2007), pp. 712–716.
"; xBookUtils.terms['fn_ch7_fn49'] = "49. Douglas J. Levey et al., “Urban mockingbirds quickly learn to identify individual humans,’’ Proceedings of the National Academy of Sciences, 106 (2009), pp. 8959–8962.
"; xBookUtils.terms['fn_ch7_fn50'] = "50. Morgan K. Ward and Darren W. Dahl, “Should the devil sell Prada? Retail rejection increases aspiring consumers’ desire for the brand,’’ Journal of Consumer Research, 41, No. 3 (2014), pp. 590–609.
"; xBookUtils.terms['fn_ch7_fn51'] = "51. Anne Z. Hoch et al., “Prevalence of the female athlete triad in high school athletes and sedentary students,’’ Clinical Journal of Sports Medicine, 19 (2009), pp. 421–428.
"; xBookUtils.terms['fn_ch7_fn52'] = "52. This exercise is based on events that are real. The data and details have been altered to protect the privacy of the individuals involved.
"; xBookUtils.terms['fn_ch7_fn53'] = "53. Based loosely on D. R. Black et al., “Minimal interventions for weight control: A cost-effective alternative,’’ Addictive Behaviors, 9 (1984), pp. 279–285.
"; xBookUtils.terms['fn_ch7_fn54'] = "54. These data were provided by Professor Sebastian Heath, School of Veterinary Medicine, Purdue University.
";