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Linear Regression Linear regression is a very simple supervised learning methods, though still very useful. Simple Linear Regression Simple linear regression is a straightforward approach for predicting a quantitative response $Y$ on the basis of a single predictor variable $X$. It assumes that there is approximately a linear relationship between $X$ and $Y$. $$ Y \approx \beta_{0} + \beta_{1}X $$ In the equation, $\beta_0$ and $\beta_1$ are two unknown constants that represetn the intercept and slope termes in the linear model. [Read More]

Statistical Learning What is statistial learning Suppose we observe a quantitative response $Y$ and $p$ different predictors, $X_1, X_2, \ldots,X_p$ . We assume that there is a relationship between $Y$ and $X=(X_1, X_2,\ldots,X_p)$, which can be written as $$ Y=f(X)+\epsilon $$ Here $f$ is some fixed but unknown fucntion of $X_1,X_2,\ldots,X_p$, and $\epsilon$ is a random error term, which is independent of $X$ and has mean zero. In this formulation, $f$ represents the systematic informationa $X$ provides about $Y$. [Read More]