Linear regression models
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Linear regression models. Simple Linear Regression. History. Developed by Sir Francis Galton (1822-1911) in his article “Regression towards mediocrity in hereditary structure”. Purposes:.

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Linear regression models

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Linear regression models

Linear regression models


Simple linear regression

Simple Linear Regression


History

History

  • Developed by Sir Francis Galton (1822-1911) in his article “Regression towards mediocrity in hereditary structure”


Purposes

Purposes:

  • To describe the linear relationship between two continuous variables, the response variable (y-axis) and a single predictor variable (x-axis)

  • To determine how much of the variation in Y can be explained by the linear relationship with X and how much of this relationship remains unexplained

  • To predict new values of Y from new values of X


The linear regression model is

The linear regression model is:

  • Xi and Yi are paired observations (i = 1 to n)

  • β0= population intercept (when Xi =0)

  • β1= population slope (measures the change in Yi per unit change in Xi)

  • εi= the random or unexplained error associated with the i th observation. The εi are assumed to be independent and distributed as N(0, σ2).


Linear relationship

Linear relationship

Y

ß1

1.0

ß0

X


Linear models approximate non linear functions over a limited domain

Linear models approximate non-linear functions over a limited domain

extrapolation

extrapolation

interpolation


Linear regression models

  • For a given value of X, the sampled Y values are independent with normally distributed errors:

Yi = βo + β1*Xi+ εi

ε ~ N(0,σ2)  E(εi) = 0

E(Yi ) = βo + β1*Xi

Y

E(Y2)

E(Y1)

X

X1

X2


Linear regression models

Fitting data to a linear model:

Yi

Yi – Ŷi = εi (residual)

Ŷi

Xi


The residual

The residual

The residual sum of squares


Estimating regression parameters

Estimating Regression Parameters

  • The “best fit” estimates for the regression population parameters (β0 and β1) are the values that minimize the residual sum of squares (SSresidual) between each observed value and the predicted value of the model:


Linear regression models

Sum of squares

Sum of cross products


Least squares parameter estimates

Least-squares parameter estimates

where


Linear regression models

Sample variance of X:

Sample covariance:


Solving for the intercept

Solving for the intercept:

Thus, our estimated regression equation is:


Hypothesis tests with regression

Hypothesis Tests with Regression

  • Null hypothesis is that there is no linear relationship between X and Y:

    H0: β1 = 0  Yi = β0 + εi

    HA: β1 ≠ 0  Yi = β0 + β1 Xi + εi

  • We can use an F-ratio (i.e., the ratio of variances) to test these hypotheses


Variance of the error of regression

Variance of the error of regression:

NOTE: this is also referred to as residual variance, mean squared error (MSE) or residual mean square (MSresidual)


Mean square of regression

Mean square of regression:

The F-ratio is: (MSRegression)/(MSResidual)

This ratio follows the F-distribution with (1, n-2) degrees of freedom


Variance components and coefficient of determination

Variance components and Coefficient of determination


Coefficient of determination

Coefficient of determination


Anova table for regression

ANOVA table for regression


Product moment correlation coefficient

Product-moment correlation coefficient


Parametric confidence intervals

Parametric Confidence Intervals

  • If we assume our parameter of interest has a particular sampling distribution and we have estimated its expected value and variance, we can construct a confidence interval for a given percentile.

  • Example: if we assume Y is a normal random variable with unknown mean μ and variance σ2, then is distributed as a standard normal variable. But, since we don’t know σ, we must divide by the standard error instead: , giving us a t-distribution with (n-1) degrees of freedom.

  • The 100(1-α)% confidence interval for μ is then given by:

  • IMPORTANT: this does not mean “There is a 100(1-α)% chance that the true population mean μ occurs inside this interval.” It means that if we were to repeatedly sample the population in the same way, 100(1-α)% of the confidence intervals would contain the true population mean μ.


Publication form of anova table for regression

Publication form of ANOVA table for regression


Variance of estimated intercept

Variance of estimated intercept


Variance of the slope estimator

Variance of the slope estimator


Variance of the fitted value

Variance of the fitted value


Variance of the predicted value

Variance of the predicted value (Ỹ):


Regression

Regression


Assumptions of regression

Assumptions of regression

  • The linear model correctly describes the functional relationship between X and Y

  • The X variable is measured without error

  • For a given value of X, the sampled Y values are independent with normally distributed errors

  • Variances are constant along the regression line


Residual plot for species area relationship

Residual plot for species-area relationship


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