Almost all available statistical packages are capable of performing Analysis of Variance (ANOVA) from raw data. Some of statistical packages have capability to perform independent sample t-test, and some other tests of significance on summary data, but seldom would you come across a software...

We consider a non-parametric model for estimating the effect of a binary treatment on an outcome variable while adjusting for an observed covariate. A naive procedure consists in performing two separate non-parametric regression of the response on the covariate: one with the treated individuals...

Introduction to empirical data analysis -- Regression analysis -- Analysis of variance -- Discriminant analysis -- Logistic regression -- Contingency analysis -- Factor analysis -- Cluster analysis -- Conjoint analysis.

This chapter discusses the formal and informal techniques that are commonly used to give quantitative answers in the field of distributional analysis. To this end, it covers subjects including inequality, poverty, and the modeling of income distributions. It also deals with parametric and...

What is Statistics? Opinions vary. In fact, there is a continuous spectrum of attitudes toward statistics ranging from pure theoreticians, proving asymptotic efficiency and searching for most powerful tests, to wild practitioners, blindly reporting p-values and claiming statistical significance...

Let Y (formula) denote the nth (upper) k-record value of an infinite sequence of independent, identically distributed random variables with common continuous distribution function F. We show that if the nthk-record value Y (formula) has an increasing failure rate (IFR), then Y (formula) and Y...

As I document using evidence from a journal data repository that I manage, the datasets used in empirical work are getting larger. When we use very large datasets, it can be dangerous to rely on standard methods for statistical inference. In addition, we need to worry about computational issues....

This paper introduces identification-robust subvector tests and confidence sets (CS's) that have asymptotic size equal to their nominal size and are asymptotically efficient under strong identification. Hence, inference is as good asymptotically as standard methods under standard regularity...