(BQ) Part 2 book "An introduction to statistical methods and data analysis" has contents: Linear regression and correlation, multiple regression and the general linear model; further regression topics, analysis of variance for blocked designs, the analysis of covariance; analysis of variance for some unbalanced designs | 11/25/08 5:38 PM CHAPTER 11 Page 572 Introduction and Abstract of Research Study Linear Regression and Correlation Estimating Model Parameters Inferences about Regression Parameters Predicting New y Values Using Regression Examining Lack of Fit in Linear Regression The Inverse Regression Problem (Calibration) Correlation Research Study: Two Methods for Detecting E. coli Summary and Key Formulas Exercises Introduction and Abstract of Research Study The modeling of the relationship between a response variable and a set of explanatory variables is one of the most widely used of all statistical techniques. We refer to this type of modeling as regression analysis. A regression model provides the user with a functional relationship between the response variable and explanatory variables that allows the user to determine which of the explanatory variables have an effect on the response. The regression model allows the user to explore what happens to the response variable for specified changes in the explanatory variables. For example, financial officers must predict future cash flows based on specified values of interest rates, raw material costs, salary increases, and so on. When designing new training programs for employees, a company would want to study the relationship between employee efficiency and explanatory variables such as the results from employment tests, experience on similar jobs, educational background, and previous training. Medical researchers attempt to determine the factors which have an effect on cardiorespiratory fitness. Forest scientists study the relationship between the volume of wood in a tree to the diameter of the tree at a specified heights and the taper of the tree. The basic idea of regression analysis is to obtain a model for the functional relationship between a response variable (often referred to as the .
