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Statistical Power Analysis for the Behavioral Sciences
Chapter 10. Set Correlation and Multivariate Methods 10.1. Introduction and Use 10.2. The Effect Size Index: f2 10.3. Determining the Power 10.4. Determining Sample Size Chapter 11. Some Issues in Power Analysis 11.1. Introduction 11.2. Effect Size 11.3. Reliability 11.4. "Qualifying" Dependent Variables Chapter 12. Computational Procedures ix ...
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Chapter 4 Introduction to Categorical Data Analysis Procedures
utstat.toronto.eduIntroduction to Multivariate Procedures, and PROC TRANSREG is summarized in Chapter 2, Introduction to Regression Procedures. A categorical variable is dened as …
SAS Example 4: Instrumental variables - University of Toronto
utstat.toronto.eduInstrumental variables on credit card data (Card1) 11 With house re-scaled (Express in tens of thousands) The CALIS Procedure Covariance Structure Analysis: Maximum Likelihood Estimation
Expected Value, Variance and Covariance
utstat.toronto.eduExpected ValueVarianceCovariance Conditional Expectation The idea Consider jointly distributed random variables Xand Y. For each possible value of X, there is a conditional distribution of Y. Each conditional distribution has an expected value (sub-population mean). If you could estimate E(YjX= x), it would be a good way to predict Y from X.
Multinomial Logit Models with R - University of Toronto
utstat.toronto.edu> # Estimate probabilities for a student with HSGPA = 90 > betahat1 Gone:(intercept) Passed:(intercept) Gone:hsgpa Passed:hsgpa 1.90422575 -13.39305637 -0.01881621 0.18643711
Stepwise Logistic Regression with R
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Sampling from a Finite Population: Interval Estimation of ...
utstat.toronto.educlaim to have Internet access at home. (b) Give a 95% confidence interval for the true percentage of students who claim ... from stratum to stratum. For example, if you were interested in estimating the average hours of sports watched by students on a campus, it would be natural to stratify by sex. ...
Expected Value, Variance and Covariance - utstat.toronto.edu
utstat.toronto.eduExpected value is an average Imagine a very large jar full of balls. This is the population. The balls are numbered x 1;:::;x N. These are measurements carried out on members of the population. Suppose for now that all the numbers are di erent. A ball is selected at random; all balls are equally likely to be chosen. Let Xbe the number on the ...
Credit Scoring via Logistic RegressionI
utstat.toronto.eduLogistic regression is used in a wide variety of applications including biomedical studies, social science research, marketing as well as nancial applications. One example of the latter is the use of binary logistic regression models for credit-scoring, that is: modeling the probability that a …
Stepwise Logistic Regression with R
utstat.toronto.eduStepwise Logistic Regression with R ... = 2k + Deviance, where k = number of parameters Small numbers are better Penalizes models with lots of parameters Penalizes models with poor fit > fullmod = glm(low ~ age+lwt+racefac+smoke+ptl+ht+ui+ftv,family=binomial) ... > # Here was the chosen model from earlier > redmod1 = glm(low ~ lwt+racefac ...
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www.utstat.toronto.edudistribution. The multivariate change of variable is developed in an Advanced section. Chapter 3 introduces expectation. The probability-generating function is dis-cussed, as are the moments and the moment-generating function of a random variable. This chapter develops some of the major inequalities used in probability. A section on