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Statistics for Health Care Professionals: Working with Excel 2nd Revised edition [Pehme köide]

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  • Formaat: Paperback / softback, 528 pages, kõrgus x laius x paksus: 231x174x27 mm, kaal: 840 g, Illustrations
  • Sari: Public Health/Epidemiology and Biostatistics
  • Ilmumisaeg: 11-Sep-2009
  • Kirjastus: John Wiley & Sons Ltd
  • ISBN-10: 0470393319
  • ISBN-13: 9780470393314
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Statistics for Health Care Professionals: Working with Excel 2nd Revised editionSuurem pilt
  • Formaat: Paperback / softback, 528 pages, kõrgus x laius x paksus: 231x174x27 mm, kaal: 840 g, Illustrations
  • Sari: Public Health/Epidemiology and Biostatistics
  • Ilmumisaeg: 11-Sep-2009
  • Kirjastus: John Wiley & Sons Ltd
  • ISBN-10: 0470393319
  • ISBN-13: 9780470393314
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470393314.html
Märksõnad:
Statistics for Health Care Professionals: Working with Excel (second edition) is written in a clear, easily followed style keyed to the powerful statistical tool, Microsoft Excel 2007. It introduces the use of statistics applicable to health administration, health policy, public health, health information management, and other professions, emphasizing the logic of probability and statistical analysis in all areas. Coverage includes data acquisition, data display, basics of probability, data distributions, confidence limits and hypothesis testing, statistical tests for categorical data, tests for related and unrelated data, analysis of variance, simple linear regression, multiple regression, and analysis with a dichotomous categorical dependent variable. A glossary and section-by-section review questions round out this uniquely comprehensive and accessible text.
Preface.
Acknowledgments.
The Authors.
Part One.
1 Statistics and Excel.
1.1 How This Book Differs from Other Statistics Texts.
1.2 Statistical Applications in Health Policy and Health Administration.
Exercises for Section 1.2.
1.3 What Is the ""Big Picture""?
1.4 Some Initial Definitions.
Exercises for Section 1.4.
1.5 Five Statistical Tests.
Exercises for Section 1.5.
Key Terms.
2 Excel as a Statistical Tool.
2.1 The Basics.
Exercises for Section 2.1.
2.2 Working and Moving Around in a Spreadsheet.
Exercises for Section 2.2.
2.3 Excel Functions.
Exercises for Section 2.3.
2.4 The =IF() Function.
Exercises for Section 2.4.
2.5 Excel Graphs.
Exercises for Section 2.5.
2.6 Sorting a String of Data.
Exercise for Section 2.6.
2.7 The Data Analysis Pack.
2.8 Functions That Give Results in More Than One Cell.
Exercises for Section 2.8.
2.9 The Dollar Sign ($) Convention for Cell References.
Key Terms.
3 Data Acquisition: Sampling and Data Preparation.
3.1 The Nature of Data.
Exercises for Section 3.1.
3.2 Sampling.
Exercises for Section 3.2.
3.3 Data Access and Preparation.
Exercises for Section 3.3.
3.4 Missing Data.
Key Terms.
4 Data Display: Descriptive Presentation, Excel Graphing Capability.
4.1 The =FREQUENCY() Function.
Exercises for Section 4.1.
4.2 Using the Pivot Table to Generate Frequencies of Categorical Variables.
Exercises for Section 4.2.
4.3 A Logical Extension of the Pivot Table: Two Variables.
Exercises for Section 4.3.
Appendix: Using Excel 2007 to Generate Pivot Tables with One Variable.
Key Terms.
5 Basic Concepts of Probability.
5.1 Some Initial Concepts and Definitions.
Exercises for Section 5.1.
5.2 Marginal Probabilities, Joint Probabilities, and Conditional Probabilities.
Exercises for Section 5.2.
5.3 Binomial Probability.
Exercises for Section 5.3.
5.4 The Poisson Distribution.
Exercises for Section 5.4.
5.5 The Normal Distribution.
Key Terms.
6 Measures of Central Tendency and Dispersion: Data Distributions.
6.1 Measures of Central Tendency and Dispersion.
Exercises for Section 6.1.
6.2 The Distribution of Frequencies.
Exercises for Section 6.2.
6.3 The Sampling Distribution of the Mean.
Exercises for Section 6.3.
6.4 Mean and Standard Deviation of a Discrete Numerical Variable.
Exercises for Section 6.4.
6.5 The Distribution of a Proportion.
Exercises for Section 6.5.
6.6 The t Distribution.
Exercises for Section 6.6.
Key Terms.
Part Two.
7 Confidence Limits and Hypothesis Testing.
7.1 What Is a Confidence Interval?
Exercises for Section 7.1.
7.2 Calculating Confidence Limits for Multiple Samples.
Exercises for Section 7.2.
7.3 What Is Hypothesis Testing?
Exercises for Section 7.3.
7.4 Type I and Type II Errors.
Exercises for Section 7.4.
7.5 Selecting Sample Sizes.
Exercises for Section 7.5.
Key Terms.
8 Statistical Tests for Categorical Data.
8.1 Independence of Two Variables.
Exercises for Section 8.1.
8.2 Examples of Chi-Square Analyses.
Exercises for Section 8.2.
8.3 Small Expected Values in Cells.
Exercises for Section 8.3.
Key Terms.
9 t Tests for Related and Unrelated Data.
9.1 What Is a t Test?.
Exercises for Section 9.1.
9.2 A t Test for Comparing Two Groups.
Exercises for Section 9.2.
9.3 A t Test for Related Data.
Exercises for Section 9.3.
Key Terms.
10 Analysis of Variance.
10.1 One-Way Analysis of Variance.
Exercises for Section 10.1.
10.2 ANOVA for Repeated Measures.
Exercises for Section 10.2.
10.3 Factorial Analysis of Variance.
Exercises for Section 10.3.
Key Terms.
11 Simple Linear Regression.
11.1 Meaning and Calculation of Linear Regression.
Exercises for Section 11.1.
11.2 Testing the Hypothesis of Independence.
Exercises for Section 11.2.
11.3 The Excel Regression Add-In.
Exercises for Section 11.3.
11.4 The Importance of Examining the Scatterplot.
11.5 The Relationship Between Regression and the t Test.
Exercises for Section 11.5.
Key Terms.
12 Multiple Regression: Concepts and Calculation.
12.1 Introduction.
Exercises for Section 12.1.
12.2 Multiple Regression and Matrices.
Exercises for Section 12.2.
Key Terms.
13 Extensions of Multiple Regression.
13.1 Dummy Variables in Multiple Regression.
Exercises for Section 13.1.
13.2 The Best Regression Model.
Exercises for Section 13.2.
13.3 Correlation and Multicolinearity.
Exercises for Section 13.3.
13.4 Nonlinear Relationships.
Exercises for Section 13.4.
Key Terms.
14 Analysis with a Dichotomous Categorical Dependent Variable.
14.1 Introduction to the Dichotomous Dependent Variable.
14.2 An Example with a Dichotomous Dependent Variable: Traditional Treatments.
Exercises for Section 14.2.
14.3 Logit for Estimating Dichotomous Dependent Variables.
Exercises for Section 14.3.
14.4 A Comparison of Ordinary Least Squares, Weighted Least Squares, and Logit.
Exercises for Section 14.4.
Key Terms.
Glossary.
References.
Index.
John F. Kros is an associate professor of marketing and supply chain management at East Carolina University College of Business, Greenville, North Carolina. David A. Rosenthal is an associate professor in the Department of Health Informatics and Information Management at the University of Tennessee's Health Science Center, Memphis.