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**Unformatted text preview: **Chapter 11 Inferences for Regression Parameters 11.1 Simple Linear Regression (SLR) Model This topic is covered in Chapter 2 (which we skipped). In these notes we are going to cover sections 2.3 to 2.10 (which in a sense describe the relation between two variables and hence treated early as descriptive statistics) as well the material in Chapter 11 that covers inference about the regression parameters. A simple linear regression model is a mathematical relationship between two quantitative variables, one of which, Y, is the variable we want to predict, using information on the second variable, X, which is assumed to be non-random. The model is written as 1 Y x β β ε = + + . In this model, • Y is called the response variable or the dependent variable (because its value depends to some extent on the value of X). • X is called the predictor (because it is used to predict Y) or explanatory variable (because it explains the variation or changes in Y). It is also called the independent variable (because its value does not depend on Y). • True regression line is denoted as 1 Y x β β = + • The parameters of the true regression line are the constants, β and β 1 • β is the intercept of the true regression line. • β 1 is the slope of the true regression line. • The true values of the regression parameters as well as the true regression line are unknown. The true regression line shows the deterministic relationship between X and Y (since X is a non-random variable and β as well as β 1 are (unknown) constants. • The random error, ε, incorporates the effects, on Y, of all variables (factors) other than X, in such a way that their net effect is zero on the average. • An observation on the i th unit in the population , denoted by y i , is 1 i i i y x β β ε = + + . • Here, ε i is the difference between the observed value of Y and the value on the true regression line that corresponds to X = x i . • The ε i are independent of each other and they all have the same normal distribution, with mean zero and variance σ 2 , that is ε i ~ N iid (0, σ 2 ). • As a result of the above property, 1 i i i y x β β ε = + + are random variables, that have normal distributions with mean μ Y that depends on the value of X and variance σ 2 that is the same for all X values, i.e., 2 1 | ~ ( , ) i i i Y X y x N β β ε μ σ = + + • 1 ˆ ˆ ˆ y x β β = + is called the prediction equation and ˆ y is the predicted value of Y for X = x. • Residual = ˆ ˆ i i i i y y e ε- = = is the difference between the observed and predicted value of Y. STA 3032 Chapter 11 Page 1 of 20 • The parameters of the true regression line are estimated the method of least squares, (LSE), where sum of the squared residuals 2 1 n i i e = ∑ is minimized, subject to 1 n i i e = = ∑ ....

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