Business Statistics: A Decision-Making Approach, 7th edition

Business Statistics: A Decision-Making Approach7th EditionChapter 1The Where, Why, and How of Data CollectionWhat is Statistics? Statistics is the development and application of methods to collect, analyze and interpret data.Modern statistical methods involve the design and analysis of experiments and surveys, the quantification of biological, social and scientific phenomenon and the application of statistical principles to understand more about the world around us. Statistics is a discipline which is concerned with: designing experiments and other data collection, summarizing information to aid understanding, drawing conclusions from data, and estimating the present or predicting the future. Population vs. Sample a b c d ef gh i jk l m n o p q rs t u v w x y z

PopulationSample b c g i n o r u y

A Population is the set of all items or individuals of interest Examples: All likely voters in the next election All parts produced todayAll sales receipts for November

A Sample is a subset of the population Examples:1000 voters selected at random for interviewA few parts selected for destructive testingEvery 100th receipt selected for auditPopulations and SamplesWhy Sample?Less time consuming than a censusLess costly to administer than a censusIt is possible to obtain statistical results of a sufficiently high precision based on samples.Sampling TechniquesConvenienceSampling TechniquesNonstatistical SamplingJudgmentStatistical Sampling Simple RandomSystematicStratifiedClusterNot interested inStatistical SamplingItems of the sample are chosen based on known or calculable probabilitiesStatistical Sampling(Probability Sampling)SystematicStratifiedClusterSimple RandomVideo ClipPlease read the bookExample of Random SamplingSuggesting how the statistical sampling techniques can be used to gather data on employees' preferences for scheduling vacation times.Simple random sampling could be used by assigning each employee a number and then using a random number generator to select employees. Table and Excel Toolpak

Simple Random SamplingEvery possible sample of a given size has an equal chance of being selectedSelection may be with replacement or without replacementThe sample can be obtained using a table of random numbers or computer random number generator

Descriptive statisticsMathematicalmethods(such as mean,median,standard deviation) that summarize and interpret some of the propertiesof a set ofdata(sample) but do not infer the properties of thepopulationfrom which the sample was drawn. Inferential statisticsMathematicalmethods(such ashypothesisdevelopment) that employprobability theoryfor deducing (inferring) the propertiesof apopulationfrom theanalysisof the properties of a set ofdata(sample) drawn from it. It is concerned also with theprecisionandreliabilityof the inferencesit helps draw.Type of StatisticsDescriptive StatisticsCollect datae.g., Survey, Observation, ExperimentsPresent datae.g., Charts and graphsCharacterize datae.g., Sample mean =

Making statements about a population by examining sample resultsSample statistics Population parameters (known) Inference (unknown, but can be estimated from sample evidence)

Inferential StatisticsInferential StatisticsEstimatione.g., Estimate the population mean weight using the sample mean weightHypothesis Testinge.g., Use sample evidence to test the claim that the population mean weight is 120 poundsDrawing conclusions and/or making decisions concerning a population based on sample results.

Tools for Collecting DataData Collection MethodsWritten questionnairesExperimentsTelephone surveysDirect observation and personal interview

Survey Design StepsDefine the issuewhat are the purpose and objectives of the survey?Define the population of interestDevelop survey questionsmake questions clear and unambiguoususe universally-accepted definitionslimit the number of questionsSurvey Design StepsPre-test the surveypilot test with a small group of participantsassess clarity and lengthDetermine the sample size and sampling methodSelect sample and administer the survey(continued)Types of QuestionsClosed-end QuestionsSelect from a short list of defined choicesExample: Major: __business__liberal arts __science__other Open-end QuestionsRespondents are free to respond with any value, words, or statementExample: What did you like best about this course?

Demographic QuestionsQuestions about the respondents personal characteristicsExample: Gender: __Female __ MaleData (variable) TypesExamples:Marital StatusPolitical PartyEye Color (Defined categories)Examples:Number of ChildrenDefects per hour (Counted items)Examples:WeightVoltage (Measured characteristics)Qualitative vs. Quantitative Variables (Data)Qualitative variables (data) take on values that are names or labels. Example: the color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier) Quantitative variables are numerical. They represent a measurable quantity. Example: # of students in CSUB or # of people in Bakersfield

Discrete vs. Continuous Variables (Data)Quantitative variables can be further classified asdiscreteorcontinuous. If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable; otherwise, it is called a discrete variable.Example: The fire department mandates that all fire fighters must weigh between 150 and 250 pounds. The weight of a fire fighter would be an example of a continuous variable; since a fire fighter's weight could take on any value between 150 and 250 pounds.

Discrete vs. Continuous Variables (Data)Example: If we flip a coin and count the number of heads. The number of heads could be any integer value between 0 and plus infinity. However, it could not be any number between 0 and plus infinity. That is, we could not, for example, get 2.3 heads. Therefore, the number of heads must be a discrete variable.

Data Measurement LevelsRatio/Interval DataOrdinal Data Nominal DataHighest LevelComplete AnalysisHigher LevelMid-level AnalysisLowest LevelBasic AnalysisCategorical Codes ID Numbers Category NamesRankings Ordered CategoriesMeasurementsBusiness Statistics: A Decision-Making Approach, 7e 2008 Prentice-Hall, Inc.Chapter 1Instructor Notes1-#NominalNominalbasically refers to categoricallydiscrete data such as name of your school, type of car you drive or name of a book. This one is easy to remember becausenominal sounds like name(they have the same Latin root).OrdinalOrdinalrefers to quantities that have a natural ordering. The ranking of favorite sports, the order of people's place in a line, the order of runners finishing a race or more often the choice on a rating scale from 1 to 5. With ordinal data you cannot state with certainty whether the intervals between each value are equal. For example, we often using rating scales (Likert-Scale questions). On a 10 point scale, the difference between a 9 and a 10 is not necessarily the same difference as the difference between a 6 and a 7. This is also an easy one to remember,ordinal sounds like order.IntervalIntervaldata is like ordinal except we can say the intervals between each value are equally split. The most common example is temperature in degrees Fahrenheit. The difference between 29 and 30 degrees is the same magnitude as the difference between 78 and 79 (although I know I prefer the latter). With attitudinal scales and the Likert questions you usually see on a survey, these are rarely interval, although many points on the scale likely are of equal intervals.RatioRatiodata is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. Degrees Kelvin has a 0 point (absolute 0) and the steps in both these scales have the same degree of magnitude.Data TypesTime Series DataOrdered data values observed over time

Cross Section DataData values observed at a fixed point in timeData TypesSales (in $1000s)2003200420052006Atlanta435460475490Boston320345375395Cleveland405390410395Denver260270285280Time Series DataCross Section Data