Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU.

Slides:



Advertisements
Similar presentations
Eco 5385 Predictive Analytics For Economists Spring 2014 Professor Tom Fomby Director, Richard B. Johnson Center for Economic Studies Department of Economics.
Advertisements

CORRELATIONAL RESEARCH o What are the Uses of Correlational Research?What are the Uses of Correlational Research? o What are the Requirements for Correlational.
All Possible Regressions and Statistics for Comparing Models
Chapter 5 Multiple Linear Regression
Kin 304 Regression Linear Regression Least Sum of Squares
12-1 Multiple Linear Regression Models Introduction Many applications of regression analysis involve situations in which there are more than.
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
12 Multiple Linear Regression CHAPTER OUTLINE
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc. Chapter 13 Nonlinear and Multiple Regression.
Multiple Linear Regression
© McGraw-Hill Higher Education. All Rights Reserved. Chapter 2F Statistical Tools in Evaluation.
MULTIPLE REGRESSION. OVERVIEW What Makes it Multiple? What Makes it Multiple? Additional Assumptions Additional Assumptions Methods of Entering Variables.
Psychology 202b Advanced Psychological Statistics, II February 22, 2011.
Part I – MULTIVARIATE ANALYSIS C3 Multiple Linear Regression II © Angel A. Juan & Carles Serrat - UPC 2007/2008.
Predicting the Semantic Orientation of Adjective Vasileios Hatzivassiloglou and Kathleen R. McKeown Presented By Yash Satsangi.
Multiple Linear Regression Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.
1 Chapter 9 Variable Selection and Model building Ray-Bing Chen Institute of Statistics National University of Kaohsiung.
1 Chapter 1 Introduction Ray-Bing Chen Institute of Statistics National University of Kaohsiung.
Chapter 6 (cont.) Regression Estimation. Simple Linear Regression: review of least squares procedure 2.
Module 32: Multiple Regression This module reviews simple linear regression and then discusses multiple regression. The next module contains several examples.
Inference for regression - Simple linear regression
Elements of Multiple Regression Analysis: Two Independent Variables Yong Sept
Regression Method.
ACCT: 742-Advanced Auditing
Shonda Kuiper Grinnell College. Statistical techniques taught in introductory statistics courses typically have one response variable and one explanatory.
Chapter Fourteen Statistical Analysis Procedures Statistical procedures that simultaneously analyze multiple measurements on each individual or.
Outline 1-D regression Least-squares Regression Non-iterative Least-squares Regression Basis Functions Overfitting Validation 2.
Then click the box for Normal probability plot. In the box labeled Standardized Residual Plots, first click the checkbox for Histogram, Multiple Linear.
Look-ahead Linear Regression Trees (LLRT)
Chapter 7 Relationships Among Variables What Correlational Research Investigates Understanding the Nature of Correlation Positive Correlation Negative.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
Lesson Multiple Regression Models. Objectives Obtain the correlation matrix Use technology to find a multiple regression equation Interpret the.
Practical Statistics Regression. There are six statistics that will answer 90% of all questions! 1. Descriptive 2. Chi-square 3. Z-tests 4. Comparison.
Chapter 4: Introduction to Predictive Modeling: Regressions
CROSS-VALIDATION AND MODEL SELECTION Many Slides are from: Dr. Thomas Jensen -Expedia.com and Prof. Olga Veksler - CS Learning and Computer Vision.
Xuhua Xia Stepwise Regression Y may depend on many independent variables How to find a subset of X’s that best predict Y? There are several criteria (e.g.,
Analysis of Covariance Combines linear regression and ANOVA Can be used to compare g treatments, after controlling for quantitative factor believed to.
Chapter 6 (cont.) Difference Estimation. Recall the Regression Estimation Procedure 2.
Stats 845 Applied Statistics. This Course will cover: 1.Regression –Non Linear Regression –Multiple Regression 2.Analysis of Variance and Experimental.
1 Experimental Statistics - week 12 Chapter 12: Multiple Regression Chapter 13: Variable Selection Model Checking.
Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Variable Selection 1 Chapter 8 Variable Selection Terry Dielman Applied Regression Analysis:
Reservoir Uncertainty Assessment Using Machine Learning Techniques Authors: Jincong He Department of Energy Resources Engineering AbstractIntroduction.
1 Chapter 4: Introduction to Predictive Modeling: Regressions 4.1 Introduction 4.2 Selecting Regression Inputs 4.3 Optimizing Regression Complexity 4.4.
Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.
© Galit Shmueli and Peter Bruce 2010 Chapter 6: Multiple Linear Regression Data Mining for Business Analytics Shmueli, Patel & Bruce.
Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU.
Using SPSS Note: The use of another statistical package such as Minitab is similar to using SPSS.
Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU.
Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU.
Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU.
Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU.
Data Mining Lectures Lecture 7: Regression Padhraic Smyth, UC Irvine ICS 278: Data Mining Lecture 7: Regression Algorithms Padhraic Smyth Department of.
2011 Data Mining Industrial & Information Systems Engineering Pilsung Kang Industrial & Information Systems Engineering Seoul National University of Science.
Canadian Bioinformatics Workshops
DEMONSTRATING TOOLS FOR SUPPORTING PROGRAMS ALONG DIFFERENT STAGES OF THE EVALUATION LIFE CYCLE JBS International 10 year whole school initiative.
Eco 6380 Predictive Analytics For Economists Spring 2016
Kin 304 Regression Linear Regression Least Sum of Squares
BPK 304W Regression Linear Regression Least Sum of Squares
BPK 304W Correlation.
S519: Evaluation of Information Systems
بحث في التحليل الاحصائي SPSS بعنوان :
Eco 6380 Predictive Analytics For Economists Spring 2016
Eco 6380 Predictive Analytics For Economists Spring 2014
Eco 6380 Predictive Analytics For Economists Spring 2016
Eco 6380 Predictive Analytics For Economists Spring 2016
Linear Model Selection and regularization
How many groups are there?
Solving a System of Linear Equations
Business Statistics - QBM117
Presentation transcript:

Eco 6380 Predictive Analytics For Economists Spring 2016 Professor Tom Fomby Department of Economics SMU

Presentation 5 Building a Predictive Regression Model using Data Mining Techniques (Backward, Forward, Stepwise, All Regressions, Cp Statistic) Chapter 6 in SPB

Building a Data Mining Model: Example with Linear Regression – The Boston Housing Data Set Selecting a Best Subset Regression Selection Methods: Forward, Backward, Stepwise, and All Subsets Methods. See the file “Multiple Linear Regression and Subset Selection.pdf” on the class website. The critical p-value as a “tuning parameter” Thus the best “architecture” of the regression equation is determined by the choice of best subset selection method as well as the choice of the p-value tuning parameter of the respective subset selection methods

Building a Model: Example with Linear Regression – The Boston Housing Data Set continued Demonstrate the danger of over-fitting a multiple regression equation by running the SAS simulation program “Cross Validation.sas” The Validation data set reveals the true bogus nature of “spurious” regressions

Evaluation of Predictive Performance I.Various Measures of Predictive Accuracy II.Comparisons of Predictive Accuracies across competing prediction models in the validation data set III.Is the Predictive Accuracy of one method statistically better than the Predictive Accuracy of a competing method?

Now go directly to the file “Scoring Measures for Prediction Problems.pdf” on the class website

Building a Model: Example with Linear Regression – The Boston Housing Data Set continued Rule of Thumb Adjustment of p-values in linear regressions arising from multiple testing vis-à-vis subset selection methods. See the paper on Data Mining by Michael C. Lovell I have posted on the class website in the file Data Mining_Lovell.pdf and, in particular, equation (3).

Building a Model: Example with Linear Regression – The Boston Housing Data Set continued Demonstrate the Best Subset Selection Methods on the Boston Housing Data Set using the SAS program “Boston Best Subset Partitioned.sas” that can be found on the course website Demonstrate Best Subset Selection Procedure in SPSS Modeler

Classroom Exercise: Exercise 3