Simple Linear Regression Chapter 11 Simple Linear Regression Slides for Optional Sections No optional sections

Probabilistic Models General form of Probabilistic Models Y = Deterministic Component + Random Error where E(y) = Deterministic Component

Probabilistic Models First Order (Straight-Line) Probabilistic Model

Probabilistic Models 5 steps of Simple Linear Regression Hypothesize the deterministic component Use sample data to estimate unknown model parameters Specify probability distribution of , estimate standard deviation of the distribution Statistically evaluate model usefulness Use for prediction, estimatation, once model is useful

Fitting the Model: The Least Squares Approach Reaction Time versus Drug Percentage Subject Amount of Drug x (%) Reaction Time y (seconds) 1 2 3 4 5

Fitting the Model: The Least Squares Approach Least Squares Line has: Sum of errors (SE) = 0 Sum of Squared errors (SSE) is smallest of all straight line models Formulas: Slope: y-intercept

Fitting the Model: The Least Squares Approach

Model Assumptions Mean of the probability distribution of ε is 0 Variance of the probability distribution of ε is constant for all values of x Probability distribution of ε is normal Values of ε are independent of each other

An Estimator of 2 Estimator of 2 for a straight-line model

Assessing the Utility of the Model: Making Inferences about the Slope 1 Sampling Distribution of

Assessing the Utility of the Model: Making Inferences about the Slope 1 A Test of Model Utility: Simple Linear Regression One-Tailed Test Two-Tailed Test H0: β1=0 Ha: β1<0 (or Ha: β1>0) Ha: β1≠0 Rejection region: t< -tα (or t< -tα when Ha: β1>0) Rejection region: |t|> tα/2 Where tα and tα/2 are based on (n-2) degrees of freedom

Assessing the Utility of the Model: Making Inferences about the Slope 1 A 100(1-α)% Confidence Interval for 1 where

The Coefficient of Correlation A measure of the strength of the linear relationship between two variables x and y

The Coefficient of Determination

Using the Model for Estimation and Prediction Sampling errors and confidence intervals will be larger for Predictions than for Estimates Standard error of Standard error of the prediction

Using the Model for Estimation and Prediction 100(1-α)% Confidence interval for Mean Value of y at x=xp 100(1-α)% Confidence interval for an Individual New Value of y at x=xp where tα/2 is based on (n-2) degrees of freedom