Note that linearity is a strong assumption in linear regression in the sense that it tests and quantifies whether the two variables are linearly dependent. Simple linear regression allows to evaluate the existence of a linear relationship between two variables and to quantify this link. the other variable is the explanatory or also called independent variable, and is represented on the \(x\)-axis.It is also called dependent variable, and is represented on the \(y\)-axis one of the variable is considered the response or the variable to be explained.Simple linear regression is an asymmetric procedure in which: I will then conclude the article by presenting more advanced topics directly linked to linear regression. I will also show, in the context of multiple linear regression, how to interpret the output and discuss about its conditions of application. So after a reminder about the principle and the interpretations that can be drawn from a simple linear regression, I will illustrate how to perform multiple linear regression in R. However, I cannot afford to write about multiple linear regression without first presenting simple linear regression. Multiple linear regression being such a powerful statistical tool, I would like to present it so that everyone understands it, and perhaps even use it when deemed necessary. With data collection becoming easier, more variables can be included and taken into account when analyzing data.Multiple linear regression allows to evaluate the relationship between two variables, while controlling for the effect (i.e., removing the effect) of other variables.In the real world, multiple linear regression is used more frequently than simple linear regression. Multiple linear regression is a generalization of simple linear regression, in the sense that this approach makes it possible to evaluate the linear relationships between a response variable (quantitative) and several explanatory variables (quantitative or qualitative).More precisely, it enables the relationship to be quantified and its significance to be evaluated. Simple linear regression is a statistical approach that allows to assess the linear relationship between two quantitative variables.There are two types of linear regression: 1 The most common statistical tool to describe and evaluate the link between variables is linear regression. The last branch of statistics is about modeling the relationship between two or more variables. Inferential statistics (with the popular hypothesis tests and confidence intervals) is another branch of statistics that allows to make inferences, that is, to draw conclusions about a population based on a sample. Remember that descriptive statistics is a branch of statistics that allows to describe your data at hand.
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