Y i = (b 0 +b 1X 1i +b 2X 2i+ … b nX ni) + Ɛ iī n is the coefficient is the nth predictor (X ni) The linear model expands to include as many predictor variables as you like.Īn additional predictor can be placed in the model given a b to estimate its relationship to the outcome: These parameters are regression coefficients. the point at which the the line crosses the vertical axis of the graph (the intercept of the line, b 0).the slope of the line (usually denoted by b 1). This model uses an unstandardised measure of the relationship (b 1) and consequently we include a parameter b 0 that tells us the value of the outcome when the predictor is zero.Īny straight line can be defined by two things: Back to top An introduction to the linear model (regression)
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