Click to edit Master title style Midterm 3 Wednesday, June 10, 1:10pm.

Slides:

Advertisements

Similar presentations

Lesson 10: Linear Regression and Correlation

Advertisements

Learning Objectives Copyright © 2004 John Wiley & Sons, Inc. Bivariate Correlation and Regression CHAPTER Thirteen.

Correlation & Regression Chapter 15. Correlation statistical technique that is used to measure and describe a relationship between two variables (X and.

Correlation Correlation is the relationship between two quantitative variables. Correlation coefficient (r) measures the strength of the linear relationship.

Optical illusion ? Correlation ( r or R or  ) -- One-number summary of the strength of a relationship -- How to recognize -- How to compute Regressions.

Basic Statistical Concepts Psych 231: Research Methods in Psychology.

Regression and Correlation

Basic Statistical Concepts

Two Quantitative Variables Scatterplots examples how to draw them Association what to look for in a scatterplot Correlation strength of a linear relationship.

Correlation A correlation exists between two variables when one of them is related to the other in some way. A scatterplot is a graph in which the paired.

Chapter 13 Introduction to Linear Regression and Correlation Analysis

Business Statistics - QBM117 Interval estimation for the slope and y-intercept Hypothesis tests for regression.

Chapter 9: Correlation and Regression

Basic Statistical Concepts Part II Psych 231: Research Methods in Psychology.

Correlation and Regression. Relationships between variables Example: Suppose that you notice that the more you study for an exam, the better your score.

Simple Linear Regression and Correlation

Simple Linear Regression Analysis

Correlation & Regression

Correlation vs. Causation What is the difference?.

Linear Regression Modeling with Data. The BIG Question Did you prepare for today? If you did, mark yes and estimate the amount of time you spent preparing.

McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.

Correlation and Regression

Chapter 11 Simple Regression

MAT 254 – Probability and Statistics Sections 1,2 & Spring.

Correlation and regression 1: Correlation Coefficient

Chapter Correlation and Regression 1 of 84 9 © 2012 Pearson Education, Inc. All rights reserved.

Correlation and Regression

Chapter 14 – Correlation and Simple Regression Math 22 Introductory Statistics.

Chapter 15 Correlation and Regression

Anthony Greene1 Correlation The Association Between Variables.

Chap 12-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 12 Introduction to Linear.

Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.

Copyright © 2010 Pearson Education, Inc Chapter Seventeen Correlation and Regression.

Production Planning and Control. A correlation is a relationship between two variables. The data can be represented by the ordered pairs (x, y) where.

Example 1: page 161 #5 Example 2: page 160 #1 Explanatory Variable - Response Variable - independent variable dependent variable.

Scatterplot and trendline. Scatterplot Scatterplot explores the relationship between two quantitative variables. Example:

Multiple Regression BPS chapter 28 © 2006 W.H. Freeman and Company.

© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.

Aim: Review for Exam Tomorrow. Independent VS. Dependent Variable Response Variables (DV) measures an outcome of a study Explanatory Variables (IV) explains.

Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.

Political Science 30: Political Inquiry. Linear Regression II: Making Sense of Regression Results Interpreting SPSS regression output Coefficients for.

Creating a Residual Plot and Investigating the Correlation Coefficient.

3.3 Correlation: The Strength of a Linear Trend Estimating the Correlation Measure strength of a linear trend using: r (between -1 to 1) Positive, Negative.

Chapter 4 Summary Scatter diagrams of data pairs (x, y) are useful in helping us determine visually if there is any relation between x and y values and,

Correlation and Regression: The Need to Knows Correlation is a statistical technique: tells you if scores on variable X are related to scores on variable.

Warm-Up Write the equation of each line. A B (1,2) and (-3, 7)

June 30, 2008Stat Lecture 16 - Regression1 Inference for relationships between variables Statistics Lecture 16.

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.

Regression Analysis. 1. To comprehend the nature of correlation analysis. 2. To understand bivariate regression analysis. 3. To become aware of the coefficient.

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.

STILL MORE 9.1. VI. CORRELATION & CAUSATION Just because there is a strong relationship, this does NOT imply cause and effect!

Correlation and Median-Median Line Statistics Test: Oct. 20 (Wednesday)

Introduction to Statistics for the Social Sciences SBS200, COMM200, GEOG200, PA200, POL200, or SOC200 Lecture Section 001, Fall 2015 Room 150 Harvill.

Chapter 12: Correlation and Linear Regression 1.

1 Chapter 9. Section 9-1 and 9-2. Triola, Elementary Statistics, Eighth Edition. Copyright Addison Wesley Longman Correlation and Regression.

Section Copyright © 2014, 2012, 2010 Pearson Education, Inc. Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series.

Correlation and Regression

CHAPTER 3 Describing Relationships

AP Statistics Chapter 14 Section 1.

Political Science 30: Political Inquiry

Chapter 5 LSRL.

CHAPTER 10 Correlation and Regression (Objectives)

2. Find the equation of line of regression

Simple Linear Regression

Correlation and Regression

Correlation & Trend Lines

Correlation and Regression

Presentation transcript:

Click to edit Master title style Midterm 3 Wednesday, June 10, 1:10pm

Click to edit Master title style Research Concepts

Click to edit Master title style Regression: Independent and Dependent Variables

Click to edit Master title style Regression Instead of deciding whether the independent variable has "some effect" on your dependent variable, we wish to see how well we can predict the dependent variable (response variable) from the independent variable (explanatory variable). Of course, if the independent variable has no effect on the dependent variable, then the independent variable will NOT help us predict the dependent variable. If the independent variable has a strong effect on the dependent variable, then we will be very GOOD at predicting the dependent variable from the independent variable.

Click to edit Master title style

Linear (strong correlation) Linear (weak correlation) Linear (strong correlation) Non-linear (strong correlation) Non-linear (strong correlation) Non-linear (no correlation) No Relationship (no correlation)

Click to edit Master title style The ‘Coefficient of Correlation’, r How close are the datapoints in the scatterplot to the best-fitting regression line?

Click to edit Master title style The Coefficient of Correlation Statistic (r) r is a value between -1 and 1. r = 1: Perfect Positive Linear Correlation r = -1: Perfect Negative Linear Correlation r = 0: No Linear Correlation

Click to edit Master title style What Do Correlations Tell Us? Correlations allow us to predict one score from another (using the "regression equation"). Good prediction doesn't always require understanding why there is a relationship. Correlation does not imply causation! Correlations are often due to coincidences or common cause factors.

Click to edit Master title style Correlation ≠ Causality r = 0.98

Regression Hypotheses Null Hypothesis: Null Hypothesis: H 0 : β 1 = 0 H 0 : β 1 = 0 Alternative Hypothesis: Alternative Hypothesis: H 1 : β 1 ≠ 0 H 1 : β 1 ≠ 0

Estimated Parameters From your data, we will get an estimate of β 1. We will call this estimate B 1. From your data, we will get an estimate of β 0. We will call this estimate B 0.

Click to edit Master title style Estimated Parameters Slope Intercept (Do not memorize this formula)

Click to edit Master title style Predicting Scores B 0 = B 1 =Y = If Mr. Bob's X score is what is his predicted score? What is the deviation score? Y = ^ Y - Y = ^ Intercept Slope