BUSMGT 2320 Lecture 11: 11. Simple Linear Regression Part 2

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Conditions needed to perform classical inference in regression i. ii. Normal distribution with mean = 0 and constant standard deviation. The x(i) are considered to be constants in the model, as are b0 and b1. Then, y(i) is a simple linear function of (i); b. If the (i) terms are normally distributed, y is also normally distributed. The value for 2 is the same for all values of x. This implied that the variability of each distribution for y is the same regardless of the x value. Thus, the value of an error term in the model is not related to any other observation in the data set. This, in turn, implies that the y(i) values are independent. The expected value (long-run mean) of y(i) is the population regression equation. Residuals are the sample counterparts to estimates of the (i). There is a residual for each data point: Q-q normal plot, or a normal probability plot, of the residuals.

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