Stat 321 – Day 1Syllabus HighlightsMythRealitySlide 5Slide 6ExampleSlide 8Slide 9Slide 10Slide 11Slide 12Slide 13Statistics vs. ProbabilitySlide 15Slide 16StatisticsDesign of StudiesGoals of this courseDataDescriptive StatisticsExamplesDescribing DataWhat to Look For…Slide 25Slide 26Slide 27Slide 28Slide 29Example 2: Rowers WeightsGraphsLab 1 – Fan Cost IndexFor Tuesday1Stat 321 – Day 1Probability and Statistics for Scientists and Engineers2Syllabus HighlightsOffice hoursWebpagesEmail aliasCourse structureRole of homeworksRole of textbookRole of quizzesRole and availability of lecture notes/powerpointPlease let me know asap if you drop the course3MythThis course will not be of use to me4RealityIndustry managersstatisticscommunicationworking in groupsYour major will help you get a job, but can’t help you keep it5MythThis is just like another Math course6RealityDon’t expect same performance as previous math coursesTreat as a foreign languagemust practice the languageThere are often several ways to approach a problem and more than one “correct answer”7Examplea Washington Post-ABC News poll indicatesa Washington Post-ABC News survey8MythThis course will be about memorizing formulas9RealityNot in this classFormulas are supplied on examsHow/when to use formulasWhat does formula/concept meanComputer does the computationtell computer which methodinterpret computer results10MythThe teacher will physically harm me if I ask a question11RealityAre no stupid questionsAt least 8 other people have the same questionI have more than one way of explaining an ideaWarning: I often answer a question with a question12MythI can succeed in this course with a minimum of effort13RealityAssignments count a lot Think about the class each day10 hours/week outside of classMemorization will not cut itMust demonstrate genuine understandingExplanations and communication count a lot14Statistics vs. ProbabilityPopulation = group of people/objects that you really want to know aboute.g., shipment of light bulbsSample = the group of people/objects you are actually able to examinee.g., 5 light bulbsVariable = what you want to measuree.g., lifetime of light bulbs15Statistics vs. ProbabilityProbability: If 10% of the light bulbs are defectives, how many will I expect to see in my sample?Statistics: If I have 1 bad light bulb in my sample, is that strong enough evidence to convince me to not take the shipment?16Statistics vs. ProbabilityPopulationSampleProbabilityStatisti cs17StatisticsDescriptive StatisticsOrganization, summarization, and presentation of the data Population or sampleInferential StatisticsUse information in sample to make conclusions about the populationpoint estimationinterval estimation18Design of StudiesHow collect the datarandom sampling etc.determines what population you can generalize the results toDesign of Experimentsefficient estimation of important effectsdetermines what causal conclusions can draw from the data19Goals of this courseFirst, effective ways for displaying and summarizing data (descriptive statistics)Second will study randomness and patterns Then we’ll know what we can learn about the population, and with what measures of uncertainty, from our sample20Data 29 30 29 29 30 27 28 27 31 26 30 28 28 29 30 28 28 30 28 29 30 31 29 30 26 30 30 31 32 31 34 36 33 30 32 34 35 36 31 31 29 33 36 32 33 35 29 34 34 36 28 28 31 24 26 26 23 26 26 24 * 24 25 25 24 30 30 27 27 29 27 30 24 25 24 26 28 29 * 32 26 24 25 23 27 31 29 30 29 31 24 26 38 28 27 27 26 28 24 28 26 * 28 27 24 25 26 27 27Variable = Miles per gallonHighwaySource = 1999 New Car Buyer’s Guide21Descriptive StatisticsDescribe the overall pattern of the data and any interesting departures from the patternType of data: quantitative (numerical) or categorical (groups, e.g., gender)Graphical summaryNumerical summary22ExamplesHighway MPG (1999 New Cars Buyer Guide)39342924151050hwy mpgFrequencyLABEL!23Describing DataSummarize the patterns in the observations (numerically and graphically)1) Shape2) Center3) Spread4) Unusual observations24What to Look For…1) Shapepositively skewedaka skewed to the rightnegatively skewedskewed to the leftsymmetric25What to Look For…1) ShapeHow many distinct peaks?Clusters/Gaps26ExamplesHighway MPG (1999 New Cars Buyer Guide)39342924151050hwy mpgFrequencyPositively skewed (to the right)27ExamplesFuel Capacity25201510151050fuel capFrequencySymmetric (roughly)28ExamplesWeights40003000200020100weightFrequencyNegatively skewed (to the left)29ExamplesTime to Travel ¼ mile19.018.518.017.517.016.516.015.515.014.514.0201001/4 mileFrequencyOutlier (guess which model)30Example 2: Rowers WeightsEach dot represents a rowerWeights of rowersNumber of rowersClusters/gaps (explain)Outlier (explain)31GraphsStem-and-leaf plots“stems” “leaves”leaf unit = 1.0–“leaf unit” tells you how to read numerical value–make sure leaves are ordered–display empty stemsweights32Lab 1 – Fan Cost IndexAssignment/data available from course webpageAsk questions during the week, due FridayGoalsMinitab practice, Applying concepts from Ch. 1Work with a partner on lab writeup!Possible issues:Copying graphsEquation editor33For TuesdayComplete questionnaire on Blackboard pageFinish reading syllabus/bring questionsMinitab accessSee online handout for freely downloading ownStart reading Chapter 1 in your textask questions on the