Simple Linear Regression

Slides:



Advertisements
Similar presentations
1 1 Chapter 5: Multiple Regression 5.1 Fitting a Multiple Regression Model 5.2 Fitting a Multiple Regression Model with Interactions 5.3 Generating and.
Advertisements

Forecasting Using the Simple Linear Regression Model and Correlation
Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
© McGraw-Hill Higher Education. All Rights Reserved. Chapter 2F Statistical Tools in Evaluation.
Simple Linear Regression
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter Topics Types of Regression Models
Simple Linear Regression Analysis
Regression The basic problem Regression and Correlation Accuracy of prediction in regression Hypothesis testing Regression with multiple predictors.
SIMPLE LINEAR REGRESSION
© 2000 Prentice-Hall, Inc. Chap Forecasting Using the Simple Linear Regression Model and Correlation.
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.
Simple Linear Regression Analysis
Checking Regression Model Assumptions NBA 2013/14 Player Heights and Weights.
SIMPLE LINEAR REGRESSION
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.
Chapter 11 Simple Regression
OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.
Regression and Correlation Jake Blanchard Fall 2010.
Further Topics in Regression Analysis Objectives: By the end of this section, I will be able to… 1) Explain prediction error, calculate SSE, and.
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 12-1 Correlation and Regression.
Introduction to Linear Regression
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
MTH 161: Introduction To Statistics
Part IV Significantly Different: Using Inferential Statistics
Chapter 7 Relationships Among Variables What Correlational Research Investigates Understanding the Nature of Correlation Positive Correlation Negative.
Copyright ©2011 Nelson Education Limited Linear Regression and Correlation CHAPTER 12.
REGRESSION DIAGNOSTICS Fall 2013 Dec 12/13. WHY REGRESSION DIAGNOSTICS? The validity of a regression model is based on a set of assumptions. Violation.
–The shortest distance is the one that crosses at 90° the vector u Statistical Inference on correlation and regression.
Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.
Psychology 202a Advanced Psychological Statistics November 12, 2015.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
Chapter 11: Linear Regression and Correlation Regression analysis is a statistical tool that utilizes the relation between two or more quantitative variables.
…. a linear regression coefficient indicates the impact of each independent variable on the outcome in the context of (or “adjusting for”) all other variables.
Dependent (response) Variable Independent (control) Variable Random Error XY x1x1 y1y1 x2x2 y2y2 …… xnxn ynyn Raw data: Assumption:  i ‘s are independent.
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 + 
Agenda 1.Exam 2 Review 2.Regression a.Prediction b.Polynomial Regression.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
Construction Engineering 221 Probability and Statistics.
BUSINESS MATHEMATICS & STATISTICS. Module 6 Correlation ( Lecture 28-29) Line Fitting ( Lectures 30-31) Time Series and Exponential Smoothing ( Lectures.
Chapter 11 Linear Regression and Correlation. Explanatory and Response Variables are Numeric Relationship between the mean of the response variable and.
INTRODUCTION TO MULTIPLE REGRESSION MULTIPLE REGRESSION MODEL 11.2 MULTIPLE COEFFICIENT OF DETERMINATION 11.3 MODEL ASSUMPTIONS 11.4 TEST OF SIGNIFICANCE.
Chapter 11: Linear Regression E370, Spring From Simple Regression to Multiple Regression.
Regression Overview. Definition The simple linear regression model is given by the linear equation where is the y-intercept for the population data, is.
Simple Linear Regression
Lecturer: Ing. Martina Hanová, PhD.
Regression Chapter 6 I Introduction to Regression
Inference in Simple Linear Regression
Chapter 12: Regression Diagnostics
Checking Regression Model Assumptions
Quantitative Methods Simple Regression.
S519: Evaluation of Information Systems
Checking Regression Model Assumptions
DRQ 8 Dr. Capps AGEC points
Simple Linear Regression
Merve denizci nazlıgül, M.s.
Lecturer: Ing. Martina Hanová, PhD.
SIMPLE LINEAR REGRESSION
Simple Linear Regression
Linear Regression and Correlation
Example on the Concept of Regression . observation
SIMPLE LINEAR REGRESSION
Linear Regression and Correlation
Linear Regression and Correlation
Correlation and Covariance
Correlation and Simple Linear Regression
Correlation and Simple Linear Regression
Presentation transcript:

Simple Linear Regression NBA 2013/14 Player Heights and Weights

Data Description / Model Heights (X) and Weights (Y) for 505 NBA Players in 2013/14 Season. Other Variables included in the Dataset: Age, Position Simple Linear Regression Model: Y = b0 + b1X + e Model Assumptions: e ~ N(0,s2) Errors are independent Error variance (s2) is constant Relationship between Y and X is linear No important (available) predictors have been ommitted

Regression Calculations

Inference Concerning b1

EXCEL Output and Inference for b0

Estimating Mean and Predicting New Response at x=x*

Coefficients of Correlation and Determination

Analysis of Variance and F-Test

Linearity of Regression

Height and Weight Data – n=505, c=18 Groups Do not reject H0: mj = b0 + b1Xj

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.