Introduction to statistics for biology - McCleery, Robin H.; Watt, T. A. (Trudy A.); Hart, Tom.; Watt, T. A. (Trudy A.). Author (Boca Raton : Chapman & Hall/CRC, 2007)
PREFACEHOW LONG IS A WORM?IntroductionSampling a PopulationThe Normal Distribution ProbabilityContinuous Measurements-Worms AgainExpressing VariabilityCONFIDENCE INTERVALSThe Importance of Confidence IntervalsCalculating Confidence Intervals Another Way of Looking At ItYour First Statistical TestOne- and Two-Tailed Tests The Other Side of the Coin-Type II ErrorsRecap-Hypothesis Testing A ComplicationTesting Fish with tMinitab Does a One-Sample t-Test95% CI for WormsAnatomy of Test StatisticsCOMPARING THINGS: TWO SAMPLE TESTSA Simple CaseMatched-Pairs t-TestAnother Example-Testing Twin SheepIndependent Samples: Comparing Two PopulationsCalculation of Independent Samples t-TestOne- and Two-Tailed Tests-A ReminderMinitab Carries Out a Two-Sample t-TestPooling the Variances?PLANNING AN EXPERIMENTPrinciples of SamplingPrinciples of Experimental DesignRecording Data and Simulating an ExperimentSimulating Your ExperimentPARTITIONING VARIATION AND CONSTRUCTING A MODEL It's Simple... But Not That SimpleThe Example: Field Margins in ConservationThe Idea of a Statistical Model Laying Out the ExperimentSources of Variation: Random VariationThe ModelANALYZING YOUR RESULTS: IS THERE ANYTHING THERE? Is Spider Abundance Affected by Treatment?Why Not Use Multiple t-Tests? ANOVA for a Wholly Randomized DesignComparing the Sources of VariationThe Two Extremes of Explanation: All or NothingThe ANOVA TableTesting Our HypothesisIncluding Blocks: Randomized Complete Block Designs Analyzing the Spider Data Set in MinitabThe Assumptions behind ANOVA and How to Test ThemAnother Use for the F-Test: Testing Homogeneity of VarianceINTERPRETING YOUR ANALYSIS: FROM HYPOTHESIS TESTING TO BIOLOGICAL MEANING Treatment Means and Confidence IntervalsDifference between Two Treatment Means Getting More Out of an Experiment: Factorial Designs and InteractionsGetting More Out of the Analysis: Using the Factorial Design to Ask More Relevant QuestionsInteractionsAdding Blocking to the Factorial AnalysisHow to Interpret Interaction Plots: The Plant Hormone Experiment Loss of Data and Unbalanced ExperimentsLimitations of ANOVA and the General Linear Model (GLM)RELATING ONE VARIABLE TO ANOTHER CorrelationCalculating the Correlation Coefficient, and a New Idea: CovarianceRegressionLinear RegressionThe ModelInterpreting Hypothesis Tests in RegressionA Further Example of Linear RegressionAssumptions The Importance of Plotting ObservationsConfidence IntervalsStandard Error of Prediction (Prediction Interval)Caution in the Interpretation of Regression and CorrelationCATEGORICAL DATAThe Chi-Squared Goodness-of-Fit TestA More Interesting Example: Testing Genetic Models Contingency Analysis: Chi-Squared Test of Proportions A Further Example of a Chi-Squared Contingency TestBeyond Two-Dimensional Tables: The Likelihood Ratio Chi-SquareNONPARAMETRIC TESTSIntroductionBasic IdeasA Taxonomy of Tests Single-Sample TestsMatched-Pairs Tests Independent SamplesTwo Quantitative Variables: Spearman's Rank CorrelationWhy Bother with Parametric Tests?MANAGING YOUR PROJECTChoosing a Topic and a Supervisor Common Mistakes General Principles of Experimental Design and ExecutionAnalyzing Your Data and Writing the Report StructureThe First Draft Illustrating Results What It Is All About: Getting Through Your ProjectAPPENDIX A: AN INTRODUCTION TO MINITABConventions Used in This Book Starting Up HelpData Entry Looking at the Worms DataUpdating GraphsStacking and Unstacking-A Useful TrickLooking Up ProbabilitiesReport Writer The Minitab Command LineSaving Your SessionAPPENDIX B: STATISTICAL POWER AND SAMPLE SIZEAPPENDIX C: STATISTICAL TABLES APPENDIX D: REFERENCES AND FURTHER READINGAPPENDIX E: STATISTICAL TESTSINDEX
Rev. ed. of: Introductory statistics for biology students / Trudy A. Watt. 1997.
Includes bibliographical references (p. 265-266) and index.
Keywords: Biometry