Index of /econometrics_minicourse_2015.

Cross section - Simultaneous equations. Time Series. Variable selection methods. Difference in differences. Regression toward the mean. In statistics, regression toward (or to) the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement—and, paradoxically, if it is extreme on its second measurement, it will tend to have been closer to the average on its first.[1][2][3] To avoid making incorrect inferences, regression toward the mean must be considered when designing scientific experiments and interpreting data.[4] The conditions under which regression toward the mean occurs depend on the way the term is mathematically defined.

Sir Francis Galton first observed the phenomenon in the context of simple linear regression of data points. However, a less restrictive approach is possible. Regression towards the mean can be defined for any bivariate distribution with identical marginal distributions. Two such definitions exist.[5] One definition accords closely with the common usage of the term “regression towards the mean”. History[edit] Cluster Sampling. Confidence vs prediction intervals for regression. COMMON MISTEAKS MISTAKES IN USING STATISTICS: Spotting and Avoiding Them Introduction Types of Mistakes Suggestions Resources Table of Contents About For example, if the model assumption is that E(Y|X=x) = α +βx, then least squares regression will produce an equation of the form y = a +bx, where a is an estimate of the true value α and b is an estimate of the true value β.

Thus for a particular value of x, Sample selection bias. Interpret Regression Coefficient Estimates - {level-level, log-level, level-log & log-log regression} - Curtis Kephart. Deterministic vs probabilistic. Errors and residuals in statistics. Introduction[edit] A statistical error (or disturbance) is the amount by which an observation differs from its expected value, the latter being based on the whole population from which the statistical unit was chosen randomly.

For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically unobservable, and hence the statistical error cannot be observed either. The difference between prediction intervals and confidence intervals. Prediction intervals and confidence intervals are not the same thing.

Unfortunately the terms are often confused, and I am often frequently correcting the error in students’ papers and articles I am reviewing or editing. FAQ: How do I interpret odds ratios in logistic regression? FAQ: How do I interpret odds ratios in logistic regression?

Introduction. Partial and Semipartial Correlation. Partial and Semipartial Correlation Give a concrete example (names of variables, context) in which it makes sense to compute a partial correlation.

Why a partial rather than a semipartial? Give a concrete example (names of variables, context) in which it makes sense to compute a semipartial correlation. Econometrics Links. Econometric Links Econometrics Journal. Bertrand Russell’s Inductivist Turkey. A turkey, in an american nurture, decide to shape its vision of the world scientifically well founded (a wissenschaftliche Weltauffassung, according to the Logical Positivism by the Wiener Kreis).

Bertrand Russell – from Wikipedia The turkey found that, on his first morning at the turkey farm, he was fed at 9 a.m. Being a good inductivist turkey he did not jump to conclusions. He waited until he collected a large number of observations that he was fed at 9 a.m. and made these observations under a wide range of circumstances, on Wednesdays, on Thursdays, on cold days, on warm days. Each day he added another observation statement to his list. However on the morning of Christmas eve he was not fed but instead had his throat cut. It doesn’t matter how many cases we list during our inductivist reasoning, nothing guarantees that the next case will lay in this inference we deducted from our observations, as the possible experiments and observations are infinite by number and type.

Regression with Stata Web Book: Chapter 2 - Regression Diagnostics. Stata Web Books Regression with Stata Chapter 2 - Regression Diagnostics Chapter Outline 2.0 Regression Diagnostics 2.1 Unusual and Influential data 2.2 Checking Normality of Residuals 2.3 Checking Homoscedasticity 2.4 Checking for Multicollinearity 2.5 Checking Linearity 2.6 Model Specification 2.7 Issues of Independence 2.8 Summary 2.9 Self assessment 2.10 For more information.

Ben Lambert. Correlation. The best illustration you'll see that correlation doesn't equal causation. "Correlation doesn't equal causation.

" You've heard it in statistics class, as a caveat in a million blog posts writing up data or a study (including some of mine), as a critique of those studies, and, naturally, as the premise for an XKCD cartoon. But I've rarely seen the point made as vividly as it was by Tyler Vigen, a law student at Harvard who, in his spare time, put together a website that finds very, very high correlations between things that are absolutely not related, like margarine consumption and the divorce rate in Maine: Spurious Correlations. Spurious Correlations Ó 1996, 1997 by William C.

Burns The analysis of human resources data typically involves the use of computer databases that were constructed to process transactions. Their purpose normally centers on administration and recordkeeping.