Cross Section and Experimental Data Analysis Using Eviews

I Gusti Ngurah Agung is a Lecturer and Academic Advisor at the Graduate School of Management, Faculty of Economics at the University of Indonesia. He has been teaching mathematical statistics and applied statistics since 1960 at the Makassar Public University as well as Hassanudin University, Makassar, since 1987 at the Faculty of Economics, University of Indonesia, and since 2006 at the Graduate School of Planning, Strategy and Public Policy, University of Indonesia. Areas of interest include population studies, education, public health, economics, management and finance. Agung has authored one applied statistics textbook in English and more than 10 pocket books in Indonesian. He holds a BSc in Mathematical Education from Hassanudin University, a Masters in Mathematics from the New Mexico State University and a second Masters in mathematical statistics as well as a PhD in biostatistics from the University of North Carolina at Chapel Hill.

Preface. 1 Misinterpretation of Selected Theoretical Concepts of Statistics. 1.1 Introduction. 1.2 What is a Population? 1.3 A Sample and Sample Space. 1.4 Distribution of a Random Sample Space. 1.5 What is a Random Variable? 1.6 Theoretical Concept of a Random Sample. 1.7 Does a Representative Sample Really Exist? 1.8 Remarks on Statistical Powers and Sample Sizes. 1.9 Hypothesis and Hypothesis Testing. 1.10 Groups of Research Variables. 1.11 Causal Relationship between Variables. 1.12 Misinterpretation of Selected Statistics. 2 Simple Statistical Analysis but Good for Strategic Decision Making. 2.1 Introduction. 2.2 A Single Input for Decision Making. 2.3 Data Transformation. 2.4 Biserial Correlation Analysis. 2.5 One-Way Tabulation of a Variable. 2.6 Two-Way Tabulations. 2.7 Three-Way Tabulation. 2.8 Special Notes and Comments. 2.9 Special Cases of the N-Way Incomplete Tables. 2.10 Partial Associations. 2.11 Multiple Causal Associations Based on Categorical Variables. 2.12 Seemingly Causal Model Based on Categorical Variables. 2.13 Alternative Descriptive Statistical Summaries. 2.14 How to Present Descriptive Statistical Summary? 2.15 General Seemingly Causal Model. 2.16 Empirical Studies Presenting Descriptive Statistical Summaries. 3 One-Way Proportion Models. 3.1 Introduction. 3.2 One-Way Proportion Models Based on a 2 2 Table. 3.3 Binary Choice Models Based on a K 2 Table. 3.4 Binary Logit Models Based on N-Way Tabulation. 3.5 General Binary Choice Models. 3.6 Special Notes and Comments. 3.7 Association between Categorical Variables. 3.8 One-Way Binary Choice Models Based on N-Way Tabulation. 3.9 Special Notes and Comments on Binary Choice Models. 4 N-Way Cell-Proportion Models. 4.1 Introduction. 4.2 The N-Way Tabulation of Proportions. 4.3 The 2 2 Factorial Model of Proportions. 4.4 I J Factorial Models of Proportions. 4.5 Multifactorial Cell-Proportion Model. 4.6 Presenting the Statistical Summary. 5 N-Way Cell-Mean Models. 5.1 Introduction. 5.2 One-Way Multivariate Cell-Mean Models. 5.3 N-Way Multivariate Cell-Mean Models. 5.4 Equality Test by Classification. 5.5 Testing Weighted Means Differences. 5.6 Descriptive Statistical Summary. 6 Multinomial Choice Models with Categorical Exogenous Variables. 6.1 Introduction. 6.2 Multinomial Choice Models. 6.3 Ordered Choice Models. 6.4 Concordance Discordance Measure of Association. 6.5 Multifactorial Ordered Choice Models. 6.6 Multilevel Choice Models. 6.7 Special Notes on the Multinomial Logit Model. 6.8 Selected Population Studies Using Multinomial Choice Models. 7 General Choice Models. 7.1 Introduction. 7.2 Binary Choice Models with a Numerical Variable. 7.3 Heterogeneous Binary Choice Models. 7.4 Homogeneous Binary Choice Models. 7.5 General Binary Choice Models. 7.6 Advanced Binary Choice Models. 7.7 Multidimensional Binary Choice Translog Linear Model. 7.8 Piecewise Binary Choice Models. 7.9 Ordered Choice Models with Numerical Independent Variables. 7.10 Studies Using General Choice Models. 7.11 Two-Stage Binary Choice Model. 8 Experimental Data Analysis. 8.1 Introduction. 8.2 Analysis Based on Cell-Mean Models. 8.3 Bivariate Correlation Analysis. 8.4 Effects of the Experimental Factors. 8.5 Effects of the Experimental Factors and Covariates. 8.6 Application of the Ordered Choice Models. 8.7 Application of Seemingly Causal Models. 8.8 Multivariate Analysis of Covariance. 8.9 Tests for Equality of Medians. 8.10 The Simplest Experimental Design. 9 Seemingly Causal Models Based on Numerical Variables. 9.1 Introduction. 9.2 The Simplest Seemingly Causal Model. 9.3 General Linear Models Based on Bivariate (X, Y). 9.4 Models Based on Numerical Trivariate. 9.5 Regression Analysis Using the Principal Components. 9.6 Seemingly Causal Models Based on (X1, X2, Y1, Y2). 9.7 Seemingly Causal Models Based on (X1, X2, X3, Y1, Y2). 9.8 New Types of Interaction Model. 9.9 Special Cases. 9.10 Special Notes and Comments. Appendix A.9.1 Hypothetical Data Set. 10 Factor Analysis and Latent Variables Models. 10.1 Introduction. 10.2 The Basic Concept of Factor Analysis. 10.3 The First-Level Latent Variables. 10.4 Illustrations Based on Hamsal s (2006) Data Set. 10.5 Selected Cases Based on Ary Suta s (2006) Data Set. 10.6 Evaluation Analysis Based on Latent Variables. 11 Application of the Stepwise Selection Methods. 11.1 Introduction. 11.2 The Options for the Stepwise Selection Methods. 11.3 Selection Method for the Numerical Variable Regression Models. 11.4 Multifactorial Stepwise Regression Models. 11.5 Illustrative Stepwise Regressions Based on Mlogit.wf1. 11.6 Special Notes and Comments. 12 Censored Multiple Regression Models. 12.1 Introduction. 12.2 Tobit Models. 12.3 General Tobit Model. 12.4 Zero One Indicator of Censoring. 12.5 Illustrative Cases of Censored Observations. 12.6 Series for a Censoring Variable. 12.7 Switching Censored Regressions. 12.8 Special Notes and Comments. Appendix A.12.1 Hypothetical Censored Data, Modified from Faad s (2008) Data Set. References. Index.