# Glossary of statistical terms

Regression Analysis:

Regression analysis provides a "best-fit" mathematical equation for the relationship between the dependent variable (response) and independent variable(s) (covariates).

There are two major classes of regression - parametric and non-parametric. Parametric regression requires choice of the regression equation with one or a greater number of unknown parameters. Linear regression, in which a linear relationship between the dependent variable and independent variables is posited, is an example. The aim of parametric regression is to find the values of these parameters which provide the best fit to the data. The number of parameters is usually much smaller than the number of data points. In contrast, the <non-parametric regression> requires no such a choice of the regression equation.

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Statistics 2 – Inference and Association

The aim of this course is to provide an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach. Once you have completed this course you will be able to test hypotheses and compute confidence intervals regarding proportions or means, computer correlations and fit simple linear regressions.

Statistics 3 – ANOVA and Regression

This course provides an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

Regression Analysis

In this course you will learn how multiple linear regression models are derived, use software to implement them, learn what assumptions underlie the models, learn how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

Introduction to Statistical Modeling

This course provides a solid introduction to the ideas and techniques of statistical modeling.

Survey Analysis

This course covers the analysis of data gathered in surveys.

Prediction & Tolerance Intervals; Measurement and Reliability

The topics covered in this course include prediction intervals, tolerance intervals, calibration intervals, measurement error, accelerated life testing, measurement system appraisal, reliability and lifetime testing.

Decision Trees and Rule-Based Segmentation

Rule induction is an important component of data mining, and this course covers two main styles of generating rules.

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