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Simple linear regression models the relationship between a dependent variable and one independent variables using a linear function. In statistics, they differentiate between a simple and multiple linear regression. The goal of a model is to get the smallest possible sum of squares and draw a line that comes closest to the data. Technically, a regression analysis model is based on the sum of squares, which is a mathematical way to find the dispersion of data points.
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Regression analysis helps you understand how the dependent variable changes when one of the independent variables varies and allows to mathematically determine which of those variables really has an impact. Independent variables (aka explanatory variables, or predictors) are the factors that might influence the dependent variable. In statistical modeling, regression analysis is used to estimate the relationships between two or more variables:ĭependent variable (aka criterion variable) is the main factor you are trying to understand and predict. Regression analysis in Excel - the basics
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If r is negative, then as one variable increases, the other tends to decrease.
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If r is positive, then as one variable increases, the other tends to increase. The sign of r corresponds to the direction of the relationship. The further away r is from zero, the stronger the linear relationship between the two variables. The Pearson correlation coefficient, r, can take on values between -1 and 1. A correlation analysis provides information on the strength and direction of the linear relationship between two variables, while a simple linear regression analysis estimates parameters in a linear equation that can be used to predict values of one variable based on the other. A correlation or simple linear regression analysis can determine if two numeric variables are significantly linearly related.