Regression and Correlation of Data

Slides:



Advertisements
Similar presentations
Correlation-Regression The correlation coefficient measures how well one can predict X from Y or Y from X.
Advertisements

Probability & Statistics for Engineers & Scientists, by Walpole, Myers, Myers & Ye ~ Chapter 11 Notes Class notes for ISE 201 San Jose State University.
Slide 4- 1 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Active Learning Lecture Slides For use with Classroom Response.
OBJECTIVES 2-2 LINEAR REGRESSION
1 1 Slide Simple Linear Regression Chapter 14 BA 303 – Spring 2011.
Linear Regression Analysis
Section 8.3 – Systems of Linear Equations - Determinants Using Determinants to Solve Systems of Equations A determinant is a value that is obtained from.
Lesson Nonlinear Regression: Transformations.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.
Solving Systems of Equations: Elimination Method.
Regression analysis Control of built engineering objects, comparing to the plan Surveying observations – position of points Linear regression Regression.
16-1 Linear Trend The long term trend of many business series often approximates a straight line.
Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.
Linear Regression Least Squares Method: the Meaning of r 2.
1 Dr. Jerrell T. Stracener EMIS 7370 STAT 5340 Probability and Statistics for Scientists and Engineers Department of Engineering Management, Information.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
MTH 161: Introduction To Statistics
Sec 1.5 Scatter Plots and Least Squares Lines Come in & plot your height (x-axis) and shoe size (y-axis) on the graph. Add your coordinate point to the.
Regression Regression relationship = trend + scatter
Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.
Linear Regression Analysis Using MS Excel Tutorial for Assignment 2 Civ E 342.
Chapter 6 (cont.) Difference Estimation. Recall the Regression Estimation Procedure 2.
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
Statistics and Numerical Method Part I: Statistics Week VI: Empirical Model 1/2555 สมศักดิ์ ศิวดำรงพงศ์ 1.
1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
Differential Equations Linear Equations with Variable Coefficients.
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 + 
Multiple Regression Analysis Regression analysis with two or more independent variables. Leads to an improvement.
Method 3: Least squares regression. Another method for finding the equation of a straight line which is fitted to data is known as the method of least-squares.
Return To Index Excel Microsoft Excel Basics Lesson 23 Simple Linear Regression An ExampleAn Example - 2 The Regression Steps - 3 The Regression.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
AP STATISTICS LESSON 3 – 3 (DAY 2) The role of r 2 in regression.
Introduction to regression 3C. Least-squares regression.
The Least Squares Regression Line. The problem with drawing line of best fit by eye is that the line drawn will vary from person to person. Instead, use.
Chapter 5: Systems of Linear Equations Section 5.1: Solving Systems of Linear Equations by Elimination.
Using Matrices to Solve a 3-Variable System
Regression and Correlation of Data Summary
REGRESSION (R2).
LEAST – SQUARES REGRESSION
Linear Regression Special Topics.
ENM 310 Design of Experiments and Regression Analysis
Solving Systems of Linear Equations in 3 Variables.
Multiple Regression.
BIVARIATE REGRESSION AND CORRELATION
Probability and Statistics for Computer Scientists Second Edition, By: Michael Baron Section 11.1: Least squares estimation CIS Computational.
2. Find the equation of line of regression
Linear Regression.
Correlation and Regression
AP STATISTICS LESSON 3 – 3 (DAY 2)
Section 10.2: Fitting a Linear Model to Data
Coefficient of Determination & using technology
Least Squares Method: the Meaning of r2
Regression Analysis Jared Dean as quoted in Big Data, Data Mining, and Machine Learning From my experience, regression is the most dominant force in driving.
Correlation and Regression
CALCULATING EQUATION OF LEAST SQUARES REGRESSION LINE
Chapter 7 Demand Forecasting in a Supply Chain
Solving Systems of Linear Equations in 3 Variables.
Unit 2 Quantitative Interpretation of Correlation
Lesson – How can I measure my linear fit? - Correlations
Regression and Correlation of Data
Descriptive Statistics Univariate Data
More Properties of Logarithms
Residuals (resids).
Calculating Linear Regressions (y = ax + b) on Calculators
Cases. Simple Regression Linear Multiple Regression.
Regression and Correlation of Data
Bootstrapping and Bootstrapping Regression Models
Presentation transcript:

Regression and Correlation of Data Other forms with single input or regressor: Method: Convert the equation to simple linear form by setting new variable(s). Calculate the new variable(s). 3. Use least square method to determine the coefficients. Convert the equation to the form containing the original variables.

Regression and Correlation of Data Other forms transformable to give equations linear in the coefficients Method: - Convert the equation to linear form. - Then follows the steps on the previous slide.

Regression and Correlation of Data Other forms transformable to give equations linear in the coefficients Method: - Convert the equation to linear form. Method: - Convert the equation to linear form.