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            Modern Business Statistics with Microso...
            6th Edition
            David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran
            Publisher: Cengage Learning
            ISBN: 9781337115186

            Solutions for Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)

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            Chapter 3.4 - Five-number Summaries And Box PlotsChapter 3.5 - Measures Of Association Between Two VariablesChapter 4 - Introduction To ProbabilityChapter 4.1 - Experiments, Counting Rules, And Assigning ProbabilitiesChapter 4.2 - Events And Their ProbabilitiesChapter 4.3 - Some Basic Relationships Of ProbabilityChapter 4.4 - Conditional ProbabilityChapter 4.5 - Bayes’ TheoremChapter 5 - Discrete Probability DistributionsChapter 5.1 - Random VariablesChapter 5.2 - Developing Discrete Probability DistributionsChapter 5.3 - Expected Value And VarianceChapter 5.4 - Bivariate Distributions, Covariance, And Financial PortfoliosChapter 5.5 - Binomial Probability DistributionChapter 5.6 - Poisson Probability DistributionChapter 5.7 - Hypergeometric Probability DistributionChapter 6 - Continuous Probability DistributionsChapter 6.1 - Uniform Probability DistributionChapter 6.2 - Normal Probability DistributionChapter 6.3 - Exponential Probability DistributionChapter 7 - Sampling And Sampling DistributionsChapter 7.2 - Selecting A SampleChapter 7.3 - Point EstimationChapter 7.5 - Sampling Distribution Of XbarChapter 7.6 - Sampling Distribution Of PbarChapter 7.8 - Practical Advice: Big Data And Errors In SamplingChapter 8 - Interval EstimationChapter 8.1 - Population Mean: S KnownChapter 8.2 - Population Mean: S KnownChapter 8.3 - Determining The Sample SizeChapter 8.4 - Population ProportionChapter 8.5 - Practical Advice: Big Data And Interval EstimationChapter 9 - Hypothesis TestsChapter 9.1 - Developing Null And Alternative HypothesesChapter 9.2 - Type I And Type Ii ErrorsChapter 9.3 - Population Mean: S KnownChapter 9.4 - Population Mean: S KnownChapter 9.5 - Population ProportionChapter 9.6 - Practical Advice: Big Data And Hypothesis TestingChapter 10 - Inference About Means And Proportions With Two PopulationsChapter 10.1 - Inferences About The Difference Between Two Population Means:s1 And S2 KnownChapter 10.2 - Inferences About The Difference Between Two Population Means:s1 And S2 UnknownChapter 10.3 - Inferences About The Difference Between Two Population Means: Matched SamplesChapter 10.4 - Inferences About The Difference Between Two Population ProportionsChapter 11 - Inferences About Population VariancesChapter 11.1 - Inferences About A Population VarianceChapter 11.2 - Inferences About Two Population VariancesChapter 12 - Tests Of Goodness Of Fit, Independence, And Multiple ProportionsChapter 12.1 - Goodness Of Fit TestChapter 12.2 - Test Of IndependenceChapter 12.3 - Testing For Equality Of Three Or More Population ProportionsChapter 13 - Experimental Design And Analysis Of VarianceChapter 13.2 - Analysis Of Variance And The Completely Randomized DesignChapter 13.3 - Multiple Comparison ProceduresChapter 13.4 - Randomized Block DesignChapter 13.5 - Factorial ExperimentChapter 14 - Simple Linear RegressionChapter 14.2 - Least Squares MethodChapter 14.3 - Coefficient Of DeterminationChapter 14.5 - Testing For SignificanceChapter 14.6 - Using The Estimated Regression Equation For Estimation And PredictionChapter 14.7 - Excel’s Regression ToolChapter 14.8 - Residual Analysis: Validating Model AssumptionsChapter 14.9 - Outliers And Influential ObservationsChapter 15 - Multiple RegressionChapter 15.2 - Least Squares MethodChapter 15.3 - Multiple Coefficient Of DeterminationChapter 15.5 - Testing For SignificanceChapter 15.6 - Using The Estimated Regression Equation For Estimation And PredictionChapter 15.7 - Categorical Independent VariablesChapter 15.8 - Residual AnalysisChapter 16 - Regression Analysis: Model BuildingChapter 16.1 - General Linear ModelChapter 16.2 - Determining When To Add Or Delete VariablesChapter 16.5 - Multiple Regression Approach To Experimental DesignChapter 16.6 - Autocorrelation And The Durbin-watson TestChapter 17 - Time Series Analysis And ForecastingChapter 17.2 - Forecast AccuracyChapter 17.3 - Moving Averages And Exponential SmoothingChapter 17.4 - Trend ProjectionChapter 17.5 - Seasonality And TrendChapter 17.6 - Time Series DecompositionChapter 18 - Nonparametric MethodsChapter 18.1 - Sign TestChapter 18.2 - Wilcoxon Signed-rank TestChapter 18.3 - Mann-whitney-wilcoxon TestChapter 18.4 - Kruskal-wallis TestChapter 18.5 - Rank CorrelationChapter 19 - Statistical Methods For Quality ControlChapter 19.2 - Statistical Process ControlChapter 19.3 - Acceptance SamplingChapter 20 - Decision Analysis (online)Chapter 20.2 - Decision Making With ProbabilitiesChapter 20.3 - Decision Analysis With Sample InformationChapter 20.4 - Computing Branch Probabilities Using Bayes’ TheoremChapter 21 - Sample Survey (on Online)Chapter 21.4 - Simple Random SamplingChapter 21.5 - Stratified Simple Random SamplingChapter 21.6 - Cluster Sampling

            Book Details

            Provide a balanced, conceptual understanding of statistics as MODERN BUSINESS STATISTICS, 6E focuses on real applications and Microsoft Excel 2016. This best-selling, comprehensive leader develops each statistical technique in an application setting with integrated Microsoft Excel 2016 instruction. Content focuses on statistical methodology as each presentation of a statistical procedure is followed by a discussion of how to use Excel to perform the procedure. Step-by-step instructions and screen captures ensure understanding. Business examples and application exercises demonstrate how statistical results provide insights into business decisions and problems. High-quality problems, noted for unwavering accuracy, and a signature problem-scenario approach apply statistical methods to business situations. New case problems and self-tests check reader understanding, while comprehensive support with MindTap and CengageNOW reinforce an understanding of business statistics.

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