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| Model Selection Loglinear Analysis | |
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| Loglinear Modeling Basics | |
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| A Two-Way Table | |
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| The Saturated Model | |
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| Main Effects | |
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| Interactions | |
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| Examining Parameters in a Saturated Model | |
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| Calculating the Missing Parameter Estimates | |
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| Testing Hypotheses about Parameters | |
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| Fitting an Independence Model | |
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| Specifying the Model | |
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| Checking Convergence | |
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| Chi-Square Goodness-of-Fit Tests | |
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| Hierarchical Models | |
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| Generating Classes | |
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| Selecting a Model | |
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| Evaluating Interactions | |
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| Testing Individual Terms in the Model | |
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| Model Selection Using Backward Elimination | |
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| Logit Loglinear Analysis | |
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| Dichotomous Logit Model | |
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| Loglinear Representation | |
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| Logit Model | |
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| Specifying the Model | |
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| Parameter Estimates for the Saturated Logit Model | |
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| Unsaturated Logit Model | |
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| Specifying the Analysis | |
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| Goodness-of-Fit Statistics | |
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| Observed and Expected Cell Counts | |
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| Parameter Estimates | |
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| Measures of Dispersion and Association | |
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| Polychotomous Logit Model | |
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| Specifying the Model | |
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| Goodness of Fit of the Model | |
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| Interpreting Parameter Estimates | |
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| Examining Residuals | |
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| Covariates | |
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| Other Logit Models | |
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| Multinomial Logistic Regression | |
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| The Logit Model | |
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| Baseline Logit Example | |
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| Specifying the Model | |
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| Parameter Estimates | |
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| Likelihood-Ratio Test for Individual Effects | |
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| Likelihood-Ratio Test for the Overall Model | |
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| Evaluating the Model | |
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| Calculating Predicted Probabilities and Expected Frequencies | |
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| Classification Table | |
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| Goodness-of-Fit Tests | |
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| Examining the Residuals | |
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| Pseudo-R-square Measures | |
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| Correcting for Overdispersion | |
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| Automated Variable Selection | |
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| Hierarchical Variable Entry | |
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| Specifying the Analysis | |
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| Step Output | |
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| Likelihood-Ratio Tests for Individual Effects | |
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| Matched Case-Control Studies | |
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| The Model | |
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| Creating the Difference Variables | |
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| The Data File | |
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| Specifying the Analysis | |
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| Examining the Results | |
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| Ordinal Regression | |
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| Fitting an Ordinal Logit Model | |
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| Modeling Cumulative Counts | |
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| Specifying the Analysis | |
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| Parameter Estimates | |
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| Testing Parallel Lines | |
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| Does the Model Fit? | |
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| Comparing Observed and Expected Counts | |
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| Including Additional Predictor Variables | |
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| Overall Model Test | |
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| Measuring Strength of Association | |
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| Classifying Cases | |
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| Generalized Linear Models | |
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| Link Function | |
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| Fitting a Heteroscedastic Probit Model | |
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| Modeling Signal Detection | |
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| Fitting a Location-Only Model | |
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| Fitting a Scale Parameter | |
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| Parameter Estimates | |
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| Model-Fitting Information | |
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| Probit Regression | |
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| Probit and Logit Response Models | |
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| Evaluating Insecticides | |
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| Confidence Intervals for Effective Dosages | |
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| Comparing Several Groups | |
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| Comparing Relative Potencies of the Agents | |
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| Estimating the Natural Response Rate | |
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| More than One Stimulus Variable | |
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| Kaplan-Meier Survival Analysis | |
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| SPSS Procedures for Survival Data | |
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| Background | |
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| Calculating Length of Time | |
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| Estimating the Survival Function | |
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| Estimating the Conditional Probability of Survival | |
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| Estimating the Cumulative Probability of Survival | |
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| The SPSS Kaplan-Meier Table | |
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| Plotting Survival Functions | |
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| Comparing Survival Functions | |
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| Specifying the Analysis | |
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| Comparing Groups | |
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| Stratified Comparisons of Survival Functions | |
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| Life Tables | |
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| Background | |
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| Studying Employment Longevity | |
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| The Body of a Life Table | |
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| Calculating Survival Probabilities | |
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| Assumptions Needed to Use the Life Table | |
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| Lost to Follow-up | |
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| Plotting Survival Functions | |
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| Comparing Survival Functions | |
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| Cox Regression | |
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| The Cox Regression Model | |
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| The Hazard Function | |
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| Proportional Hazards Assumption | |
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| Modeling Survival Times | |
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| Coding Categorical Variables | |
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| Specifying the Analysis | |
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| Testing Hypotheses about the Coefficient | |
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| Interpreting the Regression Coefficient | |
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| Baseline Hazard and Cumulative Survival Rates | |
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| Including Multiple Covariates | |
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| Model with Three Covariates | |
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| Global Tests of the Model | |
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| Plotting the Estimated Functions | |
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| Checking the Proportional Hazards Assumption | |
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| Stratification | |
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| Log-Minus-Log Survival Plot | |
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| Identifying Influential Cases | |
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| Examining Residuals | |
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| Partial (Schoenfeld) Residuals | |
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| Martingale Residuals | |
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| Selecting Predictor Variables | |
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| Variable Selection Methods | |
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| An Example of Forward Selection | |
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| Omnibus Test of the Model At Each Step | |
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| Time-Dependent Covariates | |
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| Examining the Data | |
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| Specifying a Time-Dependent Covariate | |
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| Calculating Segmented Time-Dependent Covariates | |
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| Testing the Proportional Hazard Assumption with a Time-Dependent Covariate1 | |
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| Fitting a Conditional Logistic Regression Model | |
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| Data File Structure | |
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| Specifying the Analysis | |
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| Parameter Estimates | |
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| Variance Components | |
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| Factors, Effects, and Models | |
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| Types of Factors | |
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| Types of Effects | |
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| Types of Models | |
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| Model for One-Way Classification | |
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| Estimation Methods | |
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| Negative Variance Estimates | |
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| Nested Design Model for Two-Way Classification | |
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| Univariate Repeated Measures Analysis Using a Mixed Model Approach1 | |
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| Background Information | |
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| Model | |
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| Distribution Assumptions | |
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| Estimation Methods | |
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| Linear Mixed Models | |
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| The Linear Mixed Model | |
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| Background | |
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| Unconditional Random-Effects Models | |
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| Adding a Gender Fixed Effect | |
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| Hierarchical Models | |
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| Random-Coefficient Model | |
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| A Model with School-Level and Individual-Level Covariates | |
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| A Three-Level Hierarchical Model | |
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| Repeated Measurements | |
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| Selecting a Residual Covariance Structure | |
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| Nonlinear Regression | |
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| What Is a Nonlinear Model? | |
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| Transforming Nonlinear Models | |
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| Intrinsically Nonlinear Models | |
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| Fitting a Logistic Population Growth Model | |
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| Estimating a Nonlinear Model | |
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| Finding Starting Values | |
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| Specifying the Analysis | |
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| Approximate Confidence Intervals for the Parameters | |
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| Bootstrapped Estimates | |
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| Estimating Starting Values | |
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| Use Starting Values from Previous Analysis | |
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| Look for a Linear Approximation | |
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| Use Properties of the Nonlinear Model | |
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| Solve a System of Equations | |
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| Computational Issues | |
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| Additional Nonlinear Regression Options | |
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| Nonlinear Regression Common Models | |
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| Specifying a Segmented Model | |
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| Two-Stage Least-Squares Regression | |
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| Artichoke Data | |
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| Demand-Price-Income Economic Model | |
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| Estimation with Ordinary Least Squares | |
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| Feedback and Correlated Errors | |
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| Two-Stage Least Squares | |
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| Strategy | |
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| Estimating Price | |
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| Estimating the Model | |
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| 2-Stage Least Squares Procedure | |
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| Weighted Least-Squares Regression | |
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| Diagnosing the Problem | |
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| Estimating the Weights | |
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| Estimating Weights as Powers | |
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| Specifying the Analysis | |
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| Examining the Log-Likelihood Functions | |
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| WLS Solutions | |
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| Estimating Weights from Replicates | |
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| Diagnostics from the Linear Regression Procedure | |
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| Multidimensional Scaling | |
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| Data, Models, and Analysis of Multidimensional Scaling | |
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| Example: Flying Mileages | |
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| Nature of Data Analyzed in MDS | |
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| Measurement Level of Data | |
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| Shape of Data | |
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| Conditionality of Data | |
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| Missing Data | |
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| Multivariate Data | |
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| Classical MDS | |
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| Example: Flying Mileages Revisited | |
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| Euclidean Model | |
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| Details of CMDS | |
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| Example: Ranked Flying Mileages | |
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| Repeated CMDS | |
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| Replicated MDS | |
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| Details of RMDS | |
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| Example: Perceived Body-Part Structure | |
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| Weighted MDS | |
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| Geometry of the Weighted Euclidean Model | |
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| Algebra of the Weighted Euclidean Model | |
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| Matrix Algebra of the Weighted Euclidean Model | |
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| Details of WMDS | |
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| Example: Perceived Body-Part Structure | |
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| Weirdness Index | |
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| Flattened Weights | |
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| Bibliography | |
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| Index | |