Regression and Correlation of Data

Slides:



Advertisements
Similar presentations
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Advertisements

Kin 304 Regression Linear Regression Least Sum of Squares
Probability & Statistical Inference Lecture 9
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
11 Simple Linear Regression and Correlation CHAPTER OUTLINE
Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
Simple Linear Regression. G. Baker, Department of Statistics University of South Carolina; Slide 2 Relationship Between Two Quantitative Variables If.
Simple Linear Regression
1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.
Chapter 10 Simple Regression.
Chapter Topics Types of Regression Models
Probability & Statistics for Engineers & Scientists, by Walpole, Myers, Myers & Ye ~ Chapter 11 Notes Class notes for ISE 201 San Jose State University.
Simple Linear Regression Analysis
Quantitative Business Analysis for Decision Making Simple Linear Regression.
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Business Statistics - QBM117 Statistical inference for regression.
Chapter 7 Forecasting with Simple Regression
Linear Regression/Correlation
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS & Updated by SPIROS VELIANITIS.
Marketing Research Aaker, Kumar, Day and Leone Tenth Edition
Introduction to Linear Regression and Correlation Analysis
Regression Analysis Regression analysis is a statistical technique that is very useful for exploring the relationships between two or more variables (one.
1 Least squares procedure Inference for least squares lines Simple Linear Regression.
Statistics for Business and Economics Dr. TANG Yu Department of Mathematics Soochow University May 28, 2007.
Basic Probability (Chapter 2, W.J.Decoursey, 2003) Objectives: -Define probability and its relationship to relative frequency of an event. -Learn the basic.
1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
1 11 Simple Linear Regression and Correlation 11-1 Empirical Models 11-2 Simple Linear Regression 11-3 Properties of the Least Squares Estimators 11-4.
LECTURE 3: ANALYSIS OF EXPERIMENTAL DATA
1 1 Slide Simple Linear Regression Estimation and Residuals Chapter 14 BA 303 – Spring 2011.
1 Regression Analysis The contents in this chapter are from Chapters of the textbook. The cntry15.sav data will be used. The data collected 15 countries’
Linear Regression Linear Regression. Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. Purpose Understand Linear Regression. Use R functions.
1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 
Chapter 11 Linear Regression and Correlation. Explanatory and Response Variables are Numeric Relationship between the mean of the response variable and.
1 Simple Linear Regression Chapter Introduction In Chapters 17 to 19 we examine the relationship between interval variables via a mathematical.
The simple linear regression model and parameter estimation
Regression and Correlation of Data Summary
Regression Analysis AGEC 784.
Inference for Least Squares Lines
11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.
Linear Regression.
Statistics for Managers using Microsoft Excel 3rd Edition
Kin 304 Regression Linear Regression Least Sum of Squares
Ch12.1 Simple Linear Regression
Slides by JOHN LOUCKS St. Edward’s University.
Simple Linear Regression - Introduction
Correlation and Simple Linear Regression
CHAPTER 29: Multiple Regression*
Linear Regression.
6-1 Introduction To Empirical Models
Linear Regression/Correlation
No notecard for this quiz!!
PENGOLAHAN DAN PENYAJIAN
Correlation and Simple Linear Regression
Least Squares Method: the Meaning of r2
Simple Linear Regression and Correlation
Adequacy of Linear Regression Models
Simple Linear Regression
Determine the type of correlation between the variables.
Linear Regression and Correlation
Adequacy of Linear Regression Models
Linear Regression and Correlation
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Introduction to Regression
Regression and Correlation of Data
St. Edward’s University
Correlation and Simple Linear Regression
Correlation and Simple Linear Regression
Presentation transcript:

Regression and Correlation of Data Apply the least squares method for fitting data with linear regression Methods of regression are used to summarize sets of data in useful form. The values of the data have already been recorded, and as such, are fixed.

Regression and Correlation of Data Regression analysis: a collective name for techniques for the modeling and analysis of numerical data in order to find out the relation between the dependent e.g. y and independent variable(s) e.g. x. Assumption: - There is no error in the independent variable(s). - All errors due to measurement and to approximations in the modeling equations appear in the dependent variable, y.

Regression and Correlation of Data Examples common to engineers: The output of a CSTR changes as the temperature changes within the reactor, and the measurement of output concentration causes additional variation due to measurement errors. The power produced by an electric motor varies with changing input voltage, and any measurement of output voltage includes measuring errors.

Regression and Correlation of Data Application of Regression: - Find the relation between the dependent y and independent variable(s) x. y=a+bx y=axb y=a0+a1x+a2x2+…+anxn or y=a0+a1x1+a2x2+…+anxn or else - Determine the important coefficients. e.g. Determine reaction rate constant k. CAo and CA are the initial reactant concentration and that at time t. Predict the values of the dependent variable using the regressed model and coefficients.

Regression and Correlation of Data Simple Linear Regression: For an example, for the independent variable x (called input or regressor), and the dependent variable y (called response), the following relation exists (y may also be represented as the mean of the probability distribution E(Y)): E(Y)=α+βx α and β are constant parameters, called regression coefficients. From a sample consisting of n pairs of data (xi,yi), we calculate estimates, a for α and b for β. If at x=xi, is the estimated value of E(Y), then the fitted regression line is

Regression and Correlation of Data Simple Linear Regression: How to determine a and b? Method of Least Squares: a and b are determined by minimizing the sum of the squares of errors (SSE), deviation, residuals or difference between the data set and the straight line that approximate it.

Regression and Correlation of Data Simple Linear Regression: Method of Least Squares: Centroidal point:

Regression and Correlation of Data Method of Least Squares: Sum of the squares of errors (SSE), Estimated variance of the points from the line: Estimated standard deviation or standard error of the points from the line: The degrees of freedom=n data points – the number of estimated coefficients

Regression and Correlation of Data Assumptions and graphical checks: For simple linear regression of y on x, represents errors or deviations or residuals. Assumptions: A linear relation between y and x represents the data adequately. The errors ei are entirely in the y-direction and so independent of x. The distribution of errors follows normal distribution. The variance is constant.

Regression and Correlation of Data Assumptions and graphical checks: For simple linear regression of y on x, Graphical checks – satisfactory regression Plot residuals against x or y. There are about as many positive residuals as negatives. Small deviations considerably more frequent than larger ones. There are no outstanding outliers. There is no strong systematic pattern as x or y increases. If it is unsatisfactory, other regression equation should be chosen.