STAT231 Lecture : chapter6a_regression_model.pdf
17 Jan 2014
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Statistical model: rewriting a model: a probability distribution is a simple model, the gaussian model can be rewritten as. R g: tis is called the one sample gaussian model because it assumes that everyone has the same mean. Example: house prizes: how expensive are houses in my city on. , ~ (0, average: y is a random variable for house price, is the expected value for the house prices, r is the difference between any one house. Linear regression model: the model makes it easier to add an x variables, let x x r. R g: this model specifies a linear relationship between x and y, the assumption of linearity may be appropriate or not. R g: alpha is called intercept or constant, alpha is the expected value when x=0, beta is the slope of the line. Housing data in pittsburgh (2011: problem: what is an average house price in. 2 y i y x i x i.
