Introduction to Linear Regression Analysis, Solutions Manual by Douglas C. MontgomeryPraise for the Fourth EditionAs with previous editions, the authors have produced a leading textbook on regression.--Journal of the American Statistical Association
A comprehensive and up-to-date introduction to the fundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in todays cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression modelsRegression models with random effects in addition to a discussion on subsampling and the importance of the mixed modelTests on individual regression coefficients and subsets of coefficientsExamples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data.
In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material, and a related FTP site features the presented data sets, extensive problem solutions, software hints, and PowerPoint slides to facilitate instructional use of the book.
Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
Exam Questions – Regression
A college bookstore must order books two months before each semester starts. They believe that the number of books that will ultimately be sold for any particular course is related to the number of students registered for the course when the books are ordered. They would like to develop a linear regression equation to help plan how many books to order. From past records, the bookstore obtains the number of students registered, X, and the number of books actually sold for a course, Y, for 12 different semesters. These data are below. Carefully explain what the p-value found in part A means. Fully interpret the strength of the straight-line relationship.
Notice: Visit gmstat. Question 2: The percent of total variation of the dependent variable Y explained by the set of independent variables X is measured by. Question 3: A coefficient of correlation is computed to be A The relationship between two variables is weak B The relationship between two variables is strong and positive C The relationship between two variables is strong and but negative D Correlation coefficient cannot have this value. Question 4: Let the coefficient of determination computed to be 0.
There are 3 major areas of questions that the regression analysis answers — 1 causal analysis, 2 forecasting an effect, 3 trend forecasting. The first category establishes a causal relationship between two variables, where the dependent variable is continuous and the predictors are either categorical dummy coded , dichotomous, or continuous.. In contrast to correlation analysis which does not indicate directionality of effects, the regression analysis assumes that the independent variable has an effect on the dependent variable. Medicine: Has the body weight an influence on the blood cholesterol level? To answer this question the researcher would measure body weight and blood cholesterol level in various subjects. The linear regression analysis can then show whether the body weight independent variable has an effect on the blood cholesterol level dependent variable. Biology: Does the oxygen level in water stimulate plant growth?
In this lesson, we apply regression analysis to some fictitious data, and we show how to interpret the results of our analysis. If you view this web page on a different browser e. Note: Regression computations are usually handled by a software package or a graphing calculator.
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