| Description |
1 online resource (xv, 587 pages) |
| Contents |
Front Cover; Contents; Preface; Acknowledgments; Authors; Chapter 1: The Frontier Between Knowledge and Ignorance; Chapter 2: Describing Distributions with Statistics: Middle, Spread, and Skewness; Chapter 3: Exploring Data Visually; Chapter 4: Relative Location and Normal Distributions; Chapter 5: Bivariate Correlation; Chapter 6: Probability and Risk; Chapter 7: Sampling Distributions and Estimation; Chapter 8: Hypothesis Testing and Interval Estimation; Chapter 9: Types of Errors and Power; Chapter 10: One-Sample Tests and Estimates; Chapter 11: Two-Sample Tests and Estimates |
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Chapter 12: Tests and Estimates for Two or More SamplesChapter 13: Tests and Estimates for Bivariate Linear Relationships; Chapter 14: Analysis of Frequencies and Ranks; Chapter 15: Choosing an Analysis Plan; Suggested Answers to Odd-Numbered Exercises; Appendix; Appendix; Appendix; Appendix; Back Cover |
| Summary |
880-01 The Frontier between Knowledge and Ignorance IntroductionThe Context for Statistics: Science and ResearchDefinition of StatisticsThe Big Picture: Populations, Samples, and VariablesGeneralizing from the Sample to the PopulationExperimental ResearchBlinding and Randomized Block DesignNonexperimental ResearchQuasi-Experimental ResearchInferences and Kinds of ValidityDescribing Distributions with Statistics: Middle, Spread, and Skewness IntroductionMeasures of LocationMeasures of Spread or VariabilityMeasure of Skewness or Departure from SymmetryExploring Data Visually IntroductionWhy Graph Our Data? Pie Charts and Bar GraphsTwo Kinds of Dot PlotsScatterplotsHistogramsTime Plots (Line Graphs) BoxplotsGraphs Can Be MisleadingBeyond These GraphsRelative Location and Normal Distributions IntroductionStandardizing ScoresComputing a z Score in a SampleComputing a z Score in a PopulationComparing z Scores for Different VariablesA Different Kind of Standard ScoreDistributions and ProportionsAreas under the Standard Normal CurveBivariate Correlation IntroductionPearson's Correlation CoefficientVerbal Definition of Pearson's rJudging the Strength of a CorrelationWhat Most Introductory Statistics Texts Say about CorrelationPearson's r Measures Linear Relationships OnlyCorrelations Can Be Influenced by OutliersCorrelations and Restriction of RangeCombining Groups of Scores Can Affect CorrelationsMissing Data Are Omitted from CorrelationsPearson's r Does Not Specify Which Variable Is the PredictorProbability and RiskIntroductionRelative Frequency of OccurrenceConditional ProbabilitySpecial Names for Certain Conditional ProbabilitiesStatistics Often Accompanying Sensitivity and SpecificityTwo Other Probabilities: "And" and "Or"Risk and Relative RiskOther Statistics Associated with ProbabilitySampling Distributions and Estimation IntroductionQuantifying |
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880-01/(S Variability from Sample to SampleKinds of DistributionsWhy We Need Sampling DistributionsComparing Three Distributions: What We Know So FarCentral Limit TheoremUnbiased EstimatorsStandardizing the Sample MeanInterval EstimationCalculating a Confidence Interval Estimate of μHypothesis Testing and Interval Estimation IntroductionTestable GuessesThe Rat Shipment StoryOverview of Hypothesis TestingTwo Competing Statements about What May Be TrueWriting Statistical HypothesesDirectional and Nondirectional Alternative HypothesesChoosing a Small Probability as a StandardCompute the Test Statistic and a Certain ProbabilityDecision Rules When H1 Predicts a DirectionDecision Rules When H1 Is NondirectionalAssumptionsTesting Hypotheses with Confidence Intervals: Nondirectional H1Testing Hypotheses with Confidence Intervals: Directional H1Types of Errors and Power IntroductionPossible Errors in Hypothesis TestingProbability of a Type I ErrorProbability of Correctly Retaining the Null HypothesisType I Errors and Confidence IntervalsProbability of a Type II Error and PowerFactors Influencing Power: Effect SizeFactors Influencing Power: Sample SizeFactors Influencing Power: Directional Alternative HypothesesFactors Influencing Power: Significance LevelFactors Influencing Power: VariabilityFactors Influencing Power: Relation to Confidence IntervalsOne-Sample Tests and Estimates IntroductionOne-Sample t TestDistribution for Critical Values and p ValuesCritical Values for the One-Sample t TestCompleting the Sleep Quality ExampleAssumptionsConfidence Interval for μ Using One-Sample t Critical ValueGraphing Confidence Intervals and Sample MeansTwo-Sample Tests and Estimates IntroductionPairs of Scores and the Paired t TestTwo Other Ways of Getting Pairs of ScoresFun Fact Associated with Paired MeansPaired t Hypotheses When Direction Is Not PredictedPaired t Hypotheses |
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When Direction Is PredictedFormula for the Paired t TestConfidence Interval for the Difference in Paired MeansComparing Means of Two Independent GroupsIndependent t Hypotheses When Direction Is Not PredictedIndependent t Hypotheses When Direction Is PredictedFormula for the Independent-Samples t TestAssumptionsConfidence Intervals for a Difference in Independent MeansLimitations on Using the t Statistics in This ChapterTests and Estimates for Two or More Samples IntroductionGoing beyond the Independent-Samples t TestVariance between Groups and Within GroupsOne-Way ANOVA F Test: Logic and HypothesesComputing the One-Way ANOVA F TestCritical Values and Decision RulesNumeric Example of a One-Way ANOVA F TestTesting the Null HypothesisAssumptions and RobustnessHow to Tell Which Group Is BestMultiple Comparison Procedures and HypothesesMany Statistics Possible for Multiple ComparisonsConfidence Intervals in a One-Way ANOVA DesignTests and Estimates for Bivariate Linear Relationships IntroductionHypothesizing about a CorrelationTesting a Null Hypothesis about a CorrelationAssumptions of Pearson's rUsing a Straight Line for PredictionLinear Regression AnalysisDetermining the Best-Fitting LineHypothesis Testing in Bivariate RegressionConfidence Intervals in Simple RegressionLimitations on Using RegressionAnalysis of Frequencies and Ranks IntroductionOne-Sample ProportionConfidence Interval for a ProportionGoodness of Fit HypothesesGoodness of Fit StatisticComputing the Chi-Square Test for Goodness of FitGoodness of Fit: Assumptions and RobustnessChi-Square for IndependenceHypotheses for Chi-Square for IndependenceComputing Chi-Square for IndependenceRelative RiskOdds RatiosAnalysis of RanksChoosing an Analysis Plan IntroductionStatistics That We Have CoveredOrganizing Our List: Kind of Outcomes, Number of SamplesAdding to the Tree: Two SamplesAdding Again |
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To the Tree: More Than Two SamplesCompleting the Tree: Analysis of CategoriesCompleting the Tree: The Remaining Categorical AnalysesSuggested Answers to Odd-Numbered Exercises Appendix IndexReferences appear at the end of each chapter |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Medical statistics -- Textbooks
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Statistics.
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Health -- statistics & numerical data
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Statistics as Topic
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statistics.
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HEALTH & FITNESS -- Holism.
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HEALTH & FITNESS -- Reference.
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MEDICAL -- Alternative Medicine.
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MEDICAL -- Atlases.
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MEDICAL -- Essays.
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MEDICAL -- Family & General Practice.
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MEDICAL -- Holistic Medicine.
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MEDICAL -- Osteopathy.
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Statistics
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Medical statistics
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| Genre/Form |
Textbooks
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| Form |
Electronic book
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| Author |
Toothaker, Larry E., author.
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Beasley, William Howard, illustrator
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| ISBN |
9781466565340 |
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1466565349 |
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