

However, the final regression model was just a linear combination of higher-ordered predictors. For example, polynomial regression was used to model curvature in our data by using higher-ordered values of the predictors. The parameter estimates, b 0 = 42.3 and b 1 = 0.49, were obtained using the least squares method.All of the models we have discussed thus far have been linear in the parameters (i.e., linear in the beta's).

The line passing through the data points is the graph of the estimated regression equation: ŷ = 42.3 + 0.49 x. Values of the independent variable, stress test score, are given on the horizontal axis, and values of the dependent variable, blood pressure, are shown on the vertical axis. The data are shown graphically in the figure, called a scatter diagram. Assume that both a stress test score and a blood pressure reading have been recorded for a sample of 20 patients. The graph of the estimated regression equation for simple linear regression is a straight line approximation to the relationship between y and x.Īs an illustration of regression analysis and the least squares method, suppose a university medical centre is investigating the relationship between stress and blood pressure. Using these estimates, an estimated regression equation is constructed: ŷ = b 0 + b 1 x. For simple linear regression, the least squares estimates of the model parameters β 0 and β 1 are denoted b 0 and b 1.

The least squares method is the most widely used procedure for developing estimates of the model parameters.
WHAT IS NOT A LINEAR REGRESSION EQUATION EXAMPLE HOW TO
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