000 31071cam a2206613Ii 4500
001 160396
008 091030s20112011njua b 001 0 eng
020 _a0136113508 (paperback)
020 _a9780136113508 (paperback)
040 _aMEF
_beng
_erda
049 _aTR-IsMEF
050 0 0 _aHF1017
_b.B87 2011
245 0 0 _aBusiness statistics :
_ba decision-making approach /
_cDavid F. Groebner, Boise State University, Professor Emeritus of Production Management, Patrick W. Shannon, Boise State University, Dean of the College of Business and Economics, Phillip C. Fry, Boise State University, Professor, ITSCM Department Chair, Kent D. Smith, California Polytechnic University, Professor Eneritus of Statistics.
250 _aEighth edition, International edition.
264 1 _aUpper Saddle River, N.J. :
_bPrentice Hall/Pearson,
_c2011.
264 4 _a©2011
300 _a936 pages :
_billustrations (chiefly color) ;
_c28 cm.
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
338 _avolume
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aThe where, why, and how of data collection -- Graphs, charts, and tables : describing your data -- Describing data using numerical measures -- Introduction to probability -- Discrete probability distributions -- Introduction to continuous probability distributions -- Introduction to sampling distributions -- Estimating single population parameters -- Introduction to hypothesis testing -- Estimation and hypothesis testing for two population parameters -- Hypothesis tests and estimation for population variances -- Analysis of variance -- Goodness-of-fit tests and contingency analysis -- Introduction to linear regression and correlation analysis -- Multiple regression analysis and model building -- Analyzing and forecasting time-series data -- Introduction to nonparametric statistics -- Introduction to quality and statistical process control.
520 _aFor the 1 or 2 semester course in Business Statistics. This comprehensive, 17 chapter hardcover text builds student confidence by incorporating a step-by-step system for examples, exercises, and special review sections. This step-by-step framework allows students to learn by example, practice with extensive exercises that step-up in level of difficulty, and solidify their understanding of the concepts with special review sections as they prepare for their exams. It presents descriptive and inferential statistics with a rich assortment of business examples and real data with an emphasis on decision-making. There is emphasis on using statistical software as a tool, (featuring Excel and Minitab) with many examples presented in a software environment. A briefer version is also available called A Course in Business Statistics 4e.
650 0 _aCommercial statistics.
650 0 _aStatistical decision.
700 1 _aGroebner, David F.,
_eauthor.
700 1 _aShannon, Patrick W.,
_eauthor.
700 1 _aFry, Phillip C.,
_eauthor.
700 1 _aSmith, Kent D.,
_eauthor.
900 _aMEF Üniversitesi Kütüphane katalog kayıtları RDA standartlarına uygun olarak üretilmektedir / MEF University Library Catalogue Records are Produced Compatible by RDA Rules
942 _2lcc
_cBKS
970 0 1 _aPreface,
_p17.
970 1 2 _tChapter 1 the where, why, and how of data collection,
_p25.
970 1 2 _tWhat is business statistics?,
_p26.
970 1 2 _tDescriptive statistics,
_p26.
970 1 1 _tCharts and graphs,
_p27.
970 1 2 _tInferential procedures,
_p29.
970 1 1 _tEstimation,
_p29.
970 1 1 _tHypothesis testing,
_p29.
970 1 2 _tProcedures for collecting data,
_p31.
970 1 1 _tData collection methods,
_p31.
970 1 1 _tWritten questions and surveys,
_p33.
970 1 1 _tDirect observation and personal interviews,
_p35.
970 1 2 _tOther data collection methods,
_p35.
970 1 2 _tData collection issues,
_p36.
970 1 1 _tInterviews bias,
_p36.
970 1 1 _tNonrespose bias,
_p36.
970 1 1 _tSelection bias,
_p36.
970 1 1 _tObserver bias,
_p36.
970 1 1 _tMeasurement error,
_p37.
970 1 1 _tInternal validity,
_p37.
970 1 1 _tExternal validity,
_p37.
970 1 2 _tPopulations, samples and samplinh techniques,
_p38.
970 1 2 _tPopulations and samples,
_p38.
970 1 1 _tParameters and statistics,
_p39.
970 1 2 _tSampling techniques,
_p39.
970 1 1 _tStatistics sampling,
_p40.
970 1 2 _tData types and data measurement levels,
_p44.
970 1 2 _tQuantative and qualitative data,
_p45.
970 1 2 _tTime-series data and cross-sectional data,
_p45.
970 1 2 _tData measurement levels,
_p45.
970 1 1 _tNominal data,
_p45.
970 1 1 _tOrdinal data,
_p46.
970 1 1 _tInterval data,
_p46.
970 1 1 _tRatio data,
_p46.
970 0 1 _aVisiual summary,
_p50.
970 0 1 _aKey terms,
_p52.
970 0 1 _aChapter exercises,
_p52.
970 1 1 _tVideo case 1: statistical data collection @McDonald's,
_p53.
970 0 1 _aReferences,
_p53.
970 1 2 _tGraphs, charts, and tables-describing your data,
_p55.
970 1 2 _tFrequency distributions and histograms,
_p56.
970 1 2 _tGrouped data frequency distributions,
_p60.
970 1 1 _tSteps for grouping data into classes,
_p63.
970 1 2 _tHistograms,
_p65.
970 1 1 _tIssues with excel,
_p68.
970 1 2 _tRelative frequency histograms and ogives,
_p69.
970 1 2 _tJoint frequency distributions,
_p71.
970 1 2 _tBar charts, pie charts, and stem and leaf diagrams,
_p78.
970 1 2 _tBar charts,
_p78.
970 1 2 _tPie charts,
_p84.
970 1 2 _tStem and leaf diagrams,
_p86.
970 1 2 _tLine charts and scater diagrams,
_p90.
970 1 2 _tLine charts,
_p90.
970 1 2 _tScatter diagrams,
_p94.
970 1 1 _tPersonal computers,
_p94.
970 0 1 _aVisiual summary,
_p100.
970 0 1 _aEquations,
_p101.
970 0 1 _aKey terms,
_p101.
970 0 1 _aChapter exercises,
_p101.
970 1 1 _tVideo case 3: drive-thru service times @McDonald's,
_p104.
970 1 1 _tCase 2.1: server downtime,
_p105.
970 1 1 _tCase 2.2: yakima Apples, Inc.,
_p105.
970 1 1 _tCase 2.3: Welco Lumber company-Part,
_p107.
970 0 1 _aReferences,
_p108.
970 1 2 _tDescribing data using numerical measures,
_p109.
970 1 2 _tMeasures of center and location,
_p109.
970 1 2 _tParameters and statistics,
_p110.
970 1 2 _tPopulation mean,
_p110.
970 1 2 _tSample mean,
_p113.
970 1 2 _tThe impact of extreme values on the mean,
_p114.
970 1 2 _tMedian,
_p115.
970 1 2 _tSkewed and symmetric distributions,
_p116.
970 1 2 _tMode,
_p117.
970 1 2 _tApplying the measures of central tendency,
_p118.
970 1 1 _tIssues with excel,
_p120.
970 1 2 _tOther measures of location,
_p121.
970 1 1 _tWeighted mean,
_p121.
970 1 1 _tPercentiles,
_p122.
970 1 1 _tQuartiles,
_p123.
970 1 1 _tIssues with excel,
_p124.
970 1 2 _tBox and whisker plots,
_p124.
970 1 2 _tData-level issues,
_p126.
970 1 2 _tMeasures of variation,
_p131.
970 1 2 _tRange,
_p131.
970 1 2 _tInterquartile range
_p132.
970 1 2 _tPopulation variance and standard deviation,
_p133.
970 1 2 _tSample variance and standard deviation,
_p136.
970 1 2 _tUsing the mean and standard deviation together,
_p142.
970 1 2 _tCoefficient of variation,
_p142.
970 1 1 _tThe emprical rule,
_p144.
970 1 2 _tTchebysheff's theorem,
_p145.
970 1 2 _tStandardized data values,
_p146.
970 0 1 _aVisiual summary,
_p152.
970 0 1 _aEquations,
_p153.
970 0 1 _aKey terms,
_p154.
970 0 1 _aChapter exercises,
_p154.
970 1 2 _tVideo case 3: drive-thru service times @McDonald's,
_p159.
970 1 1 _tCase 3.1: WGI-human resources,
_p159.
970 1 1 _tCase 3.2: National call center,
_p160.
970 1 1 _tCase 3.3: Welco lumber company-Part B,
_p161.
970 1 1 _tCase 3.4: AJ's fitness center,
_p161.
970 0 1 _aReferences,
_p162.
970 1 2 _tChapter 1-3 special review section,
_p163.
970 1 1 _tChapters 1-3,
_p163.
970 0 1 _aExercises,
_p166.
970 0 1 _aReview case 1: state department of insurance,
_p168.
970 0 1 _aTerm project assignments,
_p168.
970 1 2 _tChapter 4 introduction to probability,
_p170.
970 1 2 _tThe basics of probability,
_p171.
970 1 2 _tImportant probability terms,
_p171.
970 1 1 _tEvents and sample space,
_p171.
970 1 1 _tUsing tree diagrams,
_p172.
970 1 1 _tMutually exclusive events,
_p174.
970 1 1 _tIndependent and dependent events,
_p174.
970 1 2 _tMethods of assigning probability,
_p176.
970 1 1 _tClassical probability assessment,
_p176.
970 1 1 _tRelative frequency assessment,
_p177.
970 1 1 _tSubjective probability assessment,
_p179.
970 1 2 _tThe rules of probability,
_p183.
970 1 2 _tMeasuring probabilities,
_p183.
970 1 1 _tPossible values and the summation of possible values,
_p183.
970 1 1 _tAddition rule for individual outcomes,
_p184.
970 1 1 _tComplement rule,
_p186.
970 1 1 _tAddition rule for two events,
_p187.
970 1 1 _tAddition rule for mutually events,
_p191.
970 1 2 _tConditional probability,
_p191.
970 1 1 _tTree diagrams,
_p194.
970 1 1 _tConditional probability for independent events,
_p195.
970 1 2 _tMultiplaction rule,
_p196.
970 1 1 _tMultiplation rule for two events,
_p196.
970 1 1 _tUsing a tree diagram,
_p197.
970 1 1 _tMultiplication rule for independent events,
_p198.
970 1 2 _tBayers' theorem,
_p199.
970 0 1 _aVisual summary,
_p209.
970 0 1 _aEquations,
_p210.
970 0 1 _aKey terms,
_p210.
970 0 1 _aChapter exercises,
_p210.
970 1 1 _tCase 4.1: great air commuter service,
_p213.
970 1 1 _tCase 4.2: let's make a deal,
_p214.
970 0 1 _aReferences,
_p214.
970 1 2 _tDiscrete probability distributions,
_p215.
970 1 2 _tIntroduction to discrete probability distributions,
_p216.
970 1 2 _tRandom variables,
_p216.
970 1 1 _tDisplaying discrete probability distribution,
_p223.
970 1 2 _tMean and standard deviation of discrete distributions,
_p217.
970 1 1 _tCalculating the mean,
_p217.
970 1 1 _tCalculating the standard deviation,
_p218.
970 1 2 _tThe binomial probability distribution,
_p223.
970 1 2 _tThe binomial distribution,
_p223.
970 1 2 _tCharacteristics of the binomial distribution,
_p223.
970 1 1 _tCombinations,
_p225.
970 1 1 _tBinomial formula,
_p226.
970 1 1 _tUsing the binomial distribution table,
_p228.
970 1 1 _tMean and standard deviation of the binomial distribution,
_p229.
970 1 1 _tAdditional information about the binomial distribution,
_p232.
970 1 2 _tOther discrete probability distributions,
_p237.
970 1 2 _tThe poisson distribution,
_p237.
970 1 1 _tCharacteristics of the poisson distribution,
_p237.
970 1 1 _tPoisson probability distribution table,
_p238.
970 1 1 _tThe mean and standard deviation of the poisson distribution,
_p241.
970 1 2 _tThe hypergeometric distribution,
_p241.
970 1 1 _tThe hypergeometric distribution with more than two possible outcomes per trial,
_p246.
970 0 1 _aVisual summary,
_p250.
970 0 1 _aEquations,
_p251.
970 0 1 _aKey terms,
_p251.
970 0 1 _aChapter exercises,
_p251.
970 1 1 _tCase 5.1: savemor pharmacies,
_p254.
970 1 1 _tCase 5.2: arrowmark vending,
_p255.
970 1 1 _tCase 5.3: boise cascade corporation,
_p256.
970 0 1 _aReferences,
_p256.
970 1 2 _tChapter 6 introduction to continuous probability distributions,
_p257.
970 1 2 _tThe normal probability distribution,
_p258.
970 1 2 _tThe normal distribution,
_p258.
970 1 2 _tThe standard normal distribution,
_p259.
970 1 1 _tUsing the standard normal table,
_p261.
970 1 1 _tApproximate areas under the normal curve,
_p269.
970 1 2 _tOther continuous probability distributions,
_p273.
970 1 2 _tUniform probability distribution,
_p273.
970 1 2 _tThe exponential probability distribution,
_p276.
970 0 1 _aVisiual summary,
_p282.
970 0 1 _aEquations,
_p283.
970 0 1 _aKey terms,
_p283.
970 0 1 _aChapter exercises,
_p283.
970 1 1 _tCase 6.1: state entitlement programs,
_p286.
970 1 1 _tCase 6.2: credit data, Inc.,
_p287.
970 1 1 _tCase 6.3: American oil company,
_p287.
970 0 1 _aReferences,
_p287.
970 1 2 _tIntroduction to sampling distributions,
_p288.
970 1 2 _tSampling error: what it is and why it happens,
_p289.
970 1 2 _tCalculating sampling error,
_p289.
970 1 1 _tThe role of sample size in sampling error,
_p292.
970 1 2 _tSampling distribution of the mean,
_p297.
970 1 2 _tSimulation the sampling distribution for x,
_p298.
970 1 1 _tSampling from normal populations,
_p301.
970 1 2 _tThe central limit theorem,
_p306.
970 1 2 _tSampling distribution of a proportion,
_p313.
970 1 2 _tWorking with proportions,
_p313.
970 1 2 _tSamplinh distribution of p,
_p315.
970 0 1 _aVisiual summary,
_p322.
970 0 1 _aEquations,
_p323.
970 0 1 _aKey terms,
_p323.
970 0 1 _aChapter exercises,
_p323.
970 1 1 _tCase 7.1: carpita bottling company,
_p327.
970 1 1 _tCase 7.2: truck safety inscpection,
_p327.
970 0 1 _aReferences,
_p328.
970 1 2 _tEstimating single population parameters,
_p329.
970 1 2 _tPoint and confidence interval estimates for a population mean,
_p330.
970 1 2 _tPoint estimates and confidence intervals,
_p330.
970 1 2 _tConfidence interval estimate for the population mean, σ unknown,
_p332.
970 1 1 _tConfidence interval calculation,
_p333.
970 1 1 _tImpact of the confidence level on the interval estimate,
_p335.
970 1 1 _tImpact of the sample size on the interval estimate,
_p338.
970 1 2 _tConfidence interval estimates for the population mean, σ unknown,
_p338.
970 1 2 _tStudent's t-distribution,
_p338.
970 1 1 _tEstimation with larger sample sizes,
_p344.
970 1 2 _tDetermining the required sample size for estimating a population mean,
_p348.
970 1 2 _tDetermining the required sample size for estimating μ, σ known,
_p349.
970 1 2 _tDetermining the required sample size for estimating μ, σ unknown,
_p350.
970 1 2 _tEstimating a population proportion,
_p354.
970 1 2 _tConfidence interval estimate for a population proportion,
_p355.
970 1 2 _tDetermining the required sample size for estimating a population proportion,
_p357.
970 0 1 _aVisiual summary,
_p363.
970 0 1 _aEquations,
_p364.
970 0 1 _aKey terms,
_p364.
970 0 1 _aChapter exercises,
_p364.
970 1 1 _tVideo case 4: new product introductions @McDonald's,
_p367.
970 1 1 _tCase 8.1: management solutions, Inc.,
_p367.
970 1 1 _tCase 8.2: federal aviation administration,
_p368.
970 1 1 _tCase 8.3: cell phone use,
_p368.
970 0 1 _aReferences,
_p369.
970 1 2 _tIntroduction to hypothesis testing,
_p370.
970 1 2 _tHypothesis tests for means,
_p371.
970 1 2 _tFormulating the hypotheses,
_p371.
970 1 1 _tNull and alternative hypotheses,
_p371.
970 1 1 _tTesting the status quo,
_p371.
970 1 1 _tTesting a research hypothesis,
_p372.
970 1 1 _tTesting a claim about the population,
_p372.
970 1 1 _tTypes of statistical errors,
_p374.
970 1 2 _tSignificance level and critical value,
_p375.
970 1 2 _tHypothesis test for μ, σ known,
_p376.
970 1 1 _tCalculating critical values,
_p376.
970 1 1 _tDecision rules and test statistics,
_p378.
970 1 1 _tp-value value approach,
_p381.
970 1 2 _tTypes of hypothesis tests,
_p382.
970 1 2 _tp-value for two-tailed tests,
_p383.
970 1 2 _tHypothesis test for μ, σ unknown,
_p385.
970 1 2 _tHypothesis tests for a proportion,
_p392.
970 1 2 _tTesting a hypothesis about a single population proportion,
_p392.
970 1 2 _tType II errors,
_p400.
970 1 2 _tCalculating beta,
_p400.
970 1 2 _tControlling alpha and beta,
_p402.
970 1 2 _tPower of the test,
_p406.
970 0 1 _aVisual summary,
_p411.
970 0 1 _aEquations,
_p412.
970 0 1 _aKey terms,
_p413.
970 0 1 _aChapter exercises,
_p413.
970 1 1 _tVideo case 4: new product introductions @McDonald's,
_p418.
970 1 1 _tCase 9.1: campbell brewery, Inc.-Part 1,
_p418.
970 1 1 _tCase 9.2: wings of fire,
_p419.
970 0 1 _aReferences,
_p420.
970 1 2 _tChapter 10 estimation and hypothesis testing for two population parameters,
_p421.
970 1 2 _tEstimation for two population means using independent samples,
_p422.
970 1 2 _tEstimating the difference between two population means when σ₁ and σ₂ are known, using independent samples,
_p422.
970 1 2 _tEstimating the difference between two means when σ₁ and σ₂ are unknown, using independent samples,
_p424.
970 1 1 _tWhat if the population variances are not equal?,
_p428.
970 1 2 _tHypothesis tests for two population means using independent samples,
_p433.
970 1 2 _tTesting for μ₁-μ₂ when σ₁ and σ₂ are known, using independent samples,
_p433.
970 1 1 _tUsing p-values,
_p436.
970 1 2 _tTesting for μ₁-μ₂ when σ₁ and σ₂ are unknown, using independent samples,
_p436.
970 1 1 _tWhat if the population variances are not equal?,
_p443.
970 1 2 _tInterval estimation and hypothesis tests for paired samples,
_p447.
970 1 2 _tWhy use paired samples?,
_p447.
970 1 2 _tHypothesis testing for paired samples,
_p451.
970 1 2 _tEstimation and hypothesis tests for two population proportions,
_p456.
970 1 2 _tEstimating the difference between two population proportions,
_p456.
970 1 2 _tHypothesis tests for the difference between two population proportions,
_p457.
970 0 1 _aVisiual summary,
_p464.
970 0 1 _aEquations,
_p465.
970 0 1 _aKey terms,
_p466.
970 0 1 _aChapter exercises,
_p466.
970 1 1 _tCase 10.1: motive power company-part 1,
_p469.
970 1 1 _tCase 10.2: Hamilton marketing services,
_p470.
970 1 1 _tCase 10.3: green valley assembly company,
_p470.
970 1 1 _tCase 10.4: u-need-it rental agency,
_p471.
970 0 1 _aReferences,
_p471.
970 1 2 _tHypothesis tests and estimation for population variances,
_p472.
970 1 2 _tHypothesis tests and estimation for a single population variance,
_p473.
970 1 1 _tChi-square test for one population variance,
_p473.
970 1 1 _tInterval estimation for a population variance,
_p478.
970 1 2 _tHypothesis tests for two population variances,
_p482.
970 1 1 _tF-Test for two population variances,
_p482.
970 1 1 _tAdditional F-test considerations,
_p491.
970 0 1 _aVisiual summary,
_p494.
970 0 1 _aEquations,
_p495.
970 0 1 _aKey term,
_p495.
970 0 1 _aChapter exercises,
_p495.
970 1 1 _tCase 11.1: motive power company-Part 2,
_p498.
970 0 1 _aReferences,
_p498.
970 1 2 _tAnalysis of variance,
_p499.
970 1 2 _tOne-way analysis of variance,
_p500.
970 1 2 _tIntroduction to one-way ANOVA,
_p500.
970 1 2 _tPartitioning the sum of squares,
_p501.
970 1 2 _tThe ANOVA assumptions,
_p502.
970 1 2 _tApplying one-way ANOVA,
_p505.
970 1 1 _tThe Turkey-Kramer procedure for multiple comparisons,
_p512.
970 1 2 _tFixed effects versus random effects in analysis of variance,
_p517.
970 1 2 _tRandomized complete block analysis of variance,
_p521.
970 1 2 _tRandomized complete block ANOVA,
_p521.
970 1 1 _tWas booking necessary,
_p524.
970 1 2 _tFischer's least significant difference test,
_p529.
970 1 2 _tTwo-factor Analysis of variance with replication,
_p533.
970 1 2 _tTwo-factor ANOVA with replications,
_p534.
970 1 1 _tInteraction explained,
_p536.
970 1 2 _tA caution about interaction,
_p541.
970 0 1 _aVisiual summary,
_p545.
970 0 1 _aEquations,
_p546.
970 0 1 _aKey terms,
_p546.
970 0 1 _aChapter exercises,
_p546.
970 1 1 _tVideo case 3: drive-thru service times @McDonald's,
_p550.
970 1 1 _tCase 12.1: agency for new Americans,
_p550.
970 1 1 _tCase 12.2: McLaughlin Salmon works,
_p551.
970 1 1 _tCase 12.3: NW Pulp and paper,
_p551.
970 1 1 _tCase 12.4: quinn restoration,
_p552.
970 1 1 _tBusiness statistics capstone project,
_p552.
970 0 1 _aReferences,
_p553.
970 1 2 _tChapters 8-12 special review section,
_p554.
970 0 1 _aChapter 8-12,
_p554.
970 1 1 _tUsing the flow diagrams,
_p567.
970 0 1 _aExercises
_p568.
970 1 1 _tTeam project assignments,
_p570.
970 1 1 _tBusiness statistics capstone project,
_p570.
970 1 2 _tChapter 13 goodness-of-fit tests and contingency analysis,
_p571.
970 1 2 _tIntroduction to goodness-of-fit tests,
_p572.
970 1 2 _tChi-square goodness-of-fit test,
_p572.
970 1 2 _tIntroduction to contingency analysis,
_p586.
970 1 2 _t2x2 contingency tables,
_p586.
970 1 2 _trxc contingency tables,
_p590.
970 1 2 _tChi-square test limitations,
_p593.
970 0 1 _aVisiual summary,
_p597.
970 0 1 _aEquations,
_p598.
970 0 1 _aKey term,
_p598.
970 0 1 _aChapter exercises,
_p598.
970 1 1 _tCase 13.1: American oil company,
_p601.
970 1 1 _tCase 13.2: bentford electronics-part 1,
_p601.
970 0 1 _aReferences,
_p602.
970 1 2 _tChapter 14 introduction to linear regression and correlation analysis,
_p603.
970 1 2 _tScatter plots and correlation,
_p604.
970 1 2 _tThe correlayion coefficient,
_p604.
970 1 1 _tSignificance test for the correlation,
_p606.
970 1 1 _tCause-and-effect interpretations,
_p610.
970 1 2 _tSimple linear regression analysis,
_p613.
970 1 2 _tThe regression model and assumptions,
_p614.
970 1 2 _tMeaning of the regression coefficents,
_p615.
970 1 2 _tLeast squares regression proporties,
_p620.
970 1 2 _tSignificance tests in regression analysis,
_p623.
970 1 1 _tThe coefficient of determination, R² ,
_p624.
970 1 1 _tSignificance of the slope coefficient,
_p628.
970 1 2 _tUses for regression analysis,
_p636.
970 1 1 _tRegression analysis for description,
_p636.
970 1 1 _tRegression analysis for prediction,
_p639.
970 1 1 _tConfidence interval for the average y, given x,
_p640.
970 1 1 _tPrediction interval for a particular y, given x,
_p640.
970 1 2 _tCommon problems using regression analysis,
_p642.
970 0 1 _aVisual summary,
_p648.
970 0 1 _aEquations,
_p649.
970 0 1 _aKey terms,
_p650.
970 0 1 _aChapter exercises,
_p650.
970 1 1 _tCase 14.1: A&A industrial products,
_p654.
970 1 1 _tCase 14.2: Sapphire coffee-Part 1,
_p654.
970 1 1 _tCase 14.3: Alamar industries,
_p655.
970 1 1 _tCase 14.4: continental trucking,
_p655.
970 0 1 _aReferences,
_p656.
970 1 2 _tMultiple regression analysis and model building,
_p657.
970 1 2 _tIntroduction to multiple regression analysis,
_p658.
970 1 2 _tBasic model-building concepts,
_p660.
970 1 1 _tModel specification,
_p660.
970 1 1 _tModel building,
_p661.
970 1 1 _tModel diagnosis,
_p661.
970 1 1 _tComputing the regression equation,
_p664.
970 1 1 _tThe coefficient of determination,
_p666.
970 1 1 _tModel diagnosis,
_p667.
970 1 1 _tIs the model significant?,
_p667.
970 1 1 _tAre the individual variables significant?,
_p669.
970 1 1 _tIs the standard deviation of the regression model too large?,
_p670.
970 1 1 _tIs multicollinearity a problem?,
_p671.
970 1 1 _tConfidence interval estimation for regression coefficients,
_p673.
970 1 2 _tUsing qualitative independent variables,
_p678.
970 1 1 _tPossible improvements to the first city apprisal model,
_p681.
970 1 2 _tWorking with nonlinear relationships,
_p685.
970 1 2 _tAnalyzing interaction effects,
_p691.
970 1 2 _tThe partial F-test,
_p695.
970 1 2 _tStepwise regression,
_p702.
970 1 2 _tForward selection,
_p702.
970 1 2 _tBackward elimination,
_p703.
970 1 2 _tStandard stepwise regression,
_p707.
970 1 2 _tBest subsets regression,
_p707.
970 1 2 _tDetermining the apthess of the model,
_p713.
970 1 2 _tAnalysis of residuals,
_p713.
970 1 1 _tChecking up linearity,
_p714.
970 1 1 _tDo the residuals have equal variances at all levels of each x variable?,
_p716.
970 1 1 _tAre the residuals independent?,
_p717.
970 1 1 _tChecking for normally distributed error terms,
_p717.
970 1 2 _tCorrective actions,
_p713.
970 0 1 _aVisiual summary,
_p724.
970 0 1 _aEquations,
_p725.
970 0 1 _aKey terms,
_p725.
970 0 1 _aChapter exercises,
_p725.
970 1 1 _tCase 15.1: dynamic scales, Inc.,
_p729.
970 1 1 _tCase 15.2: glaser machine works,
_p730.
970 1 1 _tCase 15.3: Hawlins manufacturing,
_p730.
970 1 1 _tCase 15.4: Sapphire coffee-part 2,
_p731.
970 1 1 _tCase 15.5: Wendell motors,
_p731.
970 0 1 _aReferences,
_p732.
970 1 2 _tChapter 16 analyzing and forecasting time-series data,
_p733.
970 1 2 _tIntroduction to forecasting, time-series data, and index numbers,
_p734.
970 1 2 _tComponents of a time series,
_p735.
970 1 1 _tTried component,
_p735.
970 1 1 _tSeasonal component,
_p736.
970 1 1 _tCyclical component,
_p737.
970 1 1 _tRandom component,
_p737.
970 1 2 _tIntroduction to index numbers,
_p738.
970 1 2 _tAggregate price indexes,
_p739.
970 1 2 _tWeighted aggregated price indexes,
_p741.
970 1 1 _tThe paasche index,
_p741.
970 1 1 _tThe laspeyres index,
_p742.
970 1 2 _tCommonly used index numbers,
_p743.
970 1 1 _tConsumer price index,
_p743.
970 1 1 _tProducer price index,
_p744.
970 1 2 _tStock market indexes,
_p744.
970 1 2 _tUsing index numbers to deflate a time series,
_p745.
970 1 2 _tTrend-based forecasting techniques,
_p748.
970 1 2 _tDeveloping a trend-based forecasting model,
_p748.
970 1 2 _tComparing the forecast values to the actual data,
_p751.
970 1 1 _tAutocorrelation,
_p752.
970 1 1 _tTrue forecasts,
_p756.
970 1 2 _tNonlinear trend forecasting,
_p758.
970 1 1 _tSome words of caution,
_p762.
970 1 2 _tAdjusting for seasonality,
_p762.
970 1 1 _tComputing seasonal indexes,
_p763.
970 1 1 _tThe need to normalize the indexes,
_p765.
970 1 1 _tDeseasonalizing,
_p766.
970 1 1 _tUsing dummy variables to represent seasonality,
_p768.
970 1 2 _tForecasting using smoothing methods,
_p774.
970 1 2 _tExponential smoothing,
_p774.
970 1 1 _tSingle exponential smoothing,
_p774.
970 1 1 _tDouble exponential smoothing,
_p779.
970 0 1 _aVisual summary,
_p786.
970 0 1 _aEquations,
_p787.
970 0 1 _aKey terms,
_p787.
970 0 1 _aChapter exercises,
_p788.
970 1 1 _tVideo case 2: restaurant location and re-imaging decisions @McDonald's,
_p790.
970 1 1 _tCase 16.1: park falls chamber of commerce,
_p791.
970 1 1 _tCase 16.2: the St. Louis companies,
_p792.
970 1 1 _tCase 16.3: wagner machine works,
_p792.
970 0 1 _aReferences,
_p793.
970 1 2 _tChapter 17 Introduction to nonparametric statistics,
_p794.
970 1 2 _tThe wilcoxon signed rank test for one population median,
_p795.
970 1 2 _tThe wilcoxon signed rank test-single population,
_p795.
970 1 2 _tNonparametric tests for two population medians,
_p800.
970 1 2 _tThe mann-whitney U-test,
_p800.
970 1 2 _tMann-whitney u-test-large samples,
_p804.
970 1 1 _tThe wilcoxon matched-pairs signed rank test,
_p806.
970 1 1 _tTest in the data,
_p808.
970 1 1 _tLarge-samle wilcoxon test,
_p808.
970 1 2 _tKruskal-wallis one-way analysis of variance,
_p813.
970 1 2 _tLimitations and other considerations,
_p817.
970 0 1 _aVisual summary,
_p821.
970 0 1 _aEquations,
_p822.
970 0 1 _aChapter exercises,
_p823.
970 1 1 _tCase 17.1: bentford electronics-part 2,
_p826.
970 0 1 _aReferences,
_p827.
970 1 2 _tChapter 18 introduction to quality and statistical process control,
_p828.
970 1 2 _tQuality management and tools for process improvement,
_p829.
970 1 2 _tThe tools of quality for process improvement,
_p830.
970 1 1 _tProcess flowcharts,
_p831.
970 1 1 _tBrainstorming,
_p831.
970 1 1 _tFishbone diagram,
_p831.
970 1 1 _tHistograms,
_p831.
970 1 1 _tTrend charts,
_p831.
970 1 1 _tScatter plots,
_p831.
970 1 1 _tStatistical process control charts,
_p831.
970 1 2 _tIntroduction to statistical process control charts,
_p832.
970 1 2 _tThe existence of variation,
_p832.
970 1 1 _tSources of variation,
_p832.
970 1 1 _tTypes of variation,
_p833.
970 1 1 _tThe predictability of variation: understanding the normal distribution,
_p834.
970 1 1 _tThe concept of stability,
_p834.
970 1 2 _tIntroducing statistical process control charts,
_p834.
970 1 2 _tx-chart and r-chart,
_p835.
970 1 1 _tUsing the control charts,
_p842.
970 1 1 _tp-charts,
_p844.
970 1 1 _tUsing the p-chart,
_p847.
970 1 1 _tc-charts,
_p848.
970 1 1 _tOther control charts,
_p850.
970 0 1 _aVisiual summary,
_p855.
970 0 1 _aEquations,
_p856.
970 0 1 _aKey terms,
_p857.
970 0 1 _aChapter exercises,
_p857.
970 1 2 _tCase 18.1: izbar precision casters, Inc.,
_p858.
970 0 1 _aReferences,
_p859.
970 1 1 _tAppendices,
_p860.
970 1 1 _tRandom numbers table,
_p861.
970 1 1 _tCumulative binomial distribution table,
_p862.
970 1 1 _tCumulative poisson probability distribution table,
_p875.
970 1 1 _tStandard normal distribution table,
_p880.
970 1 1 _tExponential distribution table,
_p881.
970 1 1 _tValues of t for selected probabilities,
_p882.
970 1 1 _tValues of x² for selected probabilities,
_p883.
970 1 1 _tF-distribution table,
_p884.
970 1 1 _tCritical values of hartley's fmax test,
_p890.
970 1 1 _tDistribution of the studentized range (q-values),
_p891.
970 1 1 _tCritical values of r in the runs test,
_p893.
970 1 1 _tMann-whitney u test probabilities (n<9),
_p894.
970 1 1 _tMann-whitney u test critical values (9_< n _<20),
_p896.
970 1 1 _tCritical values of T in the wilcoxon matched-pairs signed-ranks test (n_<25),
_p898.
970 1 1 _tCritical values dL and dU of the Durbin-Watson statistic D,
_p899.
970 1 1 _tLower and upper critical values W of Wilcoxon signed-ranks test,
_p901.
970 1 1 _tControl chart factors,
_p902.
970 0 1 _aAnswers to selected odd-numbered problems,
_p903.
970 0 1 _aGlossary,
_p924.
970 0 1 _aIndex,
_p930.
999 _c4497
_d4497
003 KOHA