Section 4.2. Correlation and Regression Describe only linear relationship. Strongly influenced by extremes in data. Always plot data first. Extrapolation.

Slides:

Advertisements

Similar presentations

Math 243 Exam 2 Preparation In regression what is considered an influential point? Any ordered pair is considered influential, if when removed, the result.

Advertisements

Chapter 4: More on Two- Variable Data.  Correlation and Regression Describe only linear relationships Are not resistant  One influential observation.

AP Statistics Chapters 3 & 4 Measuring Relationships Between 2 Variables.

Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 3 Association: Contingency, Correlation, and Regression Section 3.4 Cautions in Analyzing.

2.4 Cautions about Correlation and Regression. Residuals (again!) Recall our discussion about residuals- what is a residual? The idea for line of best.

Chapter 2: Looking at Data - Relationships /true-fact-the-lack-of-pirates-is-causing-global-warming/

Looking at Data-Relationships 2.1 –Scatter plots.

STAT 104: Section 2 4 Oct, 2007 TF: Daniel Moon. TF Daniel Moon G2 (A.M. in Statistics Dept) Office hour:

Haroon Alam, Mitchell Sanders, Chuck McAllister- Ashley, and Arjun Patel.

C HAPTER 4: M ORE ON T WO V ARIABLE D ATA Sec. 4.2 – Cautions about Correlation and Regression.

ASSOCIATION: CONTINGENCY, CORRELATION, AND REGRESSION Chapter 3.

2.4: Cautions about Regression and Correlation. Cautions: Regression & Correlation Correlation measures only linear association. Extrapolation often produces.

Looking at data: relationships - Caution about correlation and regression - The question of causation IPS chapters 2.4 and 2.5 © 2006 W. H. Freeman and.

MAT 1000 Mathematics in Today's World. Last Time.

The Practice of Statistics Third Edition Chapter 4: More about Relationships between Two Variables Copyright © 2008 by W. H. Freeman & Company Daniel S.

Notes Bivariate Data Chapters Bivariate Data Explores relationships between two quantitative variables.

Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.

Chapter 15 Describing Relationships: Regression, Prediction, and Causation Chapter 151.

 Graph of a set of data points  Used to evaluate the correlation between two variables.

4.3: Establishing Causation Both correlation and regression are very useful in describing the relationship between two variables; however, they are first.

Notes Bivariate Data Chapters Bivariate Data Explores relationships between two quantitative variables.

Topic 10 - Linear Regression Least squares principle - pages 301 – – 309 Hypothesis tests/confidence intervals/prediction intervals for regression.

Chapter 4 Describing the Relation Between Two Variables 4.1 Scatter Diagrams; Correlation.

Chapter 4 More on Two-Variable Data “Each of us is a statistical impossibility around which hover a million other lives that were never destined to be.

Chapter 4 More on Two-Variable Data YMS 4.1 Transforming Relationships.

WARM-UP Do the work on the slip of paper (handout)

CHAPTER 5 Regression BPS - 5TH ED.CHAPTER 5 1. PREDICTION VIA REGRESSION LINE NUMBER OF NEW BIRDS AND PERCENT RETURNING BPS - 5TH ED.CHAPTER 5 2.

Chapter 5 Regression. u Objective: To quantify the linear relationship between an explanatory variable (x) and response variable (y). u We can then predict.

AP STATISTICS LESSON 4 – 2 ( DAY 1 ) Cautions About Correlation and Regression.

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 – Correlation and Regression before: examined relationship among 1 variable (test grades, metabolism, trip time to work, etc.) now: will examine.

Chapter 4: Correlation and Regression 4.1 – Scatter Diagrams and Linear Correlation 4.2 – Linear Regression and the Coefficient of Determinant.

Lesson Correlation and Regression Wisdom. Knowledge Objectives Recall the three limitations on the use of correlation and regression. Explain what.

C ORRELATION V S. C AUSATION 4.2 C AUTIONS ABOUT C ORRELATION AND R EGRESSION Correlation and Regression ONLY describe only linear relationships r and.

Chapter 2 Examining Relationships.  Response variable measures outcome of a study (dependent variable)  Explanatory variable explains or influences.

Business Statistics for Managerial Decision Making

Describing Relationships

1. Plot the data. What kind of growth does it exhibit? (plot by hand but you may use calculators to confirm answers.) 2. Use logs to transform the data.

UNIT 4 Bivariate Data Scatter Plots and Regression.

Section 1.6 Fitting Linear Functions to Data. Consider the set of points {(3,1), (4,3), (6,6), (8,12)} Plot these points on a graph –This is called a.

Cautions About Correlation and Regression Section 4.2.

Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.

1 Regression Line Part II Class Class Objective After this class, you will be able to -Evaluate Regression and Correlation Difficulties and Disasters.

Scatterplots and Correlation Section 3.1 Part 2 of 2 Reference Text: The Practice of Statistics, Fourth Edition. Starnes, Yates, Moore.

Chapters 8 Linear Regression. Correlation and Regression Correlation = linear relationship between two variables. Summarize relationship with line. Called.

AP Statistics. Issues Interpreting Correlation and Regression  Limitations for r, r 2, and LSRL :  Can only be used to describe linear relationships.

Cautions About Correlation and Regression Section 4.2

Sections Review.

Cautions About Correlation and Regression

Lesson 13: Things To Watch out for

Cautions about Correlation and Regression

Regression Wisdom Chapter 9.

AP STAT Section 3.3: Correlation and Regression Wisdom

DRILL Put these correlations in order from strongest to weakest.

Unit 3 – Linear regression

Cautions about Correlation and Regression

Looking at data: relationships - Caution about correlation and regression - The question of causation IPS chapters 2.4 and 2.5 © 2006 W. H. Freeman and.

Least-Squares Regression

Does Association Imply Causation?

4.2 Cautions about Correlation and Regression

Math 243 Exam 2 Preparation.

Correlation/regression using averages

3.3 Cautions Correlation and Regression Wisdom Correlation and regression describe ONLY LINEAR relationships Extrapolations (using data to.

Chapters Important Concepts and Terms

Chapter 3.2 Regression Wisdom.

Cases. Simple Regression Linear Multiple Regression.

Statistics 101 CORRELATION Section 3.2.

Chapter 9 Regression Wisdom.

Chapter 4: More on Two-Variable Data

Correlation/regression using averages

Presentation transcript:

Section 4.2

Correlation and Regression Describe only linear relationship. Strongly influenced by extremes in data. Always plot data first. Extrapolation – Use of regression line or curve outside the values of the domain of explanatory variable.

Averaged Data Correlations based on averages, not actual data, are usually too high. Smooths out data. Does not allow for scatter among individuals.

Lurking Variables Variables that influence the two studied variables but are not in the study. Can falsely suggest a strong relationship. Can hide a relationship. Ex: Herbal tea in nursing homes/Ice cream drowning.

Association does not imply Causation Strong associations cause/effect relation. Causation – Change in x causes change in y. Common Response – Both x and y respond to changes in some unobserved variable. Confounding – The effect on y of x is mixed up with effects on y of other variables.

Lurking Variables

Experiments Best way to get good evidence that x causes y. Only x is changed, while lurking variables are controlled.