000 26178cam a2205665Ii 4500
001 788528
008 161019s20122012at a 001 0 eng
020 _a113318765x
_q(paperback)
020 _a9781133187653
_q(paperback)
040 _aMEF
_beng
_erda
_MEF
041 0 _aeng
049 _aTR-IsMEF
050 0 0 _aHF1017
_b.W55 2012
100 1 _aWilliams, Thomas A.
_q(Thomas Arthur),
_d1944-,
_eauthor.
245 1 0 _aEssentials of contemporary business statistics /
_cThomas A. Williams, Rochester Institute of Technology, Dennis J. Sweeney, University of Cincinnati, David R. Anderson, University of Cincinnati.
250 _aFifth international edition.
264 1 _aAustralia ;
_aMason. Ohio :
_bSouth-Western / Cengage Learning,
_c2012.
264 4 _a©2012
300 _axxviii, 751 pages :
_bcolor illustrations ;
_c26 cm.
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
338 _avolume
_2rdacarrier
500 _aIncludes index.
520 _aFrom the renowned author team that has been writing market-leading business statistics textbooks for more than 20 years, ESSENTIALS OF CONTEMPORARY BUSINESS STATISTICS, 5E, International Edition provides a brief introduction to business statistics. The text balances a conceptual understanding of statistics with the real-world application of statistical methodology using problem-scenarios and real-world examples. Microsoft Excel(R) 2010 is integrated throughout the text, showing step-by-step instructions and screen captures to enhance learning.
650 0 _aCommercial statistics
630 0 0 _aMicrosoft Excel (Computer file)
650 0 _aCommercial statistics
_xComputer programs
700 1 _aSweeney, Dennis J.,
_eauthor.
700 1 _aAnderson, David R.
_q(David Ray),
_d1941-,
_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
_01
970 0 1 _aPreface,
_pxxi.
970 0 1 _aAbout the authors,
_pxxix.
970 1 2 _tChapter 1 Data and statistics,
_p1.
970 1 2 _tStatics in practice: bloomberg businessweek,
_p2.
970 1 2 _tApplications in business and economics,
_p3.
970 1 1 _tAccounting,
_p3.
970 1 1 _tFinance,
_p4.
970 1 1 _tMarketing,
_p4.
970 1 1 _tProduction,
_p4.
970 1 1 _tEconomics,
_p4.
970 1 2 _tData,
_p5.
970 1 1 _tElements, variables, and observations,
_p6.
970 1 1 _tScales of measurement,
_p6.
970 1 1 _tCategorical and quantative data,
_p7.
970 1 1 _tCross-sectional and time series data,
_p7.
970 1 2 _tData sources,
_p10.
970 1 1 _tExisting sources,
_p10.
970 1 1 _tStatistical studies,
_p11.
970 1 1 _tData acquisition errors,
_p13.
970 1 2 _tDescriptive statistics,
_p14.
970 1 2 _tStatistical inference,
_p15.
970 1 2 _tStatistical analysis using microsoft excel,
_p17.
970 1 1 _tData sets and excel worksheets,
_p18.
970 1 1 _tUsing excel for statistical analysis,
_p19.
970 1 2 _tData mining,
_p21.
970 1 2 _tEthical guidelines for statistical practice,
_p22.
970 0 1 _aSummary,
_p23.
970 0 1 _aGlossary,
_p24.
970 0 1 _aSupplementary exercises,
_p25.
970 0 1 _aAppendix an introduction to stattools,
_p32.
970 1 2 _tChapter 2 Descriptive statistics: tabular and graphical presentations,
_p35
970 1 2 _tStatistics in practice: colgate-palmolive company,
_p36.
970 1 2 _tSummarizing categorical data,
_37.
970 1 1 _tFrequency distribution,
_p37.
970 1 1 _tUsing excel's countif function to construct a frequency distribution,
_p38.
970 1 1 _tRelative frequency and percent frequency distributions,
_p38.
970 1 1 _tUsing excel to construct relative frequency and percent frequency distributions,
_p40.
970 1 1 _tBar charts and pie charts,
_p41.
970 1 1 _tUsing excel's chart tools to construct a bar chart and a pie chart,
_p42.
970 1 2 _tSummarizing quantitative data,
_p47.
970 1 1 _tFrequency distribution,
_p47.
970 1 1 _tUsing excel's pivottable report to construct a frequency distribution,
_p49.
970 1 1 _tRelative frequency and percent frequency distributions,
_p51.
970 1 1 _tDot plot,
_p51.
970 1 1 _tHistogram,
_p52.
970 1 1 _tUsing excel's chart tools to construct a histogram,
_p54.
970 1 1 _tCumulative distributions,
_p55.
970 1 1 _tOgive,
_p56.
970 1 2 _tExploratory data analysis: the stem and leaf display,
_p61.
970 1 2 _tCrosstabulations and scatter diagrams,
_p66.
970 1 1 _tCrosstabulation,
_p66.
970 1 1 _tUsing excel's pivottable report to construct a crosstabulation,
_p69.
970 1 1 _tSimpson's paradox,
_p72.
970 1 1 _tScatter diagram and trendline,
_p74.
970 1 1 _tUsing excel's chart tools to construct a scatter diagram and a trendline,
_p75.
970 0 1 _aSummary,
_p82.
970 0 1 _aGlossary,
_p83.
970 0 1 _aKey formulas,
_p84.
970 0 1 _aSupplementary exercises,
_p84.
970 0 1 _aCase problem 1 pelican stores,
_p90.
970 0 1 _aCase problem 2 motion picture industry,
_p91.
970 0 1 _aAppendix 2.1 using excel's pivotchart report to summarize categorical data,
_p92.
970 0 1 _aAppendix 2.2 using excel's pivotchart report to summarize quantative data,
_p94.
970 0 1 _aAppendix 2.3 using statttols for tabular and graphical presentations,
_p95.
970 1 2 _tChapter 3 Descriptive statistics: numerical measures,
_p97.
970 1 2 _tStatistics in practice: small fry design,
_p98.
970 1 2 _tMeasures of location,
_p99.
970 1 1 _tMean,
_p99.
970 1 1 _tMedian,
_p100.
970 1 1 _tMode,
_p101.
970 1 1 _tUsing excel to compute the mean, median and mode,
_p102.
970 1 1 _tPercentiles,
_p103.
970 1 1 _tQuartiles,
_p104.
970 1 1 _tUsing excel to compute percentiles and quartiles,
_p105.
970 1 2 _tMeasures of variability,
_p111.
970 1 1 _tRange,
_p111.
970 1 1 _tInterquartile range,
_p112.
970 1 1 _tVariance,
_p112.
970 1 1 _tStandard deviation,
_p114.
970 1 1 _tUsing excel to compute the sample variance and sample standard deviation,
_p115.
970 1 1 _tCoefficient of variation,
_p116.
970 1 1 _tUsing excel's descriptive statistics tool,
_p116.
970 1 2 _tMeasures of distribution shape, relative location and detecting outliers,
_p120.
970 1 1 _tDistribution shape,
_p120.
970 1 1 _tZ-scores,
_p121.
970 1 1 _tChebyshev's theorem,
_p122.
970 1 1 _tEmprical rule,
_p123.
970 1 1 _tDetecting outliers,
_p124.
970 1 2 _tExploratory data analysis,
_p128.
970 1 1 _tFive-number summary,
_p128.
970 1 1 _tBox plot,
_p128.
970 1 1 _tComparative analysis using box plots,
_p129.
970 1 2 _tMeasures of association between two variables,
_p133.
970 1 1 _tCovariance,
_p134.
970 1 1 _tInterpretation of the covariance,
_p135.
970 1 1 _tCorrelation coefficient,
_p138.
970 1 1 _tInterpretation of the correlation coefficient,
_p139.
970 1 1 _tUsing excel to compute the covariance and correlation coefficient,
_p140.
970 1 2 _tThe weighted mean and working with grouped data,
_p144.
970 1 1 _tWeighted mean,
_p144.
970 1 1 _tGrouped data,
_p145.
970 0 1 _aSummary,
_p149.
970 0 1 _aGlossary,
_p150.
970 0 1 _aKey formulas,
_p151.
970 0 1 _aSupplementary exercises,
_p153.
970 0 1 _aCase problem 1 pelican stores,
_p157.
970 0 1 _aCase problem 2 motion picture stores,
_p157.
970 0 1 _aAppendix descriptive statistics using stattools,
_p160.
970 1 2 _tChapter 4 Introduction to probability,
_p163.
970 1 2 _tStatistics in practice: oceanwide seafood,
_p164.
970 1 2 _tExperiments, counting rules, and assigning probabilities,
_p165.
970 1 1 _tCounting rules, combinations and permutations,
_p166.
970 1 1 _tAssigning probabilities,
_p170.
970 1 1 _tProbabilities for the kp&l project,
_p172.
970 1 2 _tEvents and their probabilities,
_p175.
970 1 2 _tSome basic relationships of probability,
_p179.
970 1 1 _tComplement of an event,
_p179.
970 1 1 _tAddition law,
_p180.
970 1 2 _tConditional probability,
_p186.
970 1 1 _tIndependent events,
_p189.
970 1 1 _tMultiplication law,
_p189.
970 1 2 _tBayes' theorem,
_p194.
970 1 1 _tTabular approach,
_p197.
970 1 1 _tUsing excel to compute posterior probabilities,
_p198.
970 0 1 _aSummary,
_p200.
970 0 1 _aGlossary,
_p200.
970 0 1 _aKey formulas,
_p201.
970 0 1 _aSupplementary exercises,
_p202.
970 0 1 _aCase problem hamilton county judges,
_p206.
970 1 2 _tChapter 5 Discrete probability distributions,
_p209.
970 1 2 _tStatistics in practice: citibank,
_p210.
970 1 2 _tRandom variables,
_p210.
970 1 1 _tDiscrete random variables,
_p211.
970 1 1 _tContinuous random variables,
_p212.
970 1 2 _tDiscrete probability distributions,
_p213.
970 1 2 _tExpected value and variance,
_p219.
970 1 1 _tExpected value,
_p219.
970 1 1 _tVariance,
_p219.
970 1 1 _tUsing excel to compute the expected value, variance, and standard deviation,
_p220.
970 1 2 _tBinominal probability distribution,
_p225.
970 1 1 _tA binominal experiment,
_p225.
970 1 1 _tMartin clothing store problem,
_p226.
970 1 1 _tExpected value and variance for the binominal distribution,
_p233.
970 1 2 _tPoisson probability distribution,
_p236.
970 1 1 _tAn example involving time intervals,
_p236.
970 1 1 _tAn example involving length or distance intervals,
_p237.
970 1 1 _tUsing excel to compute poisson probabilities,
_p238.
970 1 2 _tHypergeometric probability distribution,
_p241.
970 1 1 _tUsing excel to compute hypergeometric probabilities,
_p243.
970 0 1 _aSummary,
_p245.
970 0 1 _aGlossary,
_p246.
970 0 1 _aKey formulas,
_p247.
970 0 1 _aSupplementary exercises,
_p248.
970 1 2 _tChapter 6 Continous probability distributions,
_p251.
970 1 2 _tStatistics in practice: procter & gamble,
_p252.
970 1 2 _tUniform probability distribution,
_p253.
970 1 1 _tArea as a measure of probability,
_p254.
970 1 2 _tNormal probability distribution,
_p257.
970 1 1 _tNormal curve,
_p277.
970 1 1 _tStandard normal probability distribution,
_p260.
970 1 1 _tComputing probabilities for any normal probability distribution,
_p264.
970 1 1 _tGrear tire company problem,
_p265.
970 1 1 _tUsing excel to compute normal probabilities,
_p266.
970 1 2 _tExpontial probability distribution,
_p272.
970 1 1 _tComputing probabilities for the exponential distribution,
_p273.
970 1 1 _tRelationship between the poisson and exponential distributions,
_p274.
970 1 1 _tUsing excel to compute exponential probabilities,
_p275.
970 0 1 _aSummary,
_p277.
970 0 1 _aGlossary,
_p277.
970 0 1 _aKey formulas,
_p278.
970 0 1 _aSupplementary exercises,
_p278.
970 0 1 _aCase problem speciality toys,
_p281.
970 1 2 _tChapter 7 Sampling and sampling distributions,
_p283.
970 1 2 _tStatistics in practice: meadwestvaco corporation,
_p284
970 1 2 _tThe electronics associates sampling problem,
_p285.
970 1 2 _tSelecting a sample,
_p286.
970 1 1 _tSampling from a finite population,
_p286.
970 1 1 _tSampling from an infinite population,
_p289.
970 1 2 _tPoint estimation,
_p293.
970 1 1 _tPractical advice,
_p295.
970 1 2 _tIntroduction to sampling distributions,
_p297.
970 1 2 _tSampling distribution of x,
_p300.
970 1 1 _tExpected value of x,
_p300.
970 1 1 _tStandard deviation of x,
_p301.
970 1 1 _tForm of the sampling distribution of x,
_302.
970 1 1 _tSampling distribution of x for the eai problem,
_p304.
970 1 1 _tPractical value of the sampling distribution of x,
_p304.
970 1 1 _tRelationship between the sample size and sampling distribution of x,
_p306.
970 1 2 _tSampling distribution of p,
_p310.
970 1 1 _tExpected value of p,
_p310.
970 1 1 _tStandard deviation of p,
_p311.
970 1 1 _tForm of the sampling distribution of p,
_p312.
970 1 1 _tPractical value of the sampling distribution of p,
_p313.
970 1 2 _tOther sampling methods,
_p316.
970 1 1 _tStratified random sampling,
_p316.
970 1 1 _tCluster sampling,
_p316.
970 1 1 _tSystematic sampling,
_p317.
970 1 1 _tConvenience sampling,
_p317.
970 1 1 _tJudgement sampling,
_p318.
970 0 1 _aSummary,
_p318.
970 0 1 _aGlossary,
_p319.
970 0 1 _aKey formulas,
_p320.
970 0 1 _aSupplementary exercises,
_p320.
970 0 1 _aAppendix random sampling with stattools,
_p323.
970 1 2 _tChapter 8 Interval estimation,
_p324.
970 1 2 _tStatistics in practice: food lion,
_p325.
970 1 2 _tPopulation mean: q known,
_p326.
970 1 1 _tMargin of error and the interval estimate,
_p326.
970 1 1 _tUsing excel,
_p338.
970 1 1 _tPractical advice,
_p339.
970 1 1 _tUsing a small sample,
_p339.
970 1 1 _tSummary of interval estimation procedures,
_p341.
970 1 2 _tDetermining the sample size,
_p344.
970 1 2 _tPopulation proportion,
_p347.
970 1 1 _tUsing excel,
_p349.
970 1 1 _tDetermining the sample size,
_p350.
970 0 1 _aSummary,
_p354.
970 0 1 _aGlossary,
_p355.
970 0 1 _aKey formulas,
_p356.
970 0 1 _aSupplementary exercises,
_p356.
970 0 1 _aCase problem 1 young professional magazine,
_p360.
970 0 1 _aCase problem 2 gulf real estate properties,
_p361.
970 0 1 _aCase problem 3 metropolitan research, inc.,
_p362.
970 0 1 _aAppendix interval estimation with stattools,
_p363.
970 1 2 _tChapter 9 Hypothesis tests,
_p365.
970 1 2 _tStatistics in practice: john morrell & company,
_p366.
970 1 2 _tDeveloping null and alternative hypotheses,
_p367.
970 1 1 _tThe alternative hypothesis as a research hypothesis,
_p367.
970 1 1 _tThe null hypothesis as an assumption to be challenged,
_p368.
970 1 1 _tSummary of forms for null and alternative hypotheses,
_p369.
970 1 2 _tType i and type ii errors,
_p371.
970 1 2 _tPopulation mean q known,
_p373.
970 1 1 _tOne-tailed test,
_p373.
970 1 1 _tTwo-tailed,
_p379.
970 1 1 _tUsing excel,
_p382.
970 1 1 _tSummary and practical advice,
_p384.
970 1 1 _tRelationship between interval estimation and hypothesis testing,
_p385.
970 1 2 _tPopulation mean q unknown,
_p390.
970 1 1 _tOne tailed test,
_p391.
970 1 1 _tTwo-tailed test,
_p392.
970 1 1 _tUsing excel,
_p393.
970 1 1 _tSummary and practical advice,
_p396.
970 1 2 _tPopulation proportion,
_p399.
970 1 1 _tUsing excel,
_p401.
970 1 1 _tSummary,
_p403.
970 0 1 _aSummary,
_p406.
970 0 1 _aGlossary,
_p406.
970 0 1 _aKey formulas,
_p407.
970 0 1 _aSupplementary exercises,
_p407.
970 0 1 _aCase problem 1 quality associates inc,
_p410.
970 0 1 _aCase problem 2 ethical behavior of business students at bayview university,
_p411.
970 0 1 _aAppendix hypothesis testing with stattools,
_p413.
970 1 2 _tChapter 10 Comparsions involving means, experimental design, and analysis of variance,
_p414.
970 1 2 _tStatistics in practice: u.s. food and drug administration,
_p415.
970 1 2 _tInferences about the difference between two population means σ1 and σ2 known,
_p416.
970 1 1 _tInterval estimation of μ1-µ2,
_p416.
970 1 1 _tUsing excel to construct a confidence interval,
_p418.
970 1 1 _tHypothesis tests about µ1-µ2,
_p421.
970 1 1 _tUsing excel to conduct a hypothesis test,
_p422.
970 1 1 _Practical advice,
_p423.
970 1 2 _tInferences about the differences between two population means σ1 and σ2 unknown,
_p427.
970 1 1 _tInterval estimation of µ1 - µ2,
_p427.
970 1 1 _tUsing excel to construct a confidence interval,
_p428.
970 1 1 _tHypothesis tests about µ1 - µ2,
_p431.
970 1 1 _tUsing excel to conduct a hypothesis test,
_p433.
970 1 1 _tPractical advice,
_p434.
970 1 2 _tInferences about the difference between two population means: matched samples,
_p438.
970 1 1 _tUsing excel to conduct a hypothesis test,
_p441.
970 1 2 _tAn introduction to experimental design and analysis of variance,
_p446.
970 1 1 _tData collection,
_p447.
970 1 1 _tAssumptions for analysis of variance,
_p448.
970 1 1 _tAnalysis of variance: a conceptual overview,
_p448.
970 1 2 _tAnalysis of variance and the completely randomized design,
_p451.
970 1 1 _tBetween-treatments estimate of population variance,
_p452.
970 1 1 _tWithin-treatments estimate of population variance,
_p453.
970 1 1 _tComparing the variance estimates the f test,
_p453.
970 1 1 _tAnova table,
_p455.
970 1 1 _tUsing excel,
_p456.
970 1 1 _tTesting for the equality of k population means: an observational study,
_p458.
970 0 1 _aSummary,
_p462.
970 0 1 _aGlossary,
_p463.
970 0 1 _aKey formulas,
_p463.
970 0 1 _aSupplementary exercises,
_p465.
970 0 1 _aCase problem 1 par inc,
_p469.
970 0 1 _aCase problem 2 wentworth medical center,
_p470.
970 0 1 _aAppendix comparisons involving means using stattools,
_p471.
970 1 2 _tChapter 11 Comparisons involving proportions and a test of independence,
_p474.
970 1 2 _tStatistics in practice: united way,
_p475.
970 1 2 _tInferences about the difference between two population proportions,
_p476.
970 1 1 _tInterval estimation of p1 - p2,
_p476.
970 1 1 _tUsing excel to construct a confidence interval,
_p477.
970 1 1 _tHypothesis tests about p1- p2,
_p479.
970 1 1 _tUsing excel to conduct a hypothesis test,
_p481.
970 1 2 _tHypothesis test for proportions of a multinominal population,
_p486.
970 1 1 _tUsing excel to conduct a goodness of fit test,
_p490.
970 1 2 _tTest of independence,
_p493.
970 1 1 _tUsing excel to conduct a test of independence,
_p497.
970 0 1 _aSummary,
_p501.
970 0 1 _aGlossary,
_p502.
970 0 1 _aKey formulas,
_p502.
970 0 1 _aSupplementary execises,
_p503.
970 0 1 _aCase problem a bipartisan agenda for change,
_p507.
970 0 1 _aAppendix test of independence using stattools,
_p508.
970 1 2 _tChapter 12 Simple linear regression,
_p509.
970 1 2 _tStatistics in practice: alliance data systems,
_p510.
970 1 2 _tSimple linear regression equation,
_p511.
970 1 1 _tRegression model and regression equation,
_p511.
970 1 1 _tEstimated regression equation,
_p512.
970 1 2 _tLeast squares method,
_p514.
970 1 1 _tUsing excel's chart tools to construct a scatter a diagram and compute the estimated regression equation,
_p518.
970 1 2 _tCoefficient of determination,
_p526.
970 1 1 _tUsing excel to compute the coefficient of determination,
_p530.
970 1 1 _tCorrelation coefficient,
_p530.
970 1 2 _tModel assumptions,
_p534.
970 1 2 _tTesting for significance,
_p536.
970 1 1 _tEstimate of σ,
_p536.
970 1 1 _tT test,
_p537.
970 1 1 _tConfidence interval for β1,
_p539.
970 1 1 _tF test,
_p540.
970 1 1 _tSome cautions about the interpretation of significance tests,
_p541.
970 1 2 _tUsing the estimated regression equation for estimation and prediction,
_p545.
970 1 1 _tInterval estimation,
_p546.
970 1 1 _tConfidence interval for the mean value of y,
_p546.
970 1 1 _tPrediction interval for an individual value of y,
_p548.
970 1 2 _tExcel's regression tool,
_p552.
970 1 1 _tUsing excel's regression tool for the armand's pizza parlors example,
_p553.
970 1 1 _tInterpretation of estimated regression equation outout,
_p554.
970 1 1 _tInterpretation of anova output,
_p555.
970 1 1 _tInterpretation of regression statistics output,
_p556.
970 1 1 _tUsing stattools to compute prediction intervals,
_p556.
970 1 2 _tResidual analysis: validating model assumptions,
_p560.
970 1 1 _tResidual plot against x,
_p561.
970 1 1 _tResidual plot against ŷ,
_p564.
970 1 1 _tUsing excel to construct a residual plot,
_p564.
970 0 1 _aSummary,
_p567.
970 0 1 _aGlossary,
_p68.
970 0 1 _aKey formulas,
_p569.
970 0 1 _aSupplementary exercises,
_p570.
970 0 1 _aCase problem 1 measuring stock market risk,
_p577.
970 0 1 _aCase problem 2 u.s. department of transportation,
_p578.
970 0 1 _aCase problem 3 alumni giving,
_p578.
970 0 1 _aCase problem 4 pga tour statistics,
_p580.
970 0 1 _aAppendix regression analysis using stattools,
_p581.
970 1 2 _tChapter 13 Multiple regression,
_p583.
970 1 2 _tStatistics in practice: international paper,
_p584.
970 1 2 _tMultiple regression model,
_p585.
970 1 1 _tRegression model and regression equation,
_p585.
970 1 1 _tEstimated multiple regression equation,
_p585.
970 1 2 _tLeast squares method,
_p586.
970 1 1 _tAn example: butler trucking company,
_p587.
970 1 1 _tUsing excel's regression tool to develop the estimated multiple regression equation,
_p590.
970 1 1 _tNote on interpretation of coefficients,
_p592.
970 1 2 _tMultiple coefficient of determination,
_p597.
970 1 2 _tModel assumptions,
_p600.
970 1 2 _Testing for significance,
_p601.
970 1 1 _tF test,
_p601
970 1 1 _tT test,
_p604.
970 1 1 _tMulticollinearity,
_p605.
970 1 2 _tUsing the estimated regression equation for estimation and prediction,
_p609.
970 1 2 _tCategorical independent variables,
_p611.
970 1 1 _tAn example: johnson filtration inc,
_p611.
970 1 1 _tInterpreting the parameters,
_p613.
970 1 1 _tMore complex categorical variables,
_p616.
970 1 2 _tModeling curvilinear relationships,
_p620.
970 0 1 _aSummary,
_p626.
970 0 1 _aGlossary,
_p627.
970 0 1 _aKey formulas,
_p627.
970 0 1 _aSupplementary exercises,
_p629.
970 0 1 _aCase problem 1 consumer research inc,
_p634.
970 0 1 _aCase problem 2 alumni giving,
_p635.
970 0 1 _aCase problem 3 pga tour statistics,
_p636.
970 0 1 _aCase problem 4 predicting winning percentage for the nfl,
_p638.
970 0 1 _aAppendix multiple regression analysis using stattools,
_p639.
970 1 2 _tChapter 14 Statistical methods for quality control,
_p640.
970 1 2 _tStatistics in practice: dow chemical company,
_p641.
970 1 2 _tPhilosophies and frameworks,
_p642.
970 1 1 _tMalcolm baldrige national quality award,
_p643.
970 1 1 _tIso 9000,
_p643.
970 1 1 _tSix sigma,
_p643.
970 1 2 _tStatistical process control,
_p646.
970 1 1 _tControl charts,
_p646.
970 1 1 _tX chart process mean and standard deviation known,
_p647.
970 1 1 _tX chart: process mean and standard deviation unknown,
_p650.
970 1 1 _tR chart,
_p652.
970 1 1 _tP chart,
_p654.
970 1 1 _tNp chart,
_p656.
970 1 1 _tInterpretation of control charts,
_p657.
970 1 2 _tAcceptance sampling,
_p660.
970 1 1 _tKali inc an example of acceptance sampling,
_p661.
970 1 1 _tComputing the probability of accepting a lot,
_p662.
970 1 1 _tSelecting an acceptance sampling plan,
_p665.
970 1 1 _tMultiple sampling plans,
_p667.
970 0 1 _aSummary,
_p668.
970 0 1 _aGlossary,
_p668.
970 0 1 _aKey formulas,
_p669.
970 0 1 _aSupplementary exercises,
_p670.
970 0 1 _aAppendix control charts using stattools,
_p672.
970 1 2 _tChapter 15 Time series analysis and forecasting on website.
970 1 2 _tStatistics in practice: nevada occupational health clinic 15-2.
970 1 2 _tTİme series patterns 15-3.
970 1 1 _tHorizontal pattern 15-3.
970 1 1 _tTrend pattern 15-5.
970 1 1 _tSeasonal pattern 15-5.
970 1 1 _tTrend and seasonal pattern 15-6.
970 1 1 _tCyclical pattern 15-7.
970 1 1 _tUsing excel's chart tools to construct a time series plot 15-6.
970 1 1 _tSelecting a forecasting method 15-9.
970 1 2 _tForecast accuracy 15-10.
970 1 2 _tMoving averages and exponential smoothing 15-15.
970 1 1 _tMoving averages 15-15.
970 1 1 _tUsing excel's moving average tool 15-16.
970 1 1 _tWeighted moving averages 15-19.
970 1 1 _tExponential smoothing 15-19.
970 1 1 _tUsing excel's exponential smoothing tool 15-21.
970 1 2 _tTrend projection 15-27.
970 1 1 _tLinear trend regression 15-27.
970 1 1 _tUsing excel's regression tool to compute a linear trend equation 15-31.
970 1 1 _tNonlinear trend regression 15-32.
970 1 1 _tUsing excel's chart tools for trend projection 15-34.
970 1 2 _tSeasonality and trend 15-40.
970 1 1 _tSeasonality without trend 15-40.
970 1 1 _tModels based on monthly data 15-45.
970 1 2 _tTime series decomposition 15-49.
970 1 1 _tCalculating the seasonal indexes 15-50.
970 1 1 _tDeseasonalizing the time series 15-54.
970 1 1 _tUsing the deseasonlized time series to identify trend 15-55.
970 1 1 _tSeasonal adjustments 15-56.
970 1 1 _tModels based on monthly data 15-56.
970 1 1 _tCyclical component 15-57.
970 0 1 _aSummary 15-60.
970 0 1 _aGlossary 15-61.
970 0 1 _aKey formulas 15-62.
970 0 1 _aSupplementary exercises 15-62.
970 0 1 _aCase problem 1 forecasting food and beverage sales 15-66.
970 0 1 _aCase problem 2 forecasting lost sales 15-67.
970 0 1 _aAppendix forecasting using stattools 15-69.
970 0 1 _aAppendix a references and bibliography,
_p675.
970 0 1 _aAppendix b tables,
_p676.
970 0 1 _aAppendix c summation notation,
_p687.
970 0 1 _aAppendix d self test solutions and answers to select exercises,
_p689.
970 0 1 _aAppendix e microsoft excel 2010 and tools for statistical analysis,
_p729.
970 0 1 _aIndex,
_p741.
999 _c878
_d878
003 KOHA