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How to write up multiple regression

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Sign in. Linear regression , while a useful tool, has significant limits. It can only be used to make predictions that fit within the range of the training data set. And, most importantly for this article, it can only be fit to data sets with a single dependent variable and a single independent variable. This is where multiple regression c o mes in. The general form of the equation for linear regression is:.
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Handbook of Biological Statistics

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Multiple regression - Handbook of Biological Statistics

Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable or sometimes, the outcome, target or criterion variable. The variables we are using to predict the value of the dependent variable are called the independent variables or sometimes, the predictor, explanatory or regressor variables. For example, you could use multiple regression to understand whether exam performance can be predicted based on revision time, test anxiety, lecture attendance and gender. Alternately, you could use multiple regression to understand whether daily cigarette consumption can be predicted based on smoking duration, age when started smoking, smoker type, income and gender.
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Topic 1: Multiple regression: Revision/Introduction

To create this article, 9 people, some anonymous, worked to edit and improve it over time. The wikiHow Tech Team also followed the article's instructions and verified that they work. This article has been viewed , times. Learn more
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This article discusses the basics of linear regression and its implementation in Python programming language. Linear regression is a statistical method for modelling relationship between a dependent variable with a given set of independent variables. Note: In this article, we refer dependent variables as response and independent variables as features for simplicity. In order to provide a basic understanding of linear regression, we start with the most basic version of linear regression, i. Simple linear regression.
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