You can see the regression equation of each subset with hovering your mouse on the regression lines. To visualize this model, you can make a faceted plot with ggPredict() function. Under the Perform option, the Hypothesis tests option is selected by default with a null value of 0 for. Select SQFT for the X variable and PRICE for the Y variable. Since we are performing a simple linear regression, we can find the equation for the regression line in just a few steps. To create a simple linear regression model for sales price using square footage, choose the Stat > Regression > Simple Linear menu option. Multiple R-squared: 0.1626, Adjusted R-squared: 0.1078į-statistic: 2.967 on 7 and 107 DF, p-value: 0.006982įrom the analysis result, you can get the regression equation for a patient without hypertension(HBP=0) and body weight 60kg: the intercept is 64.12+(-0.39685*60) and the slope is -0.67650+(0.01686*60). Creating a simple linear regression model. The value of R2 is really useful in comparing the models in multiple linear regression. Residual standard error: 22.8 on 107 degrees of freedom where y i is the value observed for the dependent variable for. The linear regression equation is written for observation i as follows: yi a1x1i + a2x2i +. (Intercept) 64.11678 155.82328 0.411 0.682Īge:weight:HBP -0.01666 0.05777 -0.288 0.774 The principle of linear regression is to model a quantitative dependent variable Y through a linear combination of p quantitative explanatory variables, X 1, X 2,, X p. Lm(formula = NTAV ~ age * weight * HBP, data = radial) Fit4= lm(NTAV ~age *weight *HBP, data=radial) summary(fit4)
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