We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Modified over 5 years ago
LINEAR REGRESSION: What it Is and How it Works
Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r
What is Bivariate Linear Regression? Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured
Why is it Bivariate? Two variables: X and Y X - independent variable/predictor variable Y - dependent/outcome/criterion variable
Why is it Linear? Based on the linear relationship (correlation) between X and Y The relationship can be described by the equation for a straight line
The Regression Equation y = b 1 x i + b 0 + e i y = predicted score on criterion variable b 0 = intercept x i = measured score on predictor variable b 1 = slope e i = residual (error score)
Least-Squares Solution Minimize squared error in prediction. Error (residual) = difference between predicted y and actual y
How It’s Based on r Replace x and y with z X and z Y : z Y = b 1 z X + b o and the y-intercept becomes 0: z Y = b 1 z X and the slope becomes r: z Y = rz X
Take-Home Point Linear regression is a way of using information about a correlation to make predictions
Kin 304 Regression Linear Regression Least Sum of Squares
Linear Equations Review. Find the slope and y intercept: y + x = -1.
Regresi Linear Sederhana Pertemuan 01 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Coefficient of Determination Section 4.3 Alan Craig
Chapter 10 Curve Fitting and Regression Analysis
Definition Regression Model Regression Equation Y i = 0 + 1 X i ^ Given a collection of paired data, the regression equation algebraically describes.
Chapter 4 Describing the Relation Between Two Variables
LINEAR REGRESSION: Evaluating Regression Models Overview Assumptions for Linear Regression Evaluating a Regression Model.
LINEAR REGRESSION: What it Is and How it Works Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
LINEAR REGRESSION: Evaluating Regression Models. Overview Assumptions for Linear Regression Evaluating a Regression Model.
LINEAR REGRESSION: Evaluating Regression Models. Overview Standard Error of the Estimate Goodness of Fit Coefficient of Determination Regression Coefficients.
LINEAR REGRESSION: What it Is and How it Works. Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r Assumptions.
Bivariate Regression CJ 526 Statistical Analysis in Criminal Justice.
REGRESSION What is Regression? What is the Regression Equation? What is the Least-Squares Solution? How is Regression Based on Correlation? What are the.
Linear Regression MARE 250 Dr. Jason Turner.
Business Statistics - QBM117 Least squares regression.
REGRESSION Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured.
C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Linear Regression and Linear Prediction Predicting the score on one variable.
Correlation 1. Correlation - degree to which variables are associated or covary. (Changes in the value of one tends to be associated with changes in the.
Multiple Regression Research Methods and Statistics.
© 2021 SlidePlayer.com Inc. All rights reserved.