BASIC PRACTICE OF STATS-LL W/SAPLINGPLU8th EditionMoore Publisher: MAC HIGHERISBN: 9781319216245
BASIC PRACTICE OF STATS-LL W/SAPLINGPLU
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Chapter 0 - Getting StartedChapter 1 - Picturing Distribution With GraphsChapter 1.1 - Individuals And VariablesChapter 1.2 - Categorical Variables: Pie Charts And Bar GraphsChapter 1.3 - Quantitative Variables: HistogramsChapter 1.4 - Interpreting HistogramsChapter 1.5 - Quantitative Variables: StemplotsChapter 1.6 - Time PlotsChapter 2 - Describing Distributions With NumbersChapter 2.1 - Measuring Center: The Mean
Chapter 2.3 - Comparing The Mean And The MedianChapter 2.5 - The Five-number Summary And BoxplotsChapter 2.6 - Spotting Suspected Outliers And Modified BoxplotsChapter 2.8 - Choosing Measures Of Center And VariabilityChapter 2.10 - Organizing A Statistical ProblemChapter 3 - The Normal DistributionsChapter 3.1 - Density CurvesChapter 3.2 - Describing Density CurvesChapter 3.4 - The 68-95-99.7 RuleChapter 3.5 - The Standard Normal DistributionChapter 3.7 - Using The Standard Normal TableChapter 3.8 - Finding A Value Given A ProportionChapter 4 - Scatterplots And CorrelationChapter 4.1 - Explanatory And Respone VariablesChapter 4.2 - Displaying Relationships: ScatterplotsChapter 4.3 - Interpreting ScatterplotsChapter 4.4 - Adding Categorical Variables To ScatterplotsChapter 4.5 - Measuring Linear Association: CorrelationChapter 4.6 - Facts About CorrelationChapter 5 - RegressionChapter 5.1 - Regression LinesChapter 5.3 - Examples Of TechnologyChapter 5.4 - Facts About Least-squares RegressionChapter 5.5 - ResidualsChapter 5.6 - Influential ObservationsChapter 5.7 - Cautions About Correlation And RegressionChapter 5.8 - Association Does Not Imply CausationChapter 6 - Two-way TablesChapter 6.1 - Marginal DistributionsChapter 6.2 - Conditional DistributionsChapter 6.3 - Simpson's ParadoxChapter 7 - Exploring Data: Part I ReviewChapter 8 - Producing Data: SamplingChapter 8.1 - Population Versus SampleChapter 8.2 - How To Sample BadlyChapter 8.3 - Simple Random SamplesChapter 8.4 - Inference About The PopulationChapter 8.5 - Other Sampling DesignsChapter 8.6 - Cautions About Sample SurveysChapter 8.7 - The Impact Of TechnologyChapter 9 - Producing Data: ExperimentsChapter 9.1 - Observation Versus ExperimentChapter 9.2 - Subjects, Factors, And TreatmentsChapter 9.3 - How To Experiment BadlyChapter 9.4 - Randomized Comparative ExperimentsChapter 9.5 - The Logic Of Randomized Comparative ExperimentsChapter 9.6 - Cautions About ExperimentationChapter 9.7 - Matched Pairs And Other Block DesignsChapter 10 - Data Ethics*Chapter 10.1 - Institutional Review BoardsChapter 10.2 - Infored ConsentChapter 10.3 - ConfidentialityChapter 11 - Producing Data: Part Ii ReviewChapter 12 - Introducing ProbabilityChapter 12.2 - The Search For RandomnessChapter 12.3 - Probability ModelsChapter 12.4 - Probability RulesChapter 12.5 - Finite Probability ModelsChapter 12.6 - Continuous Probability ModelsChapter 12.7 - Random VariablesChapter 12.8 - Personal ProbabilityChapter 13 - General Rules Of ProbabilityChapter 13.1 - The General Addition RuleChapter 13.2 - Independence And The Multiplication RuleChapter 13.3 - Conditional ProbabilityChapter 13.4 - The General Multiplication RuleChapter 13.5 - Showing Events Are IndependentChapter 13.6 - Tree DiagramsChapter 13.7 - Bayes' RuleChapter 14 - Binomial DistributionsChapter 14.2 - Binomial Distributions In Statistical SamplingChapter 14.4 - Examples Of TechnologyChapter 14.5 - Binomial Mean And Standard DeviationChapter 14.6 - The Normal Approximation To Binomial DistributionsChapter 15 - Sampling DistributionsChapter 15.1 - Parameters And StatisticsChapter 15.2 - Statisitcal Estimation And The Law Of Large NumbersChapter 15.3 - Sampling DistributionsChapter 15.4 - The Sampling Distribution Of X-barChapter 15.5 - The Central Limit TheoremChapter 15.6 - Sampling Distributions And Statistical SignificanceChapter 16 - Confidence Intervals: The BasicsChapter 16.1 - The Reasoning Of Statistical EstimationChapter 16.2 - Margin Of Error And Confidence LevelChapter 16.3 - Confidence Lntervals For A Population MeanChapter 16.4 - How Confidence Intervals BehaveChapter 17 - Tests Of Significance: The BasicsChapter 17.1 - The Reasoning Of Tests Of SignificanceChapter 17.2 - Stating HypothesesChapter 17.3 - P-value And Statistical SignificanceChapter 17.4 - Tests For A Population MeanChapter 17.5 - Significance From A TableChapter 18 - Inference In PracticeChapter 18.1 - Conditions For Inference In PracticeChapter 18.2 - Cautions About Confidence LntervalsChapter 18.3 - Cautions About Significance TestsChapter 18.4 - Planning Studies: Sample Size For Confidence LntervalsChapter 18.5 - Planning Studies: The Power Of A Statistical Test Of SignificanceChapter 19 - From Data Production To Inference: Part Iii ReviewChapter 20 - Inference About A Population MeanChapter 20.1 - Conditions For Inference About A MeanChapter 20.2 - The T DistributionsChapter 20.3 - The One Sample T Confidence IntervalChapter 20.4 - The One-sample T TestChapter 20.6 - Matched Pairs T ProceduresChapter 20.7 - Robustness Of T ProceduresChapter 21 - Comparing Two MeansChapter 21.1 - Two-sample Problems P HatChapter 21.3 - Two-sample T ProceduresChapter 21.4 - Examples Of TechnologyChapter 21.5 - Robustness AgainChapter 21.6 - Details Of The T ApproximationChapter 22 - Inference About A Population ProportionChapter 22.1 - The Sample Proportion P HatChapter 22.2 - Large-sample Confidence Intervals For A ProportionChapter 22.3 - Choosing The Sample SizeChapter 22.4 - Significance Tests For A ProportionChapter 22.5 - Plus Four Confidence Intervals For A ProportionChapter 23 - Comparing Two ProportionsChapter 23.4 - Examples Of TechnologyChapter 23.5 - Significance Tests For Compariog ProportionsChapter 23.6 - Plus Four Confidence Lntervals For Comparing ProportionsChapter 24 - Inference About Variables: Part Iv ReviewChapter 25 - Two Categorical Variables: The Chi Square TestChapter 25.1 - Two-way TablesChapter 25.2 - The Problem Of Multiple ComparisonsChapter 25.3 - Expected Counts In Two-way TablesChapter 25.5 - Examples Of TechnolologyChapter 25.6 - The Chi-square DistributionsChapter 25.7 - Cell Counts Required For The Chi-square TestChapter 25.8 - Uses Of The Chi-square Test: Independence And HomogeneityChapter 25.9 - The Chi-square Test For Goodness Of FitChapter 26 - Inference For RegressionChapter 26.2 - Estimating The ParametersChapter 26.3 - Examples Of TechnologyChapter 26.4 - Testing The Hypothesis Of No Linear RelationshipChapter 26.5 - Testing Lack Of CorrelationChapter 26.6 - Confidence Intervals For The Regression SlopeChapter 26.7 - Inference About PredictionChapter 26.8 - Checking The Conditions For InferenceChapter 27 - One Way Analysis Of Variance: Comparing Several MeansChapter 27.2 - The Analysis Of Variance F TestChapter 27.3 - Examples Of TechnologyChapter 27.4 - The Idea Of Analysis Of VarianceChapter 27.5 - Conditions For AnovaChapter 27.6 - F Distributions And Degrees Of FreedomChapter 27.7 - Follow-up Analysis: Tukey Pairwise Multiple ComparisonsChapter 27.8 - Some Details Of AnovaChapter 28 - Nonparametric TestsChapter 28.1 - Comparing Two Samples: The Wilcoxon Rank Sum TestChapter 28.2 - The Normal Approximation For WChapter 28.3 - Examples Of TechnologyChapter 28.4 - What Hypotheses Does Wilcoxon Test?Chapter 28.5 - Dealing With Ties In Rank TestsChapter 28.6 - Matched Pairs: The Wilcoxon Signed Rank TestChapter 28.7 - The Normal Approximation For W+Chapter 28.8 - Dealing With Ties In The Signed Rank TestChapter 28.11 - The Kruskal-wallis Test StatisticChapter 29 - Multiple RegressionChapter 29.1 - Adding A Categorical Variables In RegressionChapter 29.2 - Estimating ParametersChapter 29.3 - Examples Of TechnoogyChapter 29.4 - Inference For Multiple RegressionChapter 29.6 - A Model With Two Regression LinesChapter 29.7 - The General Multiplication Linear Regression ModeChapter 29.8 - The Woes Of Regression CoefficientsChapter 29.9 - A Case Study For Multiple RegressionChapter 29.10 - Inference For Regression ParametersChapter 29.11 - Checking The Conditions For InferenceChapter 30 - Two-way Analysis Of VarianceChapter 30.1 - Beyond One-way AnovaChapter 30.2 - Two-way Anova: Conditions, Main Effects, And InteractionChapter 31 - Statistical Process ControlChapter 31.2 - Describing ProcessesChapter 31.4 - X Bar Charts For Process MonitoringChapter 31.5 - S Charts For Process MonitoringChapter 31.7 - Setting Up Contro ChartsChapter 31.8 - Comments On Statistical ControlChapter 31.9 - Don't Confuse Control With CapabilityChapter 31.11 - Control Limits For P ChartsChapter 32 - Resampling: Permutation Tests And The BootstrapChapter 32.1 - Randomization In Experiments As A Basis For InferenceChapter 32.3 - Generating Bootstrap Samples
Sample Solutions for this Textbook
We offer sample solutions for BASIC PRACTICE OF STATS-LL W/SAPLINGPLU homework problems. See examples below:
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