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Chapter 14 - Simple linear regression analysis. After mastering the material in this chapter, you will be able to: Explain the simple linear regression model, find the least squares point estimates of the slope and y-intercept, describe the assumptions behind simple linear regression and calculate the standard error,. | Simple Linear Regression Analysis Chapter 14 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis 14.1 The Simple Linear Regression Model and the Least Square Point Estimates 14.2 Model Assumptions and the Standard Error 14.3 Testing the Significance of Slope and y-Intercept 14.4 Confidence and Prediction Intervals 14.5 Simple Coefficients of Determination and Correlation 14- Simple Linear Regression Analysis Continued 14.6 Testing the Significance of the Population Correlation Coefficient 14.7 An F Test for the Model 14.8 The QHIC Case 14.9 Residual Analysis 14.10 Some Shortcut Formulas (Optional) 14- 14.1 The Simple Linear Regression Model and the Least Squares Point Estimates The dependent (or response) variable is the variable we wish to understand or predict The independent (or predictor) variable is the variable we will use to understand or predict the dependent variable Regression . | Simple Linear Regression Analysis Chapter 14 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis 14.1 The Simple Linear Regression Model and the Least Square Point Estimates 14.2 Model Assumptions and the Standard Error 14.3 Testing the Significance of Slope and y-Intercept 14.4 Confidence and Prediction Intervals 14.5 Simple Coefficients of Determination and Correlation 14- Simple Linear Regression Analysis Continued 14.6 Testing the Significance of the Population Correlation Coefficient 14.7 An F Test for the Model 14.8 The QHIC Case 14.9 Residual Analysis 14.10 Some Shortcut Formulas (Optional) 14- 14.1 The Simple Linear Regression Model and the Least Squares Point Estimates The dependent (or response) variable is the variable we wish to understand or predict The independent (or predictor) variable is the variable we will use to understand or predict the dependent variable Regression analysis is a statistical technique that uses observed data to relate the dependent variable to one or more independent variables The objective is to build a regression model that can describe, predict and control the dependent variable based on the independent variable LO14-1: Explain the simple linear regression model. 14- Form of The Simple Linear Regression Model y = β0 + β1x + ε y = β0 + β1x + ε is the mean value of the dependent variable y when the value of the independent variable is x β0 is the y-intercept; the mean of y when x is 0 β1 is the slope; the change in the mean of y per unit change in x ε is an error term that describes the effect on y of all factors other than x LO14-1 14- Regression Terms β0 and β1 are called regression parameters β0 is the y-intercept and β1 is the slope We do not know the true values of these parameters So, we must use sample data to estimate them b0 is the estimate of β0 and b1 is the estimate of β1 LO14-1 14- The Least .
