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by David A. Rosenthal, John F. Kros
Statistics for Health Care Management and Administration
Title Page
Copyright
Dedication
Preface
Introducing Excel
So How Did We Get to Here?
Intended Level of the Textbook
Textbook Organization
Leading by Example(s)
Acknowledgments
The Authors
Part 1
Chapter 1: Statistics and Excel
1.1 How This Book Differs from Other Statistics Texts
1.2 Statistical Applications in Health Policy and Health Administration
1.3 What Is the “Big Picture”?
1.4 Some Initial Definitions
1.5 Five Statistical Tests
Key Terms
Chapter 2: Excel as a Statistical Tool
2.1 The Basics
2.2 Working and Moving Around in a Spreadsheet
2.3 Excel Functions
2.4 The =IF() Function
2.5 Excel Graphs
2.6 Sorting a String of Data
2.7 The Data Analysis Pack
2.8 Functions That Give Results in More than One Cell
2.9 The Dollar Sign ($ ) Convention for Cell References
Key Terms
Chapter 3: Data Acquisition: Sampling and Data Preparation
3.1 The Nature of Data
3.2 Sampling
3.3 Data Access and Preparation
3.4 Missing Data
Key Terms
Chapter 4: Data Display: Descriptive Presentation, Excel Graphing Capability
4.1 Creating, Displaying, and Understanding Frequency Distributions
4.2 Using the Pivot Table to Generate Frequencies of Categorical Variables
4.3 A Logical Extension of the Pivot Table: Two Variables
Key Terms
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
5.4 The Poisson Distribution
5.5 The Normal Distribution
Key Terms
Chapter 6: Measures of Central Tendency and Dispersion: Data Distributions
6.1 Measures of Central Tendency and Dispersion
6.2 The Distribution of Frequencies
6.3 The Sampling Distribution of the Mean
6.4 Mean and Standard Deviation of a Discrete Numerical Variable
6.5 The Distribution of a Proportion
6.6 The t Distribution
Key Terms
Part 2
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
Key Terms
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
Key Terms
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
Key Terms
Chapter 10: Analysis of Variance
10.1 One-Way Analysis of Variance
10.2 ANOVA for Repeated Measures
10.3 Factorial Analysis of Variance
Key Terms
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
Key Terms
Chapter 12: Multiple Regression: Concepts and Calculation
12.1 Introduction
Key Terms
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
Key Terms
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 Ordinary Least Squares, Weighted Least Squares, and Logit
Key Terms
Appendix A: Multiple Regression and Matrices
An Introduction to Matrix Math
Addition and Subtraction of Matrices
Multiplication of Matrices
Matrix Multiplication and Scalars
Finding the Determinant of a Matrix
Matrix Capabilities of Excel
Explanation of Excel Output Displayed with Scientific Notation
Using the b Coefficients to Generate Regression Results
Calculation of All Multiple Regression Results
References
Glossary
Index
End User License Agreement
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Prev
Previous Chapter
Cover
Next
Next Chapter
Title Page
Table of Contents
Title Page
Copyright
Dedication
Preface
Introducing Excel
So How Did We Get to Here?
Intended Level of the Textbook
Textbook Organization
Leading by Example(s)
Acknowledgments
The Authors
Part 1
Chapter 1: Statistics and Excel
1.1 How This Book Differs from Other Statistics Texts
1.2 Statistical Applications in Health Policy and Health Administration
1.3 What Is the “Big Picture”?
1.4 Some Initial Definitions
1.5 Five Statistical Tests
Key Terms
Chapter 2: Excel as a Statistical Tool
2.1 The Basics
2.2 Working and Moving Around in a Spreadsheet
2.3 Excel Functions
2.4 The
=IF()
Function
2.5 Excel Graphs
2.6 Sorting a String of Data
2.7 The Data Analysis Pack
2.8 Functions That Give Results in More than One Cell
2.9 The Dollar Sign (
$
) Convention for Cell References
Key Terms
Chapter 3: Data Acquisition: Sampling and Data Preparation
3.1 The Nature of Data
3.2 Sampling
3.3 Data Access and Preparation
3.4 Missing Data
Key Terms
Chapter 4: Data Display: Descriptive Presentation, Excel Graphing Capability
4.1 Creating, Displaying, and Understanding Frequency Distributions
4.2 Using the Pivot Table to Generate Frequencies of Categorical Variables
4.3 A Logical Extension of the Pivot Table: Two Variables
Key Terms
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
5.4 The Poisson Distribution
5.5 The Normal Distribution
Key Terms
Chapter 6: Measures of Central Tendency and Dispersion: Data Distributions
6.1 Measures of Central Tendency and Dispersion
6.2 The Distribution of Frequencies
6.3 The Sampling Distribution of the Mean
6.4 Mean and Standard Deviation of a Discrete Numerical Variable
6.5 The Distribution of a Proportion
6.6 The
t
Distribution
Key Terms
Part 2
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
Key Terms
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
Key Terms
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
Key Terms
Chapter 10: Analysis of Variance
10.1 One-Way Analysis of Variance
10.2 ANOVA for Repeated Measures
10.3 Factorial Analysis of Variance
Key Terms
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
Key Terms
Chapter 12: Multiple Regression: Concepts and Calculation
12.1 Introduction
Key Terms
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
Key Terms
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 Ordinary Least Squares, Weighted Least Squares, and Logit
Key Terms
Appendix A: Multiple Regression and Matrices
An Introduction to Matrix Math
Addition and Subtraction of Matrices
Multiplication of Matrices
Matrix Multiplication and Scalars
Finding the Determinant of a Matrix
Matrix Capabilities of Excel
Explanation of Excel Output Displayed with Scientific Notation
Using the
b
Coefficients to Generate Regression Results
Calculation of All Multiple Regression Results
References
Glossary
Index
End User License Agreement
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Guide
Cover
Table of Contents
Begin Reading
List of Illustrations
Chapter 2: Excel as a Statistical Tool
Figure 2.1 Initial view of an Excel spreadsheet
Figure 2.2 Excel arithmetical conventions
Figure 2.3 Moving around a data set
Figure 2.4 Result of Ctrl+Shift+Right arrow
Figure 2.5 Highlighting an entire column
Figure 2.6 Copying a formula to several cells
Figure 2.7 Moving a data range with drag and drop
Figure 2.8 The Undo button
Figure 2.9 Insert Function dialog box
Figure 2.10 Function Arguments dialog box
Figure 2.11 Calculation of average
Figure 2.12 Summing two noncontiguous areas
Figure 2.13 Use of the
=IF()
function
Figure 2.14 Nested
=IF()
functions
Figure 2.15 Insert Chart dialog box
Figure 2.16 A basic bar graph
Figure 2.17 Excel's chart pop-up menu
Figure 2.18 Select Data Source dialog box
Figure 2.19 A different view of the same data
Figure 2.20 Chart formatting pop-up menu
Figure 2.21 Sort dialog box
Figure 2.22 Sort dialog box and data to be sorted
Figure 2.23 Sort dialog box with two-column sort options
Figure 2.24 Result of data sort on two variables
Figure 2.25 Data Analysis option
Figure 2.26 Excel Options button
Figure 2.27 Excel Options dialog box with add-ins screen displayed
Figure 2.28 Add-Ins dialog box with Analysis ToolPak selected
Figure 2.29 Data Analysis dialog box
Figure 2.30 Frequency calculation
Figure 2.31 Matrix math example
Figure 2.32 Calculations of percentages
Chapter 3: Data Acquisition: Sampling and Data Preparation
Figure 3.1 A small data set
Figure 3.2 Constructed data for an imaginary clinic
Figure 3.3 Beginning of random number generation
Figure 3.4 Paste Special dialog box
Figure 3.5 Sort dialog box
Figure 3.6 Result of sort operation
Figure 3.7 Partial list of women who will receive each intervention
Figure 3.8 Random Number Generation dialog box
Figure 3.9 Five sets of 10 random numbers
Figure 3.10 Value and probability input range (example)
Figure 3.11 Text Import Wizard, Step 1
Figure 3.12 Text Import Wizard, Step 2
Figure 3.13 Text Import Wizard, Step 3
Figure 3.14 Data as initially imported from a text file
Figure 3.15 Making imported dates century-correct
Figure 3.16 Text file imported to Excel with ID and Variable labels
Figure 3.17 Calculation of length of stay
Figure 3.18 Calculation of age
Figure 3.19 Imported file ready for analysis
Chapter 4: Data Display: Descriptive Presentation, Excel Graphing Capability
Figure 4.1
=MIN()
and
=MAX()
functions
Figure 4.2 Frequency distribution of age
Figure 4.3 Formulas for frequency distribution of age
Figure 4.4 Data Analysis dialog box
Figure 4.5 Histogram dialog box
Figure 4.6 Final output for histogram example
Figure 4.7 Chart of the age frequency distribution
Figure 4.8 Chart of age showing
BIN
ranges
Figure 4.9 Line chart depiction of age frequencies
Figure 4.10 Bar chart depiction of age frequencies
Figure 4.11 Pie chart depiction of age frequencies
Figure 4.12 XY(Scatter) chart of age and LOS
Figure 4.13 Cumulative frequency and percentage distributions
Figure 4.14 Formula view of Figure 4.13
Figure 4.15 Graph of age showing actual and cumulative values
Figure 4.16 Four distribution types
Figure 4.17 Graph of Medicare payments
Figure 4.18 Infant mortality for 149 countries of the world
Figure 4.19 Infant mortality for states of the United States
Figure 4.20 Simulation of the roll of a fair die 100 times
Figure 4.21 Create PivotTable dialog box
Figure 4.22 Pivot table layout screen
Figure 4.23 Finished pivot table for Sex category
Figure 4.24 Value Field Settings dialog box
Figure 4.25 Pivot table layout screen
Figure 4.26 Two-variable pivot table for DRG category and Sex
Figure 4.27 A Pareto chart for DRG categories
Chapter 5: Basic Concepts of Probability
Figure 5.1 Possible combinations of five coin flips
Figure 5.2 Venn diagram for two mutually exclusive events
Figure 5.3 Children ever born to 2,556 women
Figure 5.4 Sequential events
Figure 5.5 First 20 observations in an emergency room visit file
Figure 5.6 Contingency table of shift and emergency status
Figure 5.7 Joint probabilities for shift and emergency status
Figure 5.8 Venn diagram of two events that are not mutually exclusive
Figure 5.9 Joint probability “or” for shift and emergency status
Figure 5.10 Conditional probabilities for arrival during any shift
Figure 5.11 Conditional probabilities for high- and low-income women and number of children
Figure 5.12 All possible outcomes of the flip of a coin five times
Figure 5.13 All possible outcomes of five emergency room visits
Figure 5.14 Probabilities of number of visits that are actual emergencies
Figure 5.15 Probabilities of number of visits using formulas
Figure 5.16 Formulas used for calculations of probabilities
Figure 5.17 The
=BINOMDIST()
function
Figure 5.18 Binomial distribution for emergencies in an eight-hour shift
Figure 5.19 Binomial distributions for 0.75 and 0.85 correct
Figure 5.20 Poisson distribution of emergency room arrivals in 15-minute intervals
Figure 5.21 Calculated Poisson distribution of emergency room arrivals in 15-minute intervals
Figure 5.22 Calculated Poisson distribution of emergency room arrivals: Excel formulas
Figure 5.23 Poisson distribution for gloves that are not usable in a box of 100
Figure 5.24 A normal distribution
Chapter 6: Measures of Central Tendency and Dispersion: Data Distributions
Figure 6.1 Time spent by physician with patients
Figure 6.2 Ordered time spent by physician with patients
Figure 6.3 Calculation of variance
Figure 6.4 All samples of two from a population of four
Figure 6.5 Sum of absolute differences
Figure 6.6 Sum of absolute differences, second example
Figure 6.7 Sum of squared differences
Figure 6.8 Frequency distribution with mean and standard deviation, HDI data
Figure 6.9 Histogram (graph) of HDI values
Figure 6.10 Normal distribution
Figure 6.11 Calculations for normal distribution
Figure 6.12 Cumulative normal distribution
Figure 6.13 Cumulative normal probabilities for the weight of newborns
Figure 6.14 Length of stay for one year of discharges from a 200-bed hospital
Figure 6.15 Distribution of 250 sample means
Figure 6.16 Comparison of the population variance divided by two and the variance of the mean of all samples of size two
Figure 6.17 Probabilities of prenatal visits
Figure 6.18 Data Analysis dialog box
Figure 6.19 Random Number Generation dialog box
Figure 6.20 Random samples of visits
Figure 6.21 Example of calculations of means, standard deviations, and standard errors for 100 samples
Figure 6.22 Distribution of means from 250 samples of size 100
Figure 6.23 Calculation of the mean and standard deviation of a discrete numerical variable
Figure 6.24 Portion of correctly and incorrectly completed forms
Figure 6.25 Calculation of probability of 85 percent correct
Figure 6.26 Probability of 70 percent or less
Figure 6.27 Use of the
=NORMDIST()
function
Figure 6.28 Degrees of freedom
Figure 6.29 Two
t
distributions
Figure 6.30 Exact probabilities of
t
distributions
Chapter 7: Confidence Limits and Hypothesis Testing
Figure 7.1 Distribution of costs for 12,000 discharges
Figure 7.2 Confidence limits from 10 samples
Figure 7.3 Calculation of means and limits
Figure 7.4 Distribution of sample means around 48 inches
Figure 7.5 Distribution of sample means for 48 inches and 49 inches
Figure 7.6 Distribution around true means of 45 and 48 inches
Figure 7.7 Sixty-eight percent confidence limits for a distribution around 48 Inches
Figure 7.8 Distributions around three true means
Figure 7.9 Distributions around 48 and 44.6 inches
Figure 7.10 Two distributions for a sample of 290
Figure 7.11 Upper limit for
Figure 7.12 Positioning
Figure 7.13 Low beta value
Figure 7.14 Effect of sample size on standard error and measurement error
Chapter 8: Statistical Tests for Categorical Data
Figure 8.1 Marginal frequencies
Figure 8.2 Marginal frequencies with most probable internal frequencies
Figure 8.3 Marginal and conditional probabilities
Figure 8.4 Different conditional probabilities
Figure 8.5 Template for the chi-square
Figure 8.6 The
=CHITEST
function
Figure 8.7 The chi-square distribution for one degree of freedom
Figure 8.8 Example from the Halpern et al. ([2001]) article
Figure 8.9 An
n
-by-two chi-square
Figure 8.10 Number of children and desire for more
Figure 8.11 Adequacy of treatment of three conditions
Figure 8.12 Yates's correction
Figure 8.13 Small expected values in
df
> 1
Chapter 9: t TESTS FOR RELATED AND UNRELATED DATA
Figure 9.1 Distribution of 250
t
tests when H0 = $5,905
Figure 9.2 Distribution of 250
t
tests when H0 = $4,500
Figure 9.3 Region of rejection for two-tail test
Figure 9.4 Region of rejection for one-tail test
Figure 9.5 Type II error for true means of $5,000 and $7,000
Figure 9.6 Type II error for true means of $5,000 and $7,000 and sample size 150 for each group
Figure 9.7 Results of a breast cancer experiment
Figure 9.8
F
distribution
Figure 9.9 Dialog box for
t
test for equal variance
Figure 9.12 Calculation of before and after
t
test
Figure 9.10 Results of Excel
t
test for equal variance
Figure 9.11 Results of Excel
t
test for unequal variance
Figure 9.13 Excel add-in for before and after
t
test
Chapter 10: Analysis of Variance
Figure 10.1 Average cost distribution for two hospitals
Figure 10.2 Average cost distribution for four hospitals
Figure 10.3 Analysis of variance for four hospitals
Figure 10.4 Data arrangement for the Excel Single-Factor ANOVA add-in
Figure 10.5 ANOVA: Single-Factor dialog box
Figure 10.6 Output of the ANOVA: Single-Factor data analysis add-in
Figure 10.7 Test of differences between two means in ANOVA
Figure 10.8 Bartlett test for homogeneity of variance: Interpreting the Bartlett test
Figure 10.9 Data for ANOVA for repeated measures
Figure 10.10 Results of ANOVA repeated measures
Figure 10.11 Calculation of
SS
R
Figure 10.12 Degrees of freedom in repeated measures
Figure 10.14 ANOVA factorial analysis
Figure 10.15 Degrees of freedom in two-factor factorial ANOVA
Figure 10.16 Data arrangement for ANOVA: Two-Factor with Replication
Figure 10.17 Results of ANOVA: Two-Factor with Replication
Figure 10.18 Simple data for repeated measures in a factorial design
Figure 10.19 ANOVA results for repeated measures in a factorial design
Figure 10.20 Sources of variation and degrees of freedom in factorial designs
Figure 10.21 Appropriate analysis for repeated measures, two-factor design
Chapter 11: Simple Linear Regression
Figure 11.1 Examples of relationships
Figure 11.2 Positive relationship with the best-fitting straight line
Figure 11.3 Twenty hospital stays
Figure 11.4 Length of stay and charges
Figure 11.5 Calculation of coefficients
Figure 11.6 Calculation of
R
2
and
F
Figure 11.7 Total variance, regression variance, and error variance
Figure 11.8 Excel Data Analysis add-in dialog box
Figure 11.9 Regression dialog box
Figure 11.10 Results of using the regression add-in
Figure 11.11 Four data sets
Figure 11.12 Scatterplots of four data sets where
y
= .5
x
+ 1.55
Figure 11.13 Regression as a
t
test
Chapter 12: Multiple Regression: Concepts and Calculation
Figure 12.1 Cost data for 10 hospitals
Figure 12.2 Initial Regression dialog box
Figure 12.3 Multiple regression output
Figure 12.4 Calculation of sums
Figure 12.5 Successive elimination for
b
j
Chapter 13: Extensions of Multiple Regression
Figure 13.1 Data for 20 hospital days with sex as a dummy variable
Figure 13.2 Results of regression for 20 hospitals
Figure 13.3 Graph of cost data by length of stay
Figure 13.4 Hospital charges with dummy and interaction
Figure 13.5 Regression coefficients with dummy and interactions
Figure 13.6 Hospital charges with dummy and interaction: Modified example
Figure 13.7 Regression coefficients with dummy only: Modified example
Figure 13.8 Regression coefficients with dummy and interaction: Modified example
Figure 13.9 Hospital charges with dummy and iteration graphed
Figure 13.10 Charge data showing an interaction effect
Figure 13.11 Coefficients for charge data showing an interaction effect
Figure 13.12 Coefficients for charge data showing only the interaction effect
Figure 13.13 Graph of charge data with predicted lines
Figure 13.14 Data from
The State of the World's Children
[1996]
Figure 13.15 Coefficients for each of the predictor variables for
U5Mort
independently
Figure 13.16 Multiple regression coefficients of predictors for
U5Mort
Figure 13.17 Multiple regression coefficients of best predictors for
U5Mort
: Backward elimination
Figure 13.18 Multiple regression coefficients of best predictors for
U5Mort
: Forward inclusion
Figure 13.19 Multiple regression coefficients of best predictors for
U5Mort
: Health predictors
Figure 13.20 Multiple regression coefficients of best predictors for
U5Mort
: Cultural predictors
Figure 13.21 Correlation among the predictors for
U5Mort
Figure 13.22
CPR
and
TFR
together as predictors for
U5Mort
Figure 13.23
CPR
and
TFR
together as predictors for
U5Mort
: Reduced sample
Figure 13.24 Relationship between
GNP
and
U5Mort
Figure 13.25 Sample of data for second-degree curve analysis of
U5Mort
Figure 13.26 Relationship between
GNP
and
U5Mort
with
U5Mort
predicted: Second-degree curve
Figure 13.27 Relationship between
LogGNP
and
U5Mort
Figure 13.28 Relationship between
LogGNP
and
LogU5Mort
Figure 13.29 Relationship between
GNP
and
U5Mort
with
HiGNP
dummy
Figure 13.30
LogGNP
versus
Log U5Mort
with Format Data Series menu displayed
Figure 13.31 Format Trendline dialog box
Figure 13.32 Selections in the Format Trendline dialog box
Figure 13.33 Best-fitting line for LogGNP and LogU5Mort: Linear model
Figure 13.34 Best-fitting line for GNP and U5Mort: Logarithmic model
Figure 13.35 Best-fitting line for GNP and U5Mort: Power model
Figure 13.36 Best-fitting line for
GNP
and
U5Mort
: Exponential model
Chapter 14: Analysis with a Dichotomous Categorical Dependent Variable
Figure 14.1 Data for 32 mothers
Figure 14.2 One-zero conversion of data for 32 mothers
Figure 14.3 Chi-square analysis of immunization by age of mother
Figure 14.4 Results of OLS for immunization data
Figure 14.5 Calculation of weights for WLS
Figure 14.6 Weighted least squares results
Figure 14.7 Regression setup for weighted least squares analysis
Figure 14.8 Calculation of pseudo
R
square for WLS
Figure 14.9 Graph of two relationships between independent and dependent variables
Figure 14.10 First step in finding logL
Figure 14.11 Complete layout for maximizing logL
Figure 14.12 Solver Parameters dialog box for maximizing logL
Figure 14.13 Solver solution for Logit model
Figure 14.14 Calculation of chi-square for Logit
Figure 14.15 First step in the calculation of the information matrix
Figure 14.16 Calculation of
q
i
×
x
ij
Figure 14.17 Formation of the transpose matrix
Figure 14.18 Information matrix and t tests
Figure 14.19 Comparison of OLS-, WLS-, and Logit-predicted values
Multiple Regression and Matrices
Figure A.1 Hospital data
Figure A.2 Arrays
y
,
X
, and
X′
Figure A.3 Arrays
X′X
and
X′y
Figure A.4 Inverse of
X′X
Figure A.5 The b coefficients
Figure A.6 Calculation of standard errors of
b
Figure A.7 Calculation of all results from Figure 12.3
List of Tables
Chapter 2: Excel as a Statistical Tool
Table 2.1 Glossary of Key Excel Terms Used throughout Text
Chapter 3: Data Acquisition: Sampling and Data Preparation
Table 3.1 Formula for Figure 3.17
Table 3.2 Formula for Figure 3.18
Table 3.3 Formula for Figure 3.19
Chapter 6: Measures of Central Tendency and Dispersion: Data Distributions
Table 6.1 Formulas for Figure 6.3
Table 6.2 Formulas for Figure 6.8
Chapter 7: Confidence Limits and Hypothesis Testing
Table 7.1 Formulas for Figure 7.3
Chapter 9: t TESTS FOR RELATED AND UNRELATED DATA
Table 9.1 Formulas for Figure 9.7
Table 9.2 Formulas for Figure 9.12
Chapter 10: Analysis of Variance
Table 10.1 Formulas for Figure 10.3
Table 10.2 Formulas for Figure 10.7
Table 10.3 Formulas for Figure 10.8
Table 10.4 Formulas for Figure 10.9
Table 10.5 Formulas for Figure 10.10
Table 10.7 Formulas for Figure 10.14
Table 10.8 Formulas for Figure 10.21
Chapter 11: Simple Linear Regression
Table 11.1 Formulas for Figure 11.5
Table 11.2 Formulas for Figure 11.6
Chapter 12: Multiple Regression: Concepts and Calculation
Table 12.1 Formulas for Figure 12.4
Chapter 14: Analysis with a Dichotomous Categorical Dependent Variable
Table 14.1 Formulas for Figure 14.11
Table 14.2 Comparing linear regression, logic regression, and survival analysis
Multiple Regression and Matrices
Table A.1 Formulas for Figure A.7
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