STAT 8010 Lecture Notes - Lecture 14: Linear Regression, Squared Deviations From The Mean, Observational Error
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The equation for a line is given by: 1 = the dependent variable (variable being predicted) the independent variable (variable used for the prediction) the y-intercept the slope of the line. Below is data and the line for y= 200+100x. Notice that the data points and the line match exactly. Example: in the table below is a random sample of rents taken from a rental pricing survey. All of the apartments have air conditioning, are unfurnished and have no dishwasher. Below is a graph of the data for the clemson rent example. There appears to be a linear relationship between the number of bedrooms and the monthly rent. We would like to fit a line through the data so that it can be used to predict the expected rent for different size apartments. The probabilistic linear model is y = 0 + 1x + where.
