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Table of Contents

                            Preface
Prologue
More Tea?
1
A Refreshing
Glass of Math
	Building a Foundation
	Inverse Functions
	exponents and logarithms
	Rules for exponents and logarithms
	Differential calculus
	Matrices
		Adding Matrices
		Multiplying Matrices
		The Rules of Matrix Multiplication
		Identity and Inverse Matrices
	Statistical Data Types
	Hypothesis Testing
	Measuring Variation
		Sum of Squared Deviations
		Variance
		Standard Deviation
	Probability Density Functions
		Normal Distributions
		Chi-Squared Distributions
		Probability Density Distribution Tables
		F Distributions
2
Simple
Regression
Analysis
	First Steps
	Plotting the Data
	The Regression Equation
	General Regression Analysis Procedure
		step 1: Draw a scatter plot of the independent variable versus the dependent variable. If the dots line up, the variables may be correlated.
		Step 2: Calculate the regression equation.
		Step 3: Calculate the correlation coefficient (R ) and assess our population and assumptions.
	Samples and Populations
	Assumptions of Normality
		Step 4: Conduct the analysis of variance.
		Step 5: Calculate the confidence intervals.
		step 6: Make a prediction!
	Which Steps Are Necessary?
	Standardized Residual
	Interpolation and Extrapolation
	Autocorrelation
	Nonlinear Regression
	Transforming Nonlinear Equations into Linear Equations
3
Multiple
Regression
Analysis
	Predicting with Many Variables
	The multiple regression equation
		Multiple Regression Analysis Step-by-Step
		step 1: draw a scatter plot of each predictor variable and the outcome variable to see if they appear to be related.
		step 2: calculate the multiple regression equation.
		Step 3: Examine the accuracy of the multiple regression equation.
	The Trouble with R2
	Adjusted R2
	hypothesis testing with multiple regression
		Step 4: Conduct the Analysis of Variance (ANOVA) Test.
		Finding S11 and S22
		Step 5: calculate Confidence intervals for the population.
		step 6: Make A Prediction!
	choosing the best combination of Predictor variables
	Assessing Populations with 
Multiple Regression Analysis
	Standardized Residuals
	Mahalanobis Distance
		Step 1
		Step 2
		Step 3
	Using Categorical Data in Multiple Regression analysis
	Multicollinearity
	determining the Relative Influence of Predictor Variables on the Outcome Variable
4
Logistic
Regression
Analysis
	The Final Lesson
	the maximum likelihood method
		Finding the maximum likelihood Using the Likelihood Function
	Choosing Predictor variables
	Logistic regression analysis in Action!
		Logistic Regression Analysis Step-by-Step
		Step 1: draw a scatter plot of the predictor variables and the outcome variable to see whether they appear to be related.
		Step 2: calculate the logistic regression equation.
		Step 3: assess the accuracy of the equation
		Step 4: conduct the hypothesis tests.
		Step 5: Predict whether the Norns special will sell.
	Logistic Regression Analysis in the Real World
	Logit, Odds Ratio, and Relative Risk
		Logit
		Odds Ratio
		Adjusted Odds Ratio
		Hypothesis Testing with Odds
		Confidence Interval for an Odds Ratio
		Relative Risk
A
Regression Calculations with Excel
	Euler’s Number
	Power
	Natural Logarithm
	Matrix Multiplication
	Matrix Inversion
	Calculating a Chi-Squared Statistic from a p-Value
	Calculating a p-Value from a Chi-Squared Statistic
	Calculating an F Statistic from a p-Value
	Calculating a p-Value for an F Distribution
	Partial Regression Coefficient of a Multiple Regression Analysis
	Regression Coefficient of a Logistic Regression Equation
Index
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