Stat 217 – Day 27The PlanPreviouslyNextActivity 25-1 (p. 507) (a)-(f)Activity 25-1If Ho is trueTest statisticMinitab output (handout)Slide 10Slide 11Slide 12Slide 13Slide 14Activity 25-5 (p. 515)Technical conditionsSlide 17Activity 25-2 (p. 511)Activity 25-2Activity 25-3 (e)-(g)Activity 25-4Activity 25-4 ConclusionStat 217 – Day 27Chi-square tests (Topic 25)The PlanExam 2 returned at end of class todayMean .80 (36/45)Solutions with commentary onlineDiscuss in class tomorrowToday: Chi-squareTuesday: ANOVAWednesday: Begin RegressionThursday: Regression labPreviouslyOne population proportion or meanComparing two population proportions or meansIs the difference statistically significant = larger than what we would expect by chance (if no difference in the populations)SimulationNormal probability modelChance = random sampling or random assignmentNextComparing more than 2 population proportions or more than 2 population meansSame first question: Is the response variable quantitative or categorical?Random sampling or random assignmentSame analysis but affects “scope of conclusions”Activity 25-1 (p. 507) (a)-(f)Observational study with an independent random sample in each of 1972, 1988, 2004 (explanatory variable) looking at whether people are “very happy” (response variable)Could the differences in these three sample proportions have arisen by chance (random sampling process) alone?Activity 25-1Parameters? Let 72 represent the proportion of all adult Americans who would have rated their general level as happiness as very happy in 1972Similarly for 88 and 04(g) H0 : 72 = 88 = 04 no association between happiness level and year Ha: not all 3 equal (is an association)General strategy?Assume Ho is true, what expect to see? Are our observed results surprising?If Ho is trueWhat would our segmented bar graph look like in this case?So our two-way table would be?“expected counts”Test statisticCompare the observed counts to these expected counts(n) Large values are evidence against HoHow decide what is large?Chi-square distributionMinitab output (handout)Chi-Square Test: 1972, 1988, 2004 Expected counts are printed below observed countsChi-Square contributions are printed below expected counts 1972 1988 2004 Total 1 486 498 419 1403 511.05 466.50 425.45 1.228 2.127 0.098 2 1120 968 918 3006 1094.95 999.50 911.55 0.573 0.993 0.046 Total 1606 1466 1337 4409 Chi-Sq = 5.064, DF = 2, P-Value = 0.079Minitab output (handout)Chi-Square Test: 1972, 1988, 2004 Expected counts are printed below observed countsChi-Square contributions are printed below expected counts 1972 1988 2004 Total 1 486 498 419 1403 511.05 466.50 425.45 1.228 2.127 0.098 2 1120 968 918 3006 1094.95 999.50 911.55 0.573 0.993 0.046 Total 1606 1466 1337 4409 Chi-Sq = 5.064, DF = 2, P-Value = 0.079Minitab output (handout)Chi-Square Test: 1972, 1988, 2004 Expected counts are printed below observed countsChi-Square contributions are printed below expected counts 1972 1988 2004 Total 1 486 498 419 1403 511.05 466.50 425.45 1.228 2.127 0.098 2 1120 968 918 3006 1094.95 999.50 911.55 0.573 0.993 0.046 Total 1606 1466 1337 4409 Chi-Sq = 5.064, DF = 2, P-Value = 0.079(486-511.05)2 511.05Minitab output (handout)Chi-Square Test: 1972, 1988, 2004 Expected counts are printed below observed countsChi-Square contributions are printed below expected counts 1972 1988 2004 Total 1 486 498 419 1403 511.05 466.50 425.45 1.228 2.127 0.098 2 1120 968 918 3006 1094.95 999.50 911.55 0.573 0.993 0.046 Total 1606 1466 1337 4409 Chi-Sq = 5.064, DF = 2, P-Value = 0.0791.228 + 2.217+.098+.573 + .993 + .046Minitab output (handout)Chi-Square Test: 1972, 1988, 2004 Expected counts are printed below observed countsChi-Square contributions are printed below expected counts 1972 1988 2004 Total 1 486 498 419 1403 511.05 466.50 425.45 1.228 2.127 0.098 2 1120 968 918 3006 1094.95 999.50 911.55 0.573 0.993 0.046 Total 1606 1466 1337 4409 Chi-Sq = 5.064, DF = 2, P-Value = 0.079Activity 25-1(p) With p-value = .079, fail to reject at the 5% level (but would at 10% level!)(q) You have weak statistical evidence that the population proportions of very happy people were not identical for these three years. (Because these were random samples, you are safe in generalizing this conclusion to the populations of all American adults in each year but not a randomized experiment so no cause and effect relationship)Activity 25-5 (p. 515)Two way tableChi-square test (output on handout)But what about a two-sample z-test?Same exact results if using a two-sided alternative!Technical conditionsIndependent random samples…Expected cell counts are all at least 5Are some ways to work around this…To Turn in with PartnerRead background of Activity 25-3Examine output on handoutWhat conclusions would you draw:Significance, Causation, GeneralizabilityFor TuesdayFinish Topic 25Output on handout (don’t have to learn Minitab)Notice how the hypothesis statements in the pink boxes differ across the scenariosSelf-check Activity 25-6Activity 25-2 (p. 511)What if have a non-binary response variable?Same thing!(a) Ho: the population distributions of happiness were the same all three yearsno association between happiness level and yearHa: the population distributions were not the same (is an association)(b) X2 = 35.655 (df = 4), p-value = .000(c) Strong evidence of a change in at least one of these population distributionsActivity 25-2Where are the differences (descriptively)?Fewer “not too happy” in 1998 than expected. More “not too happy” in 1972 than expected.Activity 25-3 (e)-(g)Can apply to randomized experiment as wellHo: The population proportions of potential customers who would leave a tip (or the probability is the same regardless of the type of card they receive)No association between type of card