Probability and Statistics
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The mathematical study of the likelihood and probability of events occurring based on known information and inferred by taking a limited number of samples. Statistics plays an extremely important role in many aspects of economics and science, allowing educated guesses to be made with a minimum of expensive or difficult-to-obtain data. A joke told about statistics (or, more precisely, about statisticians), runs as follows.
Statistics
Next: Blackbox Model Selection Up: Autonomous Modeling Previous: Judging Model Quality by Cross validation is a model evaluation method that is better than residuals.
Cross Validation
In statistics, maximum-likelihood estimation ( MLE ) is a method of estimating the parameters of a statistical model .
Maximum likelihood
Poisson distribution
In probability theory and statistics , the Poisson distribution (pronounced [pwasɔ̃] ) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event. [ 1 ] The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.
What it does: The Independent Samples T Test compares the mean scores of two groups on a given variable.
Independent Samples T Test
Random assignment or random placement is an experimental technique for assigning subjects to different treatments (or no treatment).
Random assignment
In statistical inference , specifically predictive inference , a prediction interval is an estimate of an interval in which future observations will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis . Prediction intervals are used in both frequentist statistics and Bayesian statistics : a prediction interval bears the same relationship to a future observation that a frequentist confidence interval or Bayesian credible interval bears to an unobservable population parameter: prediction intervals predict the distribution of individual future points, whereas confidence intervals and credible intervals of parameters predict the distribution of estimates of the true population mean or other quantity of interest that cannot be observed.
Prediction interval
The distinction between confidence intervals, prediction intervals and tolerance intervals.
When you fit a parameter to a model, the accuracy or precision can be expressed as a confidence interval, a prediction interval or a tolerance interval. The three are quite distinct.
EXCEL 2007: Two-Variable Regression Using Data Analysis Add-in