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Is a mathematical equation that allows us to describe the relationship between two variables.

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Describes the range or strength with which a relationship occurs.

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Is a measure of dispersion, which gives negative or positive results.

Is symbolised by:

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It is a Cartesian plane where as many points as pairs of observations are represented, each point corresponding to a pair of observations.

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The mathematical model describing a linear relationship, when the value of Y and X is estimated, is represented by:

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It is an estimation method, the quantities to be estimated are determined by minimising the sum of the squares of the differences between the observed values (Y), and the estimated values (y¡) ̂

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Procedure for calculating the regression coefficients b and c 

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Procedure for calculating the regression coefficients b and c 

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The solution to two equations is given

For the first one, each term is put with the summation sign.

For the second equation, multiply the general equation by X, and then add the summation sign

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Predicting or estimating the value of Y requires that the value of X is a value that lies within the range of that variable and not a value that is above or below the limits of the population range.

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Residual variance and standard error of estimation

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Residual variance and standard error of estimation

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These are the names used to identify this measure of dispersion, it allows to clarify the degree of variation or dispersion of the set or observations that were not explained by the regression line.

Several formulas are used:

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It is an expression given to the standard deviation of the observed values with respect to a regression line.

The square root of the residual variance is defined:

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For an estimated value (Y), it consists of setting two points for the estimator, within which the population parameter or statistical value must be contained, with a certain degree of confidence, to be set by the statistical analyst.

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It is a measure that allows us to calculate the degree of dispersion of those values that were explained by the regression line.

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It is a measure of the interpretation of two random variables. It indicates the percentage of the variations of the dependent variable attributable to the influence of the independent variable.