STAT 2060 Lecture Notes - Lecture 31: Confidence Interval, Test Statistic

• School: University of Guelph
• Department: Statistics
• Course: STAT 2060
• Professor: Peter Kim

Document Summary

Linear model: consider the following linear model. Y is called the dependent (response) variable y dependent variable x is the independent (predictor) variable. Intercept and slope: consider the following linear model. 1 is the slope of the line. Random error: consider the following linear model. Is a random (error) variable with mean 0 has mean 0. And variance 2 has variance 2. Fitting the model to date: let the data come in pairs (x1, y1). Compute, the following the sample mean x and y the sum of squares for x the cross sum for x and y . Alternative formulas: let the data come in pairs (x1, y1). ,(xn, yn) be a random sample from some population. Compute, the following sum of squares for x . Custom uofg textbook the cross sum for x and y . Estimating intercept and slope: let the data come in pairs (x1, y1), .