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What Does “No Opinion” Mean in the HINTS?

What Does “No Opinion” Mean in the HINTS?

What Does “No Opinion” Mean in the HINTS?. Michael P. Massagli, Ph.D. K. Vish Viswanath, Ph.D. Dana-Farber Cancer Institute. “There has been little formal research on the use of knowledge questions” (in surveys) – Sudman and Bradburn, 1982:117. Common Practices (e.g. Fowler, 1995: 67-69)

By lotus
(289 views)

Curriculum Learning for Latent Structural SVM

Curriculum Learning for Latent Structural SVM

Curriculum Learning for Latent Structural SVM. (under submission). M. Pawan Kumar. Benjamin Packer. Daphne Koller. Aim. To learn accurate parameters for latent structural SVM. Input x. Output y  Y. Hidden Variable h  H. “Deer”.

By oke
(460 views)

General Methods for Missing Data

General Methods for Missing Data

General Methods for Missing Data. John M. Abowd March 2005. Outline. General principles Missing at random Weighting procedures Imputation procedures Hot decks Introduction to model-based procedures. General Principles.

By vonda
(218 views)

Effect of Increased Copayments on Pharmacy Use in the Department of Veterans Affairs

Effect of Increased Copayments on Pharmacy Use in the Department of Veterans Affairs

Effect of Increased Copayments on Pharmacy Use in the Department of Veterans Affairs. Kevin T. Stroupe, PhD 1,2,3,4 1 Midwest Center for Health Services & Policy Research, Hines VA Hospital, Hines, IL 2 Cooperative Studies Program Coordinating Center, Hines VA Hospital, Hines, IL

By jayme
(126 views)

Using New Measures of Fatness to Improve Estimates of Early Retirement and Entry onto the OASI Rolls

Using New Measures of Fatness to Improve Estimates of Early Retirement and Entry onto the OASI Rolls

Using New Measures of Fatness to Improve Estimates of Early Retirement and Entry onto the OASI Rolls. Richard V. Burkhauser John C. Cawley. Research Question. Our research question: Is there a causal relationship between fatness and taking Old-Age benefits at age 62?

By ailish
(124 views)

Text Classification from Labeled and Unlabeled Documents using EM

Text Classification from Labeled and Unlabeled Documents using EM

Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning (2000). Kamal Nigam Andrew K. McCallum Sebastian Thrun Tom Mitchell. Presented by Andrew Smith, May 12, 2003. Presentation Outline. Motivation and Background The Naive Bayes classifier

By sanura
(236 views)

The Pros and Cons of using SPSS as a Research Tool to explore Individual D ifferences

The Pros and Cons of using SPSS as a Research Tool to explore Individual D ifferences

The Pros and Cons of using SPSS as a Research Tool to explore Individual D ifferences. SPSS User Group Meeting York October 2011 Sophie von Stumm, University of Edinburgh. Aims of Individual Differences. A research area of psychology that aims to

By duyen
(1209 views)

Multiple Regression

Multiple Regression

Multiple Regression. Farrokh Alemi, Ph.D. Kashif Haqqi M.D. Additional Reading. For additional reading see Chapter 15 and Chapter 14 in Michael R. Middleton’s Data Analysis Using Excel, Duxbury Thompson Publishers, 2000.

By lesley
(195 views)

Unsupervised learning: Clustering

Unsupervised learning: Clustering

Unsupervised learning: Clustering. Ata Kaban The University of Birmingham http://www.cs.bham.ac.uk/~axk. The Clustering Problem. Unsupervised Learning. Data (input). ‘Interesting structure’ (output). Should contain essential traits discard unessential details

By diedrick
(167 views)

Objective vs. Perceived Air-pollution as a Factor of Housing Pricing: A Case Study of the Greater Haifa Metropolitan Are

Objective vs. Perceived Air-pollution as a Factor of Housing Pricing: A Case Study of the Greater Haifa Metropolitan Are

Objective vs. Perceived Air-pollution as a Factor of Housing Pricing: A Case Study of the Greater Haifa Metropolitan Area. Boris A. Portnov Graduate School of Management, University of Haifa, Israel (e-mail: Portnov@nrem.haifa.ac.il).

By milly
(134 views)

CRI (Course Ranking Index) January, 2011

CRI (Course Ranking Index) January, 2011

CRI (Course Ranking Index) January, 2011. Value of a Course . The value of a course can be measured in different ways. For example: FTES Productivity Core mission (Basic skills, CTE, Transfer) Degree or Certificate Applicable. Combining Measures.

By cree
(70 views)

Biostat 215 Discussion #1

Biostat 215 Discussion #1

Biostat 215 Discussion #1. Thomas B. Newman, MD, MPH with thanks to Gabriel Escobar, MD; Michael Kuzniewicz, MD, MPH, Chuck, Eric and Steve). Outline. Background about jaundice and phototherapy Discussion/Review of some key topics Potential and counterfactual outcomes

By cyrah
(144 views)

Multiple Linear Regression and the General Linear Model

Multiple Linear Regression and the General Linear Model

Multiple Linear Regression and the General Linear Model. With Thanks to My Students in AMS 572: Data Analysis. Outline. 1. Introduction to Multiple Linear Regression 2. Statistical Inference 3. Topics in Regression Modeling 4. Example 5. Variable Selection Methods

By nili
(331 views)

Introduction to Regression Lecture 4.2

Introduction to Regression Lecture 4.2

Introduction to Regression Lecture 4.2. Indicator variables for estimating seasonal effects in time series another application, Meter Sales analysis Correlated explanatory variables. Housing Completions case study. Table 1.7 Completions and Quarterly Indicators. Model formulation.

By bruno-mclean
(156 views)

Lecture 22 – Thurs., Nov. 25

Lecture 22 – Thurs., Nov. 25

Lecture 22 – Thurs., Nov. 25. Nominal explanatory variables (Chapter 9.3) Inference for multiple regression (Chapter 10.1-10.2). Nominal Variables. To incorporate nominal variables in multiple regression analysis, we use indicator variables.

By zena-contreras
(84 views)

Chapter 7 Using Indicator and Interaction Variables

Chapter 7 Using Indicator and Interaction Variables

Chapter 7 Using Indicator and Interaction Variables. Terry Dielman Applied Regression Analysis: A Second Course in Business and Economic Statistics, fourth edition. 7.1 Using and Interpreting Indicator Variables.

By dennis-walters
(219 views)

CHAPTER

CHAPTER

6. x. ECONOMETRICS. x. CHAPTER. x. x. x. Dummy Variable Regression Models. Dummy, or indicator, variables take on values of 0 or 1 to indicate the presence or absence of a quality. They can be included in regressions just like quantitative variables.

By nicodemus-illias
(120 views)

Quantitative Business Analysis for Decision Making

Quantitative Business Analysis for Decision Making

Quantitative Business Analysis for Decision Making. Multiple Linear Regression Analysis. Outlines. Multiple Regression Model Estimation Testing Significance of Predictors Multicollinearity Selection of Predictors Diagnostic Plots. Multiple Regression Model.

By aladdin-francis
(85 views)

Multiple Regression Models: Interactions and Indicator Variables

Multiple Regression Models: Interactions and Indicator Variables

Multiple Regression Models: Interactions and Indicator Variables. Today’s Data Set.

By luke-rivera
(88 views)

The 2011 Pobal HP Deprivation Index for Small Areas (SA) Statistical Features

The 2011 Pobal HP Deprivation Index for Small Areas (SA) Statistical Features

Dublin, August 20 12. The 2011 Pobal HP Deprivation Index for Small Areas (SA) Statistical Features. Introduction to The 2011 Pobal HP Deprivation Index.

By ciara-allison
(128 views)

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