Statistics for Health Policy and Administration Using Microsoft Excel / Edition 2by James E. Veney
Pub. Date: 10/31/2003
Statistics for Health Policy and Administration Using Microsoft Excel provides an introduction to the use of statistics in health policy and health administration-related fields. The book is keyed to the powerful statistical tool Microsoft Excel and shows how to prepare data and understand the data display capabilities of the program. It also offers the information needed to master probability— the foundation of statistical analysis. This important book is structured around illustrative examples that are demonstrated with Excel displays. The book is filled with step-by-step discussions of how statistical problems are solved using Excel, and it contains a wealth of exercises addressing the material covered in each section. Many of the book's exercises include the replication of examples so that the student has an immediate reference with which to compare his or her work and determine whether or not the procedure was carried out correctly. Additional exercises are provided for further practice to reinforce the valuable lessons learned
- Publication date:
- Public Health/Epidemiology and Biostatistics Series, #6
- Edition description:
- Older Edition
- Product dimensions:
- 7.28(w) x 9.55(h) x 1.57(d)
Table of Contents
CHAPTER 1: INTRODUCTION.
1.1 How This Book Differs from Other Statistics Texts.
1.2 Examples of Statistical Applications in Health Policy and 1.3 What Is the Big Picture?
1.4 Some Initial Definitions.
1.5 Five Statistical Tests.
1.6 Outline of the Book.
CHAPTER 2: EXCEL AS A STATISTICAL TOOL.
2.1 The Very Basics.
2.2 Working in and Moving Around in an Excel Spreadsheet.
2.3 Excel Functions.
2.4 The =IF() Function.
2.5 Excel Graphs.
2.6 Sorting a String of Data.
2.7 Excel’s Data Analysis Pack.
2.8 Functions That Give Results in More Than One Cell.
2.9 The Dollar Sign Convention for Cell References.
CHAPTER 3: DATA ACQUISITION: SAMPLING AND DATA PREPARATION.
3.1 The Nature of Data.
3.3 Data Access and Preparation.
3.4 Missing Data.
CHAPTER 4: DATA DISPLAY: DESCRIPTIVE PRESENTATION, EXCEL GRAPHING CAPABILITY.
4.1 The Excel Frequency Function for the Display of
4.2 Using the Pivot Table to Generate Frequencies of Categorical Variables.
4.3 A Logical Extension of the Pivot Table: Two Variables.
CHAPTER 5: BASIC CONCEPTS OF PROBABILITY.
5.1 Some Initial Concepts and Definitions.
5.2 Marginal Probabilities, Joint Probabilities, and Conditional Probabilities.
5.3 Binomial Probability 152
5.4 The Poisson Distribution 165
5.5 The Normal Distribution 169
CHAPTER 6: MEASURES OF CENTRAL TENDENCY AND DISPERSION: DATA DISTRIBUTIONS.
6.1 Measures of Central Tendency and Dispersion.
6.2 The Distribution of Frequencies 186
6.3 The Sampling Distribution of the Mean 197
6.4 Mean and Standard Deviation of a Discrete
6.5 The Distribution of a Proportion.
6.6 The t Distribution.
CHAPTER 7: CONFIDENCE LIMITS AND HYPOTHESIS TESTING.
7.1 What Is a Confidence Interval?
7.2 Calculating Confidence Limits for Multiple Samples.
7.3 What Is Hypothesis Testing?
7.4 Type I and Type II Errors.
7.5 Selecting Sample Sizes.
CHAPTER 8: STATISTICAL TESTS FOR CATEGORICAL DATA.
8.1 Independence of Two Variables.
8.2 Examples of Chi-Square Analyses.
8.3 Small Expected Values in Cells.
CHAPTER 9: t TESTS FOR RELATED AND UNRELATED DATA.
9.1 What Is a t Test?
9.2 A t Test for Comparing Two Groups.
9.3 A t Test for Related Data.
CHAPTER 10: ANALYSIS OF VARIANCE.
10.1 One-Way Analysis of Variance.
10.2 ANOVA for Repeated Measures.
10.3 Factorial Analysis of Variance.
CHAPTER 11: SIMPLE LINEAR REGRESSION.
11.1 Meaning and Calculation of Linear Regression.
11.2 Testing the Hypothesis of Independence.
11.3 The Excel Regression Add-In.
11.4 The Importance of Examining the Scatterplot.
11.5 The Relationship Between Regression and the t Test.
CHAPTER 12: MULTIPLE REGRESSION: CONCEPTS AND CALCULATION,
12.2 Multiple Regression and Matrices.
CHAPTER 13: EXTENSIONS OF MULTIPLE REGRESSION.
13.1 Dummy Variables in Multiple Regression.
13.2 The Best Regression Model.
13.3 Correlation and Multicolinearity.
13.4 Nonlinear Relationships.
CHAPTER 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.
14.3 Logit for Estimating Dichotomous Dependent Variables.
14.4 A Comparison of OLS, WLS, and Logit.
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