'Explanatory variable' presentation slideshows

965 views • 82 slides

Causal Inference for Complex Observational Data Using Stata

Causal Inference for Complex Observational Data Using Stata. Chuck Huber StataCorp chuber@stata.com. ERMs Outline. Description of the dataset Unobserved confounding and endogeneity Nonrandom treatment assignment Missing not at random (MNAR) and selection bias Treatment effects.

1.16k views • 72 slides

Laboratory & professional skills for Bioscientists term 3 : Data Analysis in R

Laboratory & professional skills for Bioscientists term 3 : Data Analysis in R. Revision: Overview and Developing understanding of term 2 statistics. Module Learning Outcomes. The successful student will be able to: Explain the purpose of data analysis

764 views • 38 slides

BINARY CHOICE MODELS: LOGIT ANALYSIS

BINARY CHOICE MODELS: LOGIT ANALYSIS. Y, p. A. 1. 1 - b 1 - b 2 X i. b 1 + b 2 X i. b 1. b 1 + b 2 X i. B. 0. X i. X. The linear probability model may make the nonsense predictions that an event will occur with probability greater than 1 or less than 0. 1.

778 views • 37 slides

Chapter 2: Types of Studies

758 views • 28 slides

Quality and Standards Matter: Implementing Effective Quality Systems to Support Trade and Competitiveness Initiatives

446 views • 27 slides

25 March 2004

Capital flows, exchange control regulations and exchange rate policy: the South African experience. 25 March 2004. 1 Introduction. The relationship between capital flows, exchange control regulations and exchange rate arrangements has long occupied policymakers

390 views • 27 slides

Chapter 4

Chapter 4. Scatterplots and Correlation. Explanatory and Response Variables. Interested in studying the relationship between two variables by measuring both variables on the same individuals. a response variable measures an outcome of a study

510 views • 24 slides

Valuation 4: Econometrics

Valuation 4: Econometrics. Why econometrics? What are the tasks? Specification and estimation Hypotheses testing Example study. Last week we looked at. What is so special about environmental goods? Theory of consumer demand for market goods

425 views • 24 slides

Regression Analysis

Regression Analysis. Introduction. Derive the ? and ? Assess the use of the T-statistic Discuss the importance of the Gauss-Markov assumptions Describe the problems associated with autocorrelation, how to measure it and possible remedies Introduce the problem of heteroskedasicity.

349 views • 22 slides

Lecture 3 (Chapter 4)

Lecture 3 (Chapter 4). Linear Models for Longitudinal Data. Linear Regression Model (Review) Ordinary Least Squares (OLS) Maximum Likelihood Estimation Distribution Theory. Linear Regression Model (A Review). Linear Regression Model (Review) Ordinary Least Squares (OLS)

369 views • 19 slides

3.3 Omitted Variable Bias

3.3 Omitted Variable Bias. -When a valid variable is excluded, we UNDERSPECIFY THE MODEL and OLS estimates are biased -Consider the true population model:. -Assume this satisfies all 4 assumptions and that we are concerned with x 1 -if we exclude x 2 , our estimation becomes:.

1.4k views • 18 slides

Stat 112: Lecture 20 Notes

Stat 112: Lecture 20 Notes. Chapter 7.2: Interaction Variables. Chapter 8: Model Building. I will e-mail Homework 6 by Friday. It will be due on Friday, Dec. 1 st (the Friday after Thanksgiving). Interaction.

360 views • 18 slides

Lecture 20 Preview: Omitted Variables and the Instrumental Variables Estimation Procedure

Lecture 20 Preview: Omitted Variables and the Instrumental Variables Estimation Procedure. Revisit Omitted Explanatory Variable Bias. Review of Our Previous Explanation of Omitted Explanatory Variable Bias.

708 views • 17 slides

Topic 2

430 views • 16 slides

Chapter 4

Chapter 4. Scatterplots and Correlation. Chapter outline. Explanatory and response variables Displaying relationships: Scatterplots Interpreting scatterplots Adding categorical variables to scatterplots Measuring linear association: correlation r Facts about correlation.

376 views • 15 slides

Investigating Relationships between Variables: Interpreting Scatterplots

Investigating Relationships between Variables: Interpreting Scatterplots. Examining Relationships. Is there a relationship between the number of minutes the 1992 Dream Team basketball player was on the court and the number of points he scored?

306 views • 14 slides

Review of Causal Analysis Guidelines for Theory Building

1.49k views • 9 slides

Does Association Imply Causation?

Does Association Imply Causation?. Sometimes, but not always! Look at example 2.42 on page 149 (section 2.6, Explaining Causation) for several x,y variables where association was found - some are causal, others are not.

738 views • 6 slides

Does Association Imply Causation?

Does Association Imply Causation?. Sometimes, but not always! Look at example 2.42 on page 149 (section 2.6, Explaining Causation) for several x,y variables where association was found - some are causal, others are not.

632 views • 6 slides