Multiple regression is the extension of simple regression, to take account of more than one
independent variable X. In multiple regression, we study the relationship between Y and a number of
explanatory variable (X1, X2, …, Xk). The model we assume is as follows:
Yi = β0 + β1X1 + β2X2 + … + βkXk + ei
When you have completed this chapter, you will be able to: Understand the importance of an appropriate model specification and multiple regression analysis, comprehend the nature and technique of multiple regression models and the concept of partial regression coefficients, use the estimation techniques for multiple regression models,...
Lecture "Applied econometrics course - Chapter 2: Multiple regression model" has content: Why we need multiple regression model, estimation, R – Square, assumption, variance and standard error of parameters, the issues of multiple regression model, Illustration by Computer.
Lecture Basic statistics for business and economics - Chapter 14: Multiple regression analysis. After completing this chapter, students will be able to: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis, develop and interpret an ANOVA table, compute and interpret measures of association in multiple regression,...
(BQ) This paper presents an investigation of the effects of machining variables on the surface roughness of wire-EDMed DC53 die steel. In this study, the machining variables investigated were pulse-peak current, pulse-on time, pulse-off time, and wire tension. Analysis of variance (ANOVA) technique was used to find out the variables affecting the surface roughness. Assumptions of ANOVA were discussed and carefully examined using analysis of residuals. Quantitative testing methods on residual analysis were used in place of the typical qualitative testing techniques.
This book had its origin over 30 years ago, when it became apparent to Jack Cohen that there
were relationships between multiple regression and correlation (MRC) on the one hand and
the analysis of variance (ANOVA) on the other which were undreamed of (or at least did not
appear) in the standard textbooks with which he was familiar. On the contrary, the texts of the
era treated MRC and ANOVA as wholly distinct systems of data analysis intended for types of
research that differed fundamentally in design, goals, and types of variables.
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Synchronized multiple regression of diagnostic radiation-induced rather than spontaneous: disseminated primary intracranial germinoma in a woman: a case report
Chapter 15 - Multiple regression and model building. After mastering the material in this chapter, you will be able to: Explain the multiple regression model and the related least squares point estimates, explain the assumptions behind multiple regression and calculate the standard error, calculate and interpret the multiple and adjusted multiple coefficients of determination,...
Chapter 14 - Multiple regression analysis. This chapter include objectives: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination.
Chapter 14 - Multiple regressions and correlation analysis. In this chapter, the learning objectives are: Describe the relationship between several independent variables and a dependent variable using multiple regression analysis; set up, interpret, and apply an ANOVA table compute and interpret the multiple standard error of estimate, the coefficient of multiple determination, and the adjusted coefficient of multiple determination; conduct a test of hypothesis to determine whether regression coefficients differ from zero;...
(BQ) Part 2 book "Basic statistics for business & economics" has contents: Estimation and confidence intervals; one sample tests of hypothesis; two sample tests of hypothesis; analysis of variance; linear regression and correlation; multiple regression and correlation analysis; chi square applications
(BQ) Part 2 book "Business research methods" has contents: Questionnaire design; data preparation and preliminary analysis, multiple regression multiple regression; data preparation and preliminary analysis, exploratory factor and principal component analysis, cluster analysis, binary logistic regression, business research reports.
(BQ) Part 2 book "Statistical techniques in business & economics" has contents: Analysis of variance, correlation and linear regression, multiple regression analysis, statistical process control and quality management, an introduction to decision theory, index numbers,...and other contents.
(BQ) Part 2 book "Statistics - The art and science of learning from data" has contents: Statistical inference - confidence intervals; comparing two groups, multiple regression, nonparametric statistics, comparing groups - analysis of variance methods,...and other contents.
(BQ) Part 2 book "Statistics for the behavioral sciences" has contents: Testing means - The related samples t-Test; estimation and confidence intervals; linear regression and multiple regression; nonparametric tests - Chi-Square tests;...and other contents.
(BQ) Part 2 book "Essentials of statistics for business and economics" has contents: Interval estimation, hypothesis tests, simple linear regression, multiple regression, comparisons involving proportions and a test of independence,...and other contents.
(BQ) Part 2 book "Basic business statistics - Concepts and applications" has contents: Analysis of variance, simple linear regression, introduction to multiple regression, multiple regression model building, statistical applications in quality management, a road map for analyzing data,...and other contents.
(BQ) Part 2 book "Introduction to statistics in psychology" has contents: Statistics and the analysis of experiments, multivariate analysis of variance, multiple regression and multiple correlation, path analysis, binomial logistic regression,...and other contents.