Richard Ian Carpenter
16 Jan 2016DATA_ADSENSE
This Shiny App was developed as a result of a series of discussions that took place in the forums of the Statistical Inference and Machine Learning courses. The unresoved issue was focused on selecting model parameters and finding the ?best? model.
mtcarsdataset and allows the user to define both the dependent and independent variables in a linear model (
summary()function were used along with the regression.
Here is an example of a regression using the
Call: lm(formula = qsec ~ hp, data = mtcars) Residuals: Min 1Q Median 3Q Max -2.1766 -0.6975 0.0348 0.6520 4.0972 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 20.556354 0.542424 37.897 < 2e-16 *** hp -0.018458 0.003359 -5.495 5.77e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.282 on 30 degrees of freedom Multiple R-squared: 0.5016, Adjusted R-squared: 0.485 F-statistic: 30.19 on 1 and 30 DF, p-value: 5.766e-06
Here is the example regression plot output, created using ggplot2: