Introductory statistics for the health sciences. (2015)

• Record Type: Book
• Further Information: Note: Lise DeShea and Larry E. Toothaker.
• Authors: DeShea, Lise; Toothaker, Larry E
• Contents: The Frontier between Knowledge and Ignorance; Introduction; The Context for Statistics: Science and Research; Definition of Statistics; The Big Picture: Populations, Samples, and Variables; Generalizing from the Sample to the Population; Experimental Research; Blinding and Randomized Block Design; Nonexperimental Research; Quasi-Experimental Research; Inferences and Kinds of Validity Describing Distributions with Statistics: Middle, Spread, and Skewness; Introduction; Measures of Location; Measures of Spread or Variability; Measure of Skewness or Departure from Symmetry Exploring Data Visually; Introduction; Why Graph Our Data?; Pie Charts and Bar Graphs; Two Kinds of Dot Plots; Scatterplots; Histograms; Time Plots (Line Graphs); Boxplots; Graphs Can Be Misleading; Beyond These Graphs Relative Location and Normal Distributions; Introduction; Standardizing Scores; Computing a z Score in a Sample; Computing a z Score in a Population; Comparing z Scores for Different Variables; A Different Kind of Standard Score; Distributions and Proportions; Areas under the Standard Normal Curve Bivariate Correlation; Introduction; Pearson’s Correlation Coefficient; Verbal Definition of Pearson’s r ; Judging the Strength of a Correlation; What Most Introductory Statistics Texts Say about Correlation; Pearson’s r Measures Linear Relationships Only; Correlations Can Be Influenced by Outliers; Correlations and Restriction of Range; Combining Groups of Scores Can Affect Correlations; Missing Data Are Omitted from Correlations; Pearson’s r Does Not Specify Which Variable Is the Predictor Probability and Risk; Introduction; Relative Frequency of Occurrence; Conditional Probability; Special Names for Certain Conditional Probabilities; Statistics Often Accompanying Sensitivity and Specificity; Two Other Probabilities: "And" and "Or"; Risk and Relative Risk; Other Statistics Associated with Probability Sampling Distributions and Estimation; Introduction; Quantifying Variability from Sample to Sample; Kinds of Distributions; Why We Need Sampling Distributions; Comparing Three Distributions: What We Know So Far; Central Limit Theorem; Unbiased Estimators; Standardizing the Sample Mean; Interval Estimation; Calculating a Confidence Interval Estimate of μ Hypothesis Testing and Interval Estimation; Introduction; Testable Guesses; The Rat Shipment Story; Overview of Hypothesis Testing; Two Competing Statements about What May Be True; Writing Statistical Hypotheses; Directional and Nondirectional Alternative Hypotheses; Choosing a Small Probability as a Standard; Compute the Test Statistic and a Certain Probability; Decision Rules When H 1 Predicts a Direction; Decision Rules When H 1 Is Nondirectional; Assumptions; Testing Hypotheses with Confidence Intervals: Nondirectional H 1 ; Testing Hypotheses with Confidence Intervals: Directional H 1 Types of Errors and Power; Introduction; Possible Errors in Hypothesis Testing; Probability of a Type I Error; Probability of Correctly Retaining the Null Hypothesis; Type I Errors and Confidence Intervals; Probability of a Type II Error and Power; Factors Influencing Power: Effect Size; Factors Influencing Power: Sample Size; Factors Influencing Power: Directional Alternative Hypotheses; Factors Influencing Power: Significance Level; Factors Influencing Power: Variability; Factors Influencing Power: Relation to Confidence Intervals One-Sample Tests and Estimates; Introduction; One-Sample t Test; Distribution for Critical Values and p Values; Critical Values for the One-Sample t Test; Completing the Sleep Quality Example; Assumptions; Confidence Interval for μ Using One-Sample t Critical Value; Graphing Confidence Intervals and Sample Means Two-Sample Tests and Estimates; Introduction; Pairs of Scores and the Paired t Test; Two Other Ways of Getting Pairs of Scores; Fun Fact Associated with Paired Means; Paired t Hypotheses When Direction Is Not Predicted; Paired t Hypotheses When Direction Is Predicted; Formula for the Paired t Test; Confidence Interval for the Difference in Paired Means; Comparing Means of Two Independent Groups; Independent t Hypotheses When Direction Is Not Predicted; Independent t Hypotheses When Direction Is Predicted; Formula for the Independent-Samples t Test; Assumptions; Confidence Intervals for a Difference in Independent Means; Limitations on Using the t Statistics in This Chapter Tests and Estimates for Two or More Samples; Introduction; Going beyond the Independent-Samples t Test; Variance between Groups and Within Groups; One-Way ANOVA F Test: Logic and Hypotheses; Computing the One-Way ANOVA F Test; Critical Values and Decision Rules; Numeric Example of a One-Way ANOVA F Test; Testing the Null Hypothesis; Assumptions and Robustness; How to Tell Which Group Is Best; Multiple Comparison Procedures and Hypotheses; Many Statistics Possible for Multiple Comparisons; Confidence Intervals in a One-Way ANOVA Design Tests and Estimates for Bivariate Linear Relationships; Introduction; Hypothesizing about a Correlation; Testing a Null Hypothesis about a Correlation; Assumptions of Pearson’s r ; Using a Straight Line for Prediction; Linear Regression Analysis; Determining the Best-Fitting Line; Hypothesis Testing in Bivariate Regression; Confidence Intervals in Simple Regression; Limitations on Using Regression Analysis of Frequencies and Ranks; Introduction; One-Sample Proportion; Confidence Interval for a Proportion; Goodness of Fit Hypotheses; Goodness of Fit Statistic; Computing the Chi-Square Test for Goodness of Fit; Goodness of Fit: Assumptions and Robustness; Chi-Square for Independence; Hypotheses for Chi-Square for Independence; Computing Chi-Square for Independence; Relative Risk; Odds Ratios; Analysis of Ranks Choosing an Analysis Plan; Introduction; Statistics That We Have Covered; Organizing Our List: Kind of Outcomes, Number of Samples; Adding to the Tree: Two Samples; Adding Again to the Tree: More Than Two Samples; Completing the Tree: Analysis of Categories; Completing the Tree: The Remaining Categorical Analyses Suggested Answers to Odd-Numbered Exercises Appendix Index References appear at the end of each chapter.
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2015
• Extent: 1 online resource, illustrations (colour)
• Subjects: 610.21; Medical statistics -- Textbooks
• Languages: English
• ISBNs: 9781466565340; 9781466565357
• Related ISBNs: 9781466565333
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.