Which of the following would be necessary to establish a cause-and- effect relationship between two variables? Strong association between the variables.

Slides:



Advertisements
Similar presentations
Lesson 10: Linear Regression and Correlation
Advertisements

Regresi Linear Sederhana Pertemuan 01 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Regression BPS chapter 5 © 2006 W.H. Freeman and Company.
Simple Linear Regression. Start by exploring the data Construct a scatterplot  Does a linear relationship between variables exist?  Is the relationship.
Chapter 8 Linear Regression © 2010 Pearson Education 1.
AP Statistics Chapters 3 & 4 Measuring Relationships Between 2 Variables.
Relationships Between Quantitative Variables
Basic Statistical Concepts
Chapter Topics Types of Regression Models
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.
Stat 112: Lecture 9 Notes Homework 3: Due next Thursday
Basic Practice of Statistics - 3rd Edition
Correlation and Linear Regression
Chapter 5 Regression. Chapter 51 u Objective: To quantify the linear relationship between an explanatory variable (x) and response variable (y). u We.
Linear Regression and Correlation
1 10. Causality and Correlation ECON 251 Research Methods.
Lecture 22 Dustin Lueker.  The sample mean of the difference scores is an estimator for the difference between the population means  We can now use.
Regression Analysis. Scatter plots Regression analysis requires interval and ratio-level data. To see if your data fits the models of regression, it is.
MEASURES of CORRELATION. CORRELATION basically the test of measurement. Means that two variables tend to vary together The presence of one indicates the.
Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition.
Chapter 15 Describing Relationships: Regression, Prediction, and Causation Chapter 151.
4.3: Establishing Causation Both correlation and regression are very useful in describing the relationship between two variables; however, they are first.
BPS - 3rd Ed. Chapter 51 Regression. BPS - 3rd Ed. Chapter 52 u Objective: To quantify the linear relationship between an explanatory variable (x) and.
BPS - 5th Ed. Chapter 51 Regression. BPS - 5th Ed. Chapter 52 u Objective: To quantify the linear relationship between an explanatory variable (x) and.
Review Multiple Choice Regression: Chapters 7, 8, 9.
Regression BPS chapter 5 © 2010 W.H. Freeman and Company.
Stat 112 Notes 9 Today: –Multicollinearity (Chapter 4.6) –Multiple regression and causal inference.
AP STATISTICS LESSON 4 – 2 ( DAY 1 ) Cautions About Correlation and Regression.
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.
Chapter 4 – Correlation and Regression before: examined relationship among 1 variable (test grades, metabolism, trip time to work, etc.) now: will examine.
© 2001 Prentice-Hall, Inc.Chap 13-1 BA 201 Lecture 18 Introduction to Simple Linear Regression (Data)Data.
Simple Linear Regression The Coefficients of Correlation and Determination Two Quantitative Variables x variable – independent variable or explanatory.
BPS - 3rd Ed. Chapter 51 Regression. BPS - 3rd Ed. Chapter 52 u To describe the change in Y per unit X u To predict the average level of Y at a given.
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.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Linear Regression and Correlation Chapter 13.
MATH 2311 Section 5.2 & 5.3. Correlation Coefficient.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
Chapter 5: 02/17/ Chapter 5 Regression. 2 Chapter 5: 02/17/2004 Objective: To quantify the linear relationship between an explanatory variable (x)
Two-Variable Statistics. Correlation  A relationship between two variables  As one goes up, the other changes in a predictable way (either mostly goes.
Section 3.3 Day 3.
Practice As part of a program to reducing smoking, a national organization ran an advertising campaign to convince people to quit or reduce their smoking.
Regression Analysis.
Selecting the Best Measure for Your Study
Essential Statistics Regression
Does adding a fuel additive help gasoline mileage in automobiles?
Cautions About Correlation and Regression
Regression Analysis.
Cautions about Correlation and Regression
Regression BPS 7e Chapter 5 © 2015 W. H. Freeman and Company.
The Least-Squares Regression Line
Simple Linear Regression
Correlation and Simple Linear Regression
a= b= WARM - UP Variable Coef StDev T P
EQ: How well does the line fit the data?
The Weather Turbulence
Correlation and Regression
Unit 4 Vocabulary.
Correlation and Simple Linear Regression
Cautions about Correlation and Regression
Basic Practice of Statistics - 5th Edition Regression
Review of Chapter 3 Examining Relationships
Multiple Linear Regression
Basic Practice of Statistics - 3rd Edition Regression
Algebra Review The equation of a straight line y = mx + b
Basic Practice of Statistics - 3rd Edition Lecture Powerpoint
Descriptive Statistics Univariate Data
Homework: PG. 204 #30, 31 pg. 212 #35,36 30.) a. Reading scores are predicted to increase by for each one-point increase in IQ. For x=90: 45.98;
Honors Statistics Review Chapters 7 & 8
Presentation transcript:

Which of the following would be necessary to establish a cause-and- effect relationship between two variables? Strong association between the variables.   An association between the variables in many different settings. The alleged cause is plausible.   All of the above.

When possible, what is the best way to establish that an observed association is the result of a cause-and-effect relationship? Study the least-squares regression line.   Obtain the correlation coefficient.   Use a well-designed experiment.   Examine z-scores rather than the original variables.

What is one of the main reasons it is difficult to conclude a cause-and- effect relationship? Extrapolation of the data   Common responses   Lurking variables High values of the coefficient of determination

The regression line to predict average exam grade from hours of study is . The slope of the regression line indicates . For any student, an extra hour of study is predicted to increase the grade 5.6 points. on average, an extra hour of study is predicted to increase the grade 5.6 points. an extra hour of study will increase the grade 15 points

A store manager conducted an experiment in which he systematically varied the width of a display for toothpaste from 3 ft. to 6 ft. and recorded the corresponding number of tubes of toothpaste sold per day. The data was used to fit a regression line, which was   tubes sold per day = 20+10(display width) What is the predicted number of tubes sold per day for a display width of 12 feet? 120 140 Neither

A researcher examines data from all cities with populations over 100,000 in the United States. He notices that those cities that have a major league baseball team tend to have a greater number of divorces than other cities. One can reasonably conclude that the presence of a major league baseball team contributes to divorce. Men spend time at the ballpark at the expense of their marriage. this correlation cannot be explained and is probably accidental. Cities with major league baseball teams should have no more divorces than other cities. none of the above.

Answers: D, C, C, B, C (12 feet is far away from the data), C (the lurking variable is “population of the city”)