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TRABAJO FINAL SOBRE EL MODELO DE LA REGRESIÓN MÚLTIPLE

MASTER:

ASIGNATURA:

NOMBRE DE LOS COMPONENTES DEL GRUPOS DE TRABAJO:

Leer el siguiente escrito, en el que se hacen unas reflexionessobre el Modelo de la regresión múltiple y se añade un ejemplo con datos hipotéticos sobre ‘satifacción vital’. Obsérvese que hay 13 variables y es posible tomar, como posibles variablesdependientes, una de las dos siguientes: LifeSat (puntuación del Invertario de Satisfacción 7 años después de abandonar el Collage) o Income (Ingresos, en miles, siete años después de dejar el Collage).Multiple Regression with Many Predictor Variables
The purpose of multiple regression is to predict a single variable from one or more independent variables. Multiple regression with many predictorvariables is an extension of linear regression with two predictor variables. A linear transformation of the X variables is done so that the sum of squared deviations of the observed and predicted Y is aminimum. The computations are more complex, however, because the interrelationships among all the variables must be taken into account in the weights assigned to the variables. The interpretation of theresults of a multiple regression analysis is also more complex for much the same reason.
The prediction of Y is accomplished by the following equation:
Y'i = b0 + b1X1i + b2X2i + … + bkXki
The "b"values are called regression weights and are computed in a way that minimizes the sum of squared deviations

in the same manner as in simple linear regression. In this case there are K predictorvariables rather than two and K + 1 regression weights must be estimated, one for each of the K predictor variable and one for the constant (b0) term.
EXAMPLE DATA
The data used to illustrate theinner workings of multiple regression will be generated from the "Example Student." The data are presented below:
Life Satisfaction Simulated Data
Example Student
Subject Age Gender Married...
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