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Stat 217 – Day 13Last Time – Confidence IntervalsConfidence IntervalExam 1Exam 1 ResourcesExam 1 AdviceSome advice during examSome big, big ideasMain TopicsTest of SignificanceSlide 11ExampleQuestions?Review SessionStat 217 – Day 13ReviewLast Time – Confidence IntervalsWhen we can apply the CLT large enough sample sizerandom sampling from large populationthe “empirical rule” predicts 95% of our statistics fall within 2 standard deviations one each side of the population parameterSo 95% of the time our population parameter is within 2 standard deviations of our sample statisticConfidence Interval“margin-of-error” = .04 or 4 percentage pointsExam 1May use one 8.5 x 11 (both sides) page of self-produced notesMixture of multiple choice, short answer, longer questions (see quizzes, labs)Bring a calculator (not a cell phone)Access to the computer (e.g., applets, JMP)Exam 1 ResourcesReview handoutReview questions/solutionsReadings 1-8Self-check videosWhat Went Wrong ExamplesQuiz solutions (and blank copies)Access to pre-labsSelf-tests, practice problemsSome grading comments on quizzes, investigations labs, annotated handouts (lecture notes page)Exam 1 AdviceReview handout, problems onlineWork problemsReview labsStart with ideas that we have emphasized more oftenSome advice during examIf you get stuck on a problem, move onlater parts, later problemsTry to hit the highlights in your answer (e.g., not all sources of bias, just the most serious)Be succinct (think before you write)Read the question carefullyShow all of your work, explain wellcommunication pointsRead entire question before writing anythingSome big, big ideasObservational units, variableWhat see in sample (descriptive) vs. saying something beyond the sample (inferential)Statistic vs. ParameterInterpretation of p-valueStatistical significanceInterpretations, reasoningProperties, “what if” questions…How are you deciding this?Main TopicsSample from a random process (e.g., coin toss, dolphins, cancer dog)Parameter:  = probability of “success”Statistic: sample proportionRandom sample from a finite large population (e.g., Gallup poll)Parameter:  = population proportion of “successes”Statistic: sample proportionConsider sampling, nonsampling biasesTest of SignificanceTest a conjecture about Assume null hypothesis is trueLook at the distribution of the statistic when the null hypothesis is trueSimulation (Coin Tossing, One Prop Inf)Central Limit Theorem (Reese’s Pieces)If observed value is in the tail of the distribution (small p-value), reject the null hypothesis. Otherwise “fail to reject.”FTR: Not convincing evidence against HoConfidence IntervalWant to estimate  from the sample dataCould test all the values and make an interval of the ones that are not rejectedNot practicalWhen the CLT applies, estimate + margin-of-errorestimate + 2 standard errorsI’m 95% confident that the parameter is between these two valuesProcedure works 95% of the timeExampleQuestions?Review Session7-9pm, Building 53, Room


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Cal Poly STAT 217 - Review

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