Chapter 6 Scatterplots, Association and Correlation
|
|
- Dorthy Rodgers
- 6 years ago
- Views:
Transcription
1 Chapter 6 Scatterplots, Association and Correlation
2 Looking for Correlation Example Does the number of hours you watch TV per week impact your average grade in a class? Hours Grade
3 Looking for Correlation Example Does the number of hours you watch TV per week impact your average grade in a class? Hours Grade To see if there is a relationship, we will create a scatterplot and analyze it. Definition A scatterplot is a geographical representation between two quantitative variables. They may be from the same individual (i.e. education v. income, height v. weight) or from paired individuals (i.e. age of partners in a relationship).
4 Scatterplots When working with scatterplots, there are two variables. They may be two different types. Definition A response variable measures the outcome of a study.
5 Scatterplots When working with scatterplots, there are two variables. They may be two different types. Definition A response variable measures the outcome of a study. Definition An explanatory variable may explain or influence changes in a response variable.
6 Scatterplots When working with scatterplots, there are two variables. They may be two different types. Definition A response variable measures the outcome of a study. Definition An explanatory variable may explain or influence changes in a response variable. Explanatory variables are often called independent and are on the x-axis. Response variables are often called dependent and are on the y-axis.
7 Back to Our Example In our example, which is the explanatory variable?
8 Back to Our Example In our example, which is the explanatory variable? Watched TV hours.
9 Back to Our Example In our example, which is the explanatory variable? Watched TV hours. The response variable is there for the average grade. So the question we are trying to answer is Does watching TV influence the average grade in a class?
10 Back to Our Example In our example, which is the explanatory variable? Watched TV hours. The response variable is there for the average grade. So the question we are trying to answer is Does watching TV influence the average grade in a class? Let s plot the data and see what we have.
11 The Scatterplot Grades v. Hours of TV Grade Hours of TV
12 How Does the Relationship Look? What do we think?
13 How Does the Relationship Look? What do we think? It looks like the more hours of TV that are watched, the lower the average grade. But how good is the relationship? We can measure this in different ways. One is direction (+, ) and another is by ranking the strength. These are both accomplished by looking at the correlation coefficient.
14 Facts About Correlation Coefficients: 1 1 r 1. The least correlation is 0 and the best correlation is ±1. Whether r is positive or negative only tells us which direction the relationship goes - whether y increases as x increases or if y decreases as x increases. Being negative is not bad.
15 Facts About Correlation Coefficients: 1 1 r 1. The least correlation is 0 and the best correlation is ±1. Whether r is positive or negative only tells us which direction the relationship goes - whether y increases as x increases or if y decreases as x increases. Being negative is not bad. 2 Correlation makes no distinction between x and y, that is, between the choice of explanatory and response variables. We need to make sure we are careful, though, as the next part (regression line) depends heavily on the correct choice.
16 Facts About Correlation Coefficients: 1 1 r 1. The least correlation is 0 and the best correlation is ±1. Whether r is positive or negative only tells us which direction the relationship goes - whether y increases as x increases or if y decreases as x increases. Being negative is not bad. 2 Correlation makes no distinction between x and y, that is, between the choice of explanatory and response variables. We need to make sure we are careful, though, as the next part (regression line) depends heavily on the correct choice. 3 Correlation measures only the linear relationship.
17 Facts About Correlation Coefficients: 1 1 r 1. The least correlation is 0 and the best correlation is ±1. Whether r is positive or negative only tells us which direction the relationship goes - whether y increases as x increases or if y decreases as x increases. Being negative is not bad. 2 Correlation makes no distinction between x and y, that is, between the choice of explanatory and response variables. We need to make sure we are careful, though, as the next part (regression line) depends heavily on the correct choice. 3 Correlation measures only the linear relationship. 4 Correlation is not resistant.
18 Facts About Correlation Coefficients: 1 1 r 1. The least correlation is 0 and the best correlation is ±1. Whether r is positive or negative only tells us which direction the relationship goes - whether y increases as x increases or if y decreases as x increases. Being negative is not bad. 2 Correlation makes no distinction between x and y, that is, between the choice of explanatory and response variables. We need to make sure we are careful, though, as the next part (regression line) depends heavily on the correct choice. 3 Correlation measures only the linear relationship. 4 Correlation is not resistant. 5 Correlation has no units.
19 So How Do We Find This Correlation Coefficient? The Correlation Coefficient r = 1 ( ) ( ) x i x yi y n 1 S x S y = 1 n 1 zx z y
20 So How Do We Find This Correlation Coefficient? The Correlation Coefficient r = 1 ( ) ( ) x i x yi y n 1 S x S y = 1 n 1 zx z y Let s find the correlation coefficient for our example. First, we need a few values, x, y, S x, S y.
21 So How Do We Find This Correlation Coefficient? The Correlation Coefficient r = 1 ( ) ( ) x i x yi y n 1 S x S y = 1 n 1 zx z y Let s find the correlation coefficient for our example. First, we need a few values, x, y, S x, S y. x = y = S x = S y = 8.971
22 Finding the Correlation Coefficient For each pair, find the z-score for each value. Then multiply them together. After summing, divide by n 1. i z x z y product
23 Finding the Correlation Coefficient For each pair, find the z-score for each value. Then multiply them together. After summing, divide by n 1. i z x z y product
24 Finding the Correlation Coefficient For each pair, find the z-score for each value. Then multiply them together. After summing, divide by n 1. i z x z y product r = 1 ( ) =
25 Finding the Correlation Coefficient For each pair, find the z-score for each value. Then multiply them together. After summing, divide by n 1. i z x z y product r = 1 ( ) = Interpretation: Moderate negative correlation
26 So Can We Say There Is A Relationship? So, can we say that there is a direct relationship between the number of hours of TV watched and the average grade? Not so fast...
27 So Can We Say There Is A Relationship? So, can we say that there is a direct relationship between the number of hours of TV watched and the average grade? Not so fast... Correlation does not necessarily imply causation.
28 So Can We Say There Is A Relationship? So, can we say that there is a direct relationship between the number of hours of TV watched and the average grade? Not so fast... Correlation does not necessarily imply causation. Just because it looks the part does not mean we have evidence that there is a relationship. We have to consider a couple of other things. One is lurking variables. These are variables that may be present but we are not actually considering them within the data.
29 So Can We Say There Is A Relationship? So, can we say that there is a direct relationship between the number of hours of TV watched and the average grade? Not so fast... Correlation does not necessarily imply causation. Just because it looks the part does not mean we have evidence that there is a relationship. We have to consider a couple of other things. One is lurking variables. These are variables that may be present but we are not actually considering them within the data. Can you think of any lurking variables that would impact our example?
30 Significance We also need to test for significance to see what is going on. If r n > 3, the correlation is significant Otherwise it is not significant
31 Significance We also need to test for significance to see what is going on. If r n > 3, the correlation is significant Otherwise it is not significant The smaller this value, the smaller the probability that the correlation will be significant.
32 Significance We also need to test for significance to see what is going on. If r n > 3, the correlation is significant Otherwise it is not significant The smaller this value, the smaller the probability that the correlation will be significant. Reasons why data may not be significant: 1 Genuine lack of correlation
33 Significance We also need to test for significance to see what is going on. If r n > 3, the correlation is significant Otherwise it is not significant The smaller this value, the smaller the probability that the correlation will be significant. Reasons why data may not be significant: 1 Genuine lack of correlation 2 Not enough data
34 Significance We also need to test for significance to see what is going on. If r n > 3, the correlation is significant Otherwise it is not significant The smaller this value, the smaller the probability that the correlation will be significant. Reasons why data may not be significant: 1 Genuine lack of correlation 2 Not enough data Our example is not significant because of quantity. So we cannot consider that watching TV has a direct impact on grades.
35 Assumptions and Conditions for Correlation Quantitative Variables Condition Don t make the common error of calling an association involving a categorical variable a correlation. Correlation is only about quantitative variables.
36 Assumptions and Conditions for Correlation Quantitative Variables Condition Don t make the common error of calling an association involving a categorical variable a correlation. Correlation is only about quantitative variables. Straight Enough Condition The best check for the assumption that the variables are truly linearly related is to look at the scatterplot to see whether it looks reasonably straight. That s a judgment call, but not a difficult one.
37 Assumptions and Conditions for Correlation Quantitative Variables Condition Don t make the common error of calling an association involving a categorical variable a correlation. Correlation is only about quantitative variables. Straight Enough Condition The best check for the assumption that the variables are truly linearly related is to look at the scatterplot to see whether it looks reasonably straight. That s a judgment call, but not a difficult one. No Outliers Condition Outliers can distort the correlation dramatically, making a weak association look strong or a strong one look weak. Outliers can even change the sign of the correlation. But it s easy to see outlier in the scatterplot, so to check this condition, just look.
38 Another Example Example The following gives the power numbers for the starting 9 for the 2007 Boston Red Sox. Is there relationship between the number of home runs and the number of RBIs? Does the number of home runs affect the number of RBIs? Produce a scatterplot and discuss the correlation. Player Home Runs RBIs Varitek Youkilis Pedroia 8 50 Lowell Lugo 8 73 Ramirez Crisp 6 60 Drew Ortiz
39 Red Sox Example Which is the response variable? Which is the response variable?
40 Red Sox Example Which is the response variable? Which is the response variable? Since we are asking if HR affects RBIs, HR would be the explanatory variable and therefore x. So RBIs is the y variable Red Sox Power Numbers RBIs Home Runs
41 Before We Go On Something to notice: we have two values with the same x-coordinate Red Sox Power Numbers 120 RBIs Home Runs
42 Finding the Correlation Coefficient What is our guess as to the correlation?
43 Finding the Correlation Coefficient What is our guess as to the correlation? Now let s find the correlation coefficient. But there must be an easier way... and that way would be technology.
44 Finding the Correlation Coefficient What is our guess as to the correlation? Now let s find the correlation coefficient. But there must be an easier way... and that way would be technology. Input data in usual way, with explanatory variable under L 1 and response variable under L 2
45 Finding the Correlation Coefficient What is our guess as to the correlation? Now let s find the correlation coefficient. But there must be an easier way... and that way would be technology. Input data in usual way, with explanatory variable under L 1 and response variable under L 2 Press STAT and scroll to TESTS
46 Finding the Correlation Coefficient What is our guess as to the correlation? Now let s find the correlation coefficient. But there must be an easier way... and that way would be technology. Input data in usual way, with explanatory variable under L 1 and response variable under L 2 Press STAT and scroll to TESTS Select LinRegTTest
47 Finding the Correlation Coefficient What is our guess as to the correlation? Now let s find the correlation coefficient. But there must be an easier way... and that way would be technology. Input data in usual way, with explanatory variable under L 1 and response variable under L 2 Press STAT and scroll to TESTS Select LinRegTTest Make sure the XList and YList are the lists where the data for the explanatory and response variables are located, respectively
48 Finding the Correlation Coefficient What is our guess as to the correlation? Now let s find the correlation coefficient. But there must be an easier way... and that way would be technology. Input data in usual way, with explanatory variable under L 1 and response variable under L 2 Press STAT and scroll to TESTS Select LinRegTTest Make sure the XList and YList are the lists where the data for the explanatory and response variables are located, respectively Press Calculate and scroll to find r and r 2
49 Using Technology For our example, we have
50 Using Technology For our example, we have r r
51 Using Technology For our example, we have r r So the correlation coefficient tells us that there is a strong positive correlation.
52 What Does r 2 Tell Us? r 2 tells us how much better our predictions will be if we go through the trouble to find the regression line rather than just make our predictions with the means. Ours is pretty good here, indicating that we should find the regression line Red Sox Power Numbers RBIs Home Runs
53 Technology and Scatterplots We can also create a scatterplot on the calculator.
54 Technology and Scatterplots We can also create a scatterplot on the calculator. Make sure there are no functions in the grapher (press Y= to check)
55 Technology and Scatterplots We can also create a scatterplot on the calculator. Make sure there are no functions in the grapher (press Y= to check) Input the data in the usual way (we already have it there for this example)
56 Technology and Scatterplots We can also create a scatterplot on the calculator. Make sure there are no functions in the grapher (press Y= to check) Input the data in the usual way (we already have it there for this example) Press 2 nd and Y= to get into the STAT PLOT menu
57 Technology and Scatterplots We can also create a scatterplot on the calculator. Make sure there are no functions in the grapher (press Y= to check) Input the data in the usual way (we already have it there for this example) Press 2 nd and Y= to get into the STAT PLOT menu Make sure only the plot we want is turned on
58 Technology and Scatterplots We can also create a scatterplot on the calculator. Make sure there are no functions in the grapher (press Y= to check) Input the data in the usual way (we already have it there for this example) Press 2 nd and Y= to get into the STAT PLOT menu Make sure only the plot we want is turned on Select the first graph in the first row and then make sure the XList and YList are correct
59 Technology and Scatterplots We can also create a scatterplot on the calculator. Make sure there are no functions in the grapher (press Y= to check) Input the data in the usual way (we already have it there for this example) Press 2 nd and Y= to get into the STAT PLOT menu Make sure only the plot we want is turned on Select the first graph in the first row and then make sure the XList and YList are correct Press ZOOM 9
60 One More Example Example There is some evidence that drinking moderate amounts of wine helps prevent heart attacks. The accompanying table gives data on yearly wine consumption (in liters of alcohol from drinking wine per person) and yearly deaths from heart disease (per 100,000 people) in 19 developing nations. Construct a scatterplot and describe what you see. Country Alcohol Deaths County Alcohol Deaths Australia Austria Belgium Canada Denmark Finland France Iceland Ireland Italy Netherlands New Zealand Norway Spain Sweden 1, Switzerland United Kingdom United States West Germany
61 The Scatterplot Heart Disease v. Alcohol from Wine Deaths (per 100,000) Alcohol from Wine (in liters)
62 The Scatterplot Heart Disease v. Alcohol from Wine Deaths (per 100,000) Alcohol from Wine (in liters) r =.8428, strong negative correlation
63 The Scatterplot Heart Disease v. Alcohol from Wine Deaths (per 100,000) Alcohol from Wine (in liters) r =.8428, strong negative correlation r 2 =.7103, worthwhile to find linear regression line
Chapter 4 Data with Two Variables
Chapter 4 Data with Two Variables 1 Scatter Plots and Correlation and 2 Pearson s Correlation Coefficient Looking for Correlation Example Does the number of hours you watch TV per week impact your average
More informationChapter 4 Data with Two Variables
Chapter 4 Data with Two Variables 1 Scatter Plots and Correlation and 2 Pearson s Correlation Coefficient Looking for Correlation Example Does the number of hours you watch TV per week impact your average
More informationSTATISTICS Relationships between variables: Correlation
STATISTICS 16 Relationships between variables: Correlation The gentleman pictured above is Sir Francis Galton. Galton invented the statistical concept of correlation and the use of the regression line.
More informationCh. 3 Review - LSRL AP Stats
Ch. 3 Review - LSRL AP Stats Multiple Choice Identify the choice that best completes the statement or answers the question. Scenario 3-1 The height (in feet) and volume (in cubic feet) of usable lumber
More informationAP Statistics Two-Variable Data Analysis
AP Statistics Two-Variable Data Analysis Key Ideas Scatterplots Lines of Best Fit The Correlation Coefficient Least Squares Regression Line Coefficient of Determination Residuals Outliers and Influential
More informationIf the roles of the variable are not clear, then which variable is placed on which axis is not important.
Chapter 6 - Scatterplots, Association, and Correlation February 6, 2015 In chapter 6-8, we look at ways to compare the relationship of 2 quantitative variables. First we will look at a graphical representation,
More informationBivariate Data Summary
Bivariate Data Summary Bivariate data data that examines the relationship between two variables What individuals to the data describe? What are the variables and how are they measured Are the variables
More informationThe empirical ( ) rule
The empirical (68-95-99.7) rule With a bell shaped distribution, about 68% of the data fall within a distance of 1 standard deviation from the mean. 95% fall within 2 standard deviations of the mean. 99.7%
More informationDescribing Bivariate Relationships
Describing Bivariate Relationships Bivariate Relationships What is Bivariate data? When exploring/describing a bivariate (x,y) relationship: Determine the Explanatory and Response variables Plot the data
More informationLearning Objectives. Math Chapter 3. Chapter 3. Association. Response and Explanatory Variables
ASSOCIATION: CONTINGENCY, CORRELATION, AND REGRESSION Chapter 3 Learning Objectives 3.1 The Association between Two Categorical Variables 1. Identify variable type: Response or Explanatory 2. Define Association
More information5.1 Bivariate Relationships
Chapter 5 Summarizing Bivariate Data Source: TPS 5.1 Bivariate Relationships What is Bivariate data? When exploring/describing a bivariate (x,y) relationship: Determine the Explanatory and Response variables
More informationChapter 6. September 17, Please pick up a calculator and take out paper and something to write with. Association and Correlation.
Please pick up a calculator and take out paper and something to write with. Sep 17 8:08 AM Chapter 6 Scatterplots, Association and Correlation Copyright 2015, 2010, 2007 Pearson Education, Inc. Chapter
More informationBIVARIATE DATA data for two variables
(Chapter 3) BIVARIATE DATA data for two variables INVESTIGATING RELATIONSHIPS We have compared the distributions of the same variable for several groups, using double boxplots and back-to-back stemplots.
More informationScatterplots and Correlation
Chapter 4 Scatterplots and Correlation 2/15/2019 Chapter 4 1 Explanatory Variable and Response Variable Correlation describes linear relationships between quantitative variables X is the quantitative explanatory
More informationCorrelation & Regression
Correlation & Regression Correlation It is critical that when "interpreting" the association between 2 variables via a scatterplot, to employ "weasel words" such as in general and on average and tends
More information3.1 Scatterplots and Correlation
3.1 Scatterplots and Correlation Most statistical studies examine data on more than one variable. In many of these settings, the two variables play different roles. Explanatory variable (independent) predicts
More informationChapter 7. Scatterplots, Association, and Correlation
Chapter 7 Scatterplots, Association, and Correlation Bin Zou (bzou@ualberta.ca) STAT 141 University of Alberta Winter 2015 1 / 29 Objective In this chapter, we study relationships! Instead, we investigate
More information4.1 Introduction. 4.2 The Scatter Diagram. Chapter 4 Linear Correlation and Regression Analysis
4.1 Introduction Correlation is a technique that measures the strength (or the degree) of the relationship between two variables. For example, we could measure how strong the relationship is between people
More informationChapter 3: Examining Relationships
Chapter 3: Examining Relationships Most statistical studies involve more than one variable. Often in the AP Statistics exam, you will be asked to compare two data sets by using side by side boxplots or
More informationUnit 9 Regression and Correlation Homework #14 (Unit 9 Regression and Correlation) SOLUTIONS. X = cigarette consumption (per capita in 1930)
BIOSTATS 540 Fall 2015 Introductory Biostatistics Page 1 of 10 Unit 9 Regression and Correlation Homework #14 (Unit 9 Regression and Correlation) SOLUTIONS Consider the following study of the relationship
More informationChapter 7 Summary Scatterplots, Association, and Correlation
Chapter 7 Summary Scatterplots, Association, and Correlation What have we learned? We examine scatterplots for direction, form, strength, and unusual features. Although not every relationship is linear,
More informationThis document contains 3 sets of practice problems.
P RACTICE PROBLEMS This document contains 3 sets of practice problems. Correlation: 3 problems Regression: 4 problems ANOVA: 8 problems You should print a copy of these practice problems and bring them
More informationMath 243 OpenStax Chapter 12 Scatterplots and Linear Regression OpenIntro Section and
Math 243 OpenStax Chapter 12 Scatterplots and Linear Regression OpenIntro Section 2.1.1 and 8.1-8.2.6 Overview Scatterplots Explanatory and Response Variables Describing Association The Regression Equation
More informationChapter 7. Scatterplots, Association, and Correlation. Copyright 2010 Pearson Education, Inc.
Chapter 7 Scatterplots, Association, and Correlation Copyright 2010 Pearson Education, Inc. Looking at Scatterplots Scatterplots may be the most common and most effective display for data. In a scatterplot,
More informationAP Statistics. Chapter 6 Scatterplots, Association, and Correlation
AP Statistics Chapter 6 Scatterplots, Association, and Correlation Objectives: Scatterplots Association Outliers Response Variable Explanatory Variable Correlation Correlation Coefficient Lurking Variables
More informationAP Statistics S C A T T E R P L O T S, A S S O C I A T I O N, A N D C O R R E L A T I O N C H A P 6
AP Statistics 1 S C A T T E R P L O T S, A S S O C I A T I O N, A N D C O R R E L A T I O N C H A P 6 The invalid assumption that correlation implies cause is probably among the two or three most serious
More informationExample: Can an increase in non-exercise activity (e.g. fidgeting) help people gain less weight?
Example: Can an increase in non-exercise activity (e.g. fidgeting) help people gain less weight? 16 subjects overfed for 8 weeks Explanatory: change in energy use from non-exercise activity (calories)
More informationProb/Stats Questions? /32
Prob/Stats 10.4 Questions? 1 /32 Prob/Stats 10.4 Homework Apply p551 Ex 10-4 p 551 7, 8, 9, 10, 12, 13, 28 2 /32 Prob/Stats 10.4 Objective Compute the equation of the least squares 3 /32 Regression A scatter
More informationLinear Regression. Al Nosedal University of Toronto. Summer Al Nosedal University of Toronto Linear Regression Summer / 115
Linear Regression Al Nosedal University of Toronto Summer 2017 Al Nosedal University of Toronto Linear Regression Summer 2017 1 / 115 My momma always said: Life was like a box of chocolates. You never
More informationLecture 4 Scatterplots, Association, and Correlation
Lecture 4 Scatterplots, Association, and Correlation Previously, we looked at Single variables on their own One or more categorical variable In this lecture: We shall look at two quantitative variables.
More informationLecture 4 Scatterplots, Association, and Correlation
Lecture 4 Scatterplots, Association, and Correlation Previously, we looked at Single variables on their own One or more categorical variables In this lecture: We shall look at two quantitative variables.
More informationLooking at Data Relationships. 2.1 Scatterplots W. H. Freeman and Company
Looking at Data Relationships 2.1 Scatterplots 2012 W. H. Freeman and Company Here, we have two quantitative variables for each of 16 students. 1) How many beers they drank, and 2) Their blood alcohol
More informationScatterplots and Correlations
Scatterplots and Correlations Section 4.1 1 New Definitions Explanatory Variable: (independent, x variable): attempts to explain observed outcome. Response Variable: (dependent, y variable): measures outcome
More informationSTA Module 5 Regression and Correlation. Learning Objectives. Learning Objectives (Cont.) Upon completing this module, you should be able to:
STA 2023 Module 5 Regression and Correlation Learning Objectives Upon completing this module, you should be able to: 1. Define and apply the concepts related to linear equations with one independent variable.
More informationChapter 8. Linear Regression /71
Chapter 8 Linear Regression 1 /71 Homework p192 1, 2, 3, 5, 7, 13, 15, 21, 27, 28, 29, 32, 35, 37 2 /71 3 /71 Objectives Determine Least Squares Regression Line (LSRL) describing the association of two
More informationSteps to take to do the descriptive part of regression analysis:
STA 2023 Simple Linear Regression: Least Squares Model Steps to take to do the descriptive part of regression analysis: A. Plot the data on a scatter plot. Describe patterns: 1. Is there a strong, moderate,
More informationy n 1 ( x i x )( y y i n 1 i y 2
STP3 Brief Class Notes Instructor: Ela Jackiewicz Chapter Regression and Correlation In this chapter we will explore the relationship between two quantitative variables, X an Y. We will consider n ordered
More informationChapter 6: Exploring Data: Relationships Lesson Plan
Chapter 6: Exploring Data: Relationships Lesson Plan For All Practical Purposes Displaying Relationships: Scatterplots Mathematical Literacy in Today s World, 9th ed. Making Predictions: Regression Line
More informationRelationships Regression
Relationships Regression BPS chapter 5 2006 W.H. Freeman and Company Objectives (BPS chapter 5) Regression Regression lines The least-squares regression line Using technology Facts about least-squares
More informationMathematics. Pre-Leaving Certificate Examination, Paper 2 Higher Level Time: 2 hours, 30 minutes. 300 marks L.20 NAME SCHOOL TEACHER
L.20 NAME SCHOOL TEACHER Pre-Leaving Certificate Examination, 2016 Name/vers Printed: Checked: To: Updated: Name/vers Complete Paper 2 Higher Level Time: 2 hours, 30 minutes 300 marks School stamp 3 For
More informationTalking feet: Scatterplots and lines of best fit
Talking feet: Scatterplots and lines of best fit Student worksheet What does your foot say about your height? Can you predict people s height by how long their feet are? If a Grade 10 student s foot is
More informationappstats8.notebook October 11, 2016
Chapter 8 Linear Regression Objective: Students will construct and analyze a linear model for a given set of data. Fat Versus Protein: An Example pg 168 The following is a scatterplot of total fat versus
More informationChapter 8. Linear Regression. The Linear Model. Fat Versus Protein: An Example. The Linear Model (cont.) Residuals
Chapter 8 Linear Regression Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 8-1 Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Fat Versus
More informationChapter 8. Linear Regression. Copyright 2010 Pearson Education, Inc.
Chapter 8 Linear Regression Copyright 2010 Pearson Education, Inc. Fat Versus Protein: An Example The following is a scatterplot of total fat versus protein for 30 items on the Burger King menu: Copyright
More informationObjectives. 2.3 Least-squares regression. Regression lines. Prediction and Extrapolation. Correlation and r 2. Transforming relationships
Objectives 2.3 Least-squares regression Regression lines Prediction and Extrapolation Correlation and r 2 Transforming relationships Adapted from authors slides 2012 W.H. Freeman and Company Straight Line
More information6.1.1 How can I make predictions?
CCA Ch 6: Modeling Two-Variable Data Name: Team: 6.1.1 How can I make predictions? Line of Best Fit 6-1. a. Length of tube: Diameter of tube: Distance from the wall (in) Width of field of view (in) b.
More informationChapter 12: Linear Regression and Correlation
Chapter 12: Linear Regression and Correlation Linear Equations Linear regression for two variables is based on a linear equation with one independent variable. It has the form: y = a + bx where a and b
More informationChapter 10: Comparing Two Quantitative Variables Section 10.1: Scatterplots & Correlation
Stat 300: Intro to Probability & Statistics Textbook: Introduction to Statistical Investigations Name: American River College Chapter 10: Comparing Two Quantitative Variables Section 10.1: Scatterplots
More informationeconomic growth is not conducive to a country s overall economic performance and, additionally,
WEB APPENDIX: EXAMINING THE CROSS-NATIONAL AND LONGITUDINAL VARIATION IN ECONOMIC PERFORMANCE USING FUZZY-SETS APPENDIX REASONING BEHIND THE BREAKPOINTS FOR THE SETS ECONOMIC GROWTH, EMPLOYMENT AND DEBT
More informationBusiness Statistics 41000:
Business Statistics 41000: Plotting and Summarizing Bivariate Data Drew D. Creal University of Chicago, Booth School of Business Week 2: January 17 and 18, 2014 1 Class information Drew D. Creal Email:
More informationDescribing Bivariate Data
Describing Bivariate Data Correlation Linear Regression Assessing the Fit of a Line Nonlinear Relationships & Transformations The Linear Correlation Coefficient, r Recall... Bivariate Data: data that consists
More informationCHAPTER 3 Describing Relationships
CHAPTER 3 Describing Relationships 3.1 Scatterplots and Correlation The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers Scatterplots and Correlation Learning
More informationThe Simple Linear Regression Model
The Simple Linear Regression Model Lesson 3 Ryan Safner 1 1 Department of Economics Hood College ECON 480 - Econometrics Fall 2017 Ryan Safner (Hood College) ECON 480 - Lesson 3 Fall 2017 1 / 77 Bivariate
More informationChapter 3: Describing Relationships
Chapter 3: Describing Relationships Section 3.2 The Practice of Statistics, 4 th edition For AP* STARNES, YATES, MOORE Chapter 3 Describing Relationships 3.1 Scatterplots and Correlation 3.2 Section 3.2
More informationScatterplots. 3.1: Scatterplots & Correlation. Scatterplots. Explanatory & Response Variables. Section 3.1 Scatterplots and Correlation
3.1: Scatterplots & Correlation Scatterplots A scatterplot shows the relationship between two quantitative variables measured on the same individuals. The values of one variable appear on the horizontal
More information3.2: Least Squares Regressions
3.2: Least Squares Regressions Section 3.2 Least-Squares Regression After this section, you should be able to INTERPRET a regression line CALCULATE the equation of the least-squares regression line CALCULATE
More informationThe response variable depends on the explanatory variable.
A response variable measures an outcome of study. > dependent variables An explanatory variable attempts to explain the observed outcomes. > independent variables The response variable depends on the explanatory
More informationDescribing Data: Two Variables
STAT 250 Dr. Kari Lock Morgan Describing Data: Two Variables SECTIONS 2.4, 2.5 One quantitative variable (2.4) One quantitative and one categorical (2.4) Two quantitative (2.5) z- score Which is better,
More informationChapter 3: Describing Relationships
Chapter 3: Describing Relationships Section 3.2 The Practice of Statistics, 4 th edition For AP* STARNES, YATES, MOORE Chapter 3 Describing Relationships 3.1 Scatterplots and Correlation 3.2 Section 3.2
More informationLecture 7, Chapter 7 summary
1 Lecture 7, Chapter 7 summary Scatterplots, Association, and Correlation Topic: Association between two quantitative variables Use scatterplots to see the type of association It does not matter which
More informationChapter 9. Correlation and Regression
Chapter 9 Correlation and Regression Lesson 9-1/9-2, Part 1 Correlation Registered Florida Pleasure Crafts and Watercraft Related Manatee Deaths 100 80 60 40 20 0 1991 1993 1995 1997 1999 Year Boats in
More informationdetermine whether or not this relationship is.
Section 9-1 Correlation A correlation is a between two. The data can be represented by ordered pairs (x,y) where x is the (or ) variable and y is the (or ) variable. There are several types of correlations
More informationChapter 2: Looking at Data Relationships (Part 3)
Chapter 2: Looking at Data Relationships (Part 3) Dr. Nahid Sultana Chapter 2: Looking at Data Relationships 2.1: Scatterplots 2.2: Correlation 2.3: Least-Squares Regression 2.5: Data Analysis for Two-Way
More informationSummarizing Data: Paired Quantitative Data
Summarizing Data: Paired Quantitative Data regression line (or least-squares line) a straight line model for the relationship between explanatory (x) and response (y) variables, often used to produce a
More informationRelated Example on Page(s) R , 148 R , 148 R , 156, 157 R3.1, R3.2. Activity on 152, , 190.
Name Chapter 3 Learning Objectives Identify explanatory and response variables in situations where one variable helps to explain or influences the other. Make a scatterplot to display the relationship
More informationAP Statistics L I N E A R R E G R E S S I O N C H A P 7
AP Statistics 1 L I N E A R R E G R E S S I O N C H A P 7 The object [of statistics] is to discover methods of condensing information concerning large groups of allied facts into brief and compendious
More informationSt. Gallen, Switzerland, August 22-28, 2010
Session Number: First Poster Session Time: Monday, August 23, PM Paper Prepared for the 31st General Conference of The International Association for Research in Income and Wealth St. Gallen, Switzerland,
More informationReview of Regression Basics
Review of Regression Basics When describing a Bivariate Relationship: Make a Scatterplot Strength, Direction, Form Model: y-hat=a+bx Interpret slope in context Make Predictions Residual = Observed-Predicted
More informationThe flu example from last class is actually one of our most common transformations called the log-linear model:
The Log-Linear Model The flu example from last class is actually one of our most common transformations called the log-linear model: ln Y = β 1 + β 2 X + ε We can use ordinary least squares to estimate
More informationTHE PEARSON CORRELATION COEFFICIENT
CORRELATION Two variables are said to have a relation if knowing the value of one variable gives you information about the likely value of the second variable this is known as a bivariate relation There
More informationPS2.1 & 2.2: Linear Correlations PS2: Bivariate Statistics
PS2.1 & 2.2: Linear Correlations PS2: Bivariate Statistics LT1: Basics of Correlation LT2: Measuring Correlation and Line of best fit by eye Univariate (one variable) Displays Frequency tables Bar graphs
More informationBiostatistics: Correlations
Biostatistics: s One of the most common errors we find in the press is the confusion between correlation and causation in scientific and health-related studies. In theory, these are easy to distinguish
More informationMATH 1070 Introductory Statistics Lecture notes Relationships: Correlation and Simple Regression
MATH 1070 Introductory Statistics Lecture notes Relationships: Correlation and Simple Regression Objectives: 1. Learn the concepts of independent and dependent variables 2. Learn the concept of a scatterplot
More information1. Create a scatterplot of this data. 2. Find the correlation coefficient.
How Fast Foods Compare Company Entree Total Calories Fat (grams) McDonald s Big Mac 540 29 Filet o Fish 380 18 Burger King Whopper 670 40 Big Fish Sandwich 640 32 Wendy s Single Burger 470 21 1. Create
More information2012 OCEAN DRILLING CITATION REPORT
2012 OCEAN DRILLING CITATION REPORT Covering Citations Related to the Deep Sea Drilling Project, Ocean Drilling Program, and Integrated Ocean Drilling Program from GeoRef Citations Indexed by the American
More informationObjectives. 2.1 Scatterplots. Scatterplots Explanatory and response variables Interpreting scatterplots Outliers
Objectives 2.1 Scatterplots Scatterplots Explanatory and response variables Interpreting scatterplots Outliers Adapted from authors slides 2012 W.H. Freeman and Company Relationship of two numerical variables
More informationChapter 14. Statistical versus Deterministic Relationships. Distance versus Speed. Describing Relationships: Scatterplots and Correlation
Chapter 14 Describing Relationships: Scatterplots and Correlation Chapter 14 1 Statistical versus Deterministic Relationships Distance versus Speed (when travel time is constant). Income (in millions of
More informationExploratory Data Analysis: Two Variables
Exploratory Data Analysis: Two Variables FPP 7-9 Exploratory data analysis: two variables 2 qualitative/categorical variables Contingency tables (we will cover these later in the semester) 1 qualitative/categorical,
More informationAP Stats ~ 3A: Scatterplots and Correlation OBJECTIVES:
OBJECTIVES: IDENTIFY explanatory and response variables in situations where one variable helps to explain or influences the other. MAKE a scatterplot to display the relationship between two quantitative
More informationLeast Squares Regression
Least Squares Regression Sections 5.3 & 5.4 Cathy Poliak, Ph.D. cathy@math.uh.edu Office in Fleming 11c Department of Mathematics University of Houston Lecture 14-2311 Cathy Poliak, Ph.D. cathy@math.uh.edu
More informationLesson 4 Linear Functions and Applications
In this lesson, we take a close look at Linear Functions and how real world situations can be modeled using Linear Functions. We study the relationship between Average Rate of Change and Slope and how
More informationCalories, Obesity and Health in OECD Countries
Presented at: The Agricultural Economics Society's 81st Annual Conference, University of Reading, UK 2nd to 4th April 200 Calories, Obesity and Health in OECD Countries Mario Mazzocchi and W Bruce Traill
More informationDo Now 18 Balance Point. Directions: Use the data table to answer the questions. 2. Explain whether it is reasonable to fit a line to the data.
Do Now 18 Do Now 18 Balance Point Directions: Use the data table to answer the questions. 1. Calculate the balance point.. Explain whether it is reasonable to fit a line to the data.. The data is plotted
More informationCurrent Account Dynamics under Information Rigidity and Imperfect Capital Mobility
Crawford School of Public Policy CAMA Centre for Applied Macroeconomic Analysis Current Account Dynamics under Information Rigidity and Imperfect Capital Mobility CAMA Working Paper 56/2018 November 2018
More informationChapter 5 Least Squares Regression
Chapter 5 Least Squares Regression A Royal Bengal tiger wandered out of a reserve forest. We tranquilized him and want to take him back to the forest. We need an idea of his weight, but have no scale!
More informationExport Destinations and Input Prices. Appendix A
Export Destinations and Input Prices Paulo Bastos Joana Silva Eric Verhoogen Jan. 2016 Appendix A For Online Publication Figure A1. Real Exchange Rate, Selected Richer Export Destinations UK USA Sweden
More informationExploratory Data Analysis: Two Variables
9/1/9 Exploratory Data Analysis: Two Variables FPP 7-9 Exploratory data analysis: two variables 2 qualitative/categorical variables Contingency tables (we will cover these later in the semester) 1 qualitative/categorical,
More informationSIMPLE LINEAR REGRESSION STAT 251
1 SIMPLE LINEAR REGRESSION STAT 251 OUTLINE Relationships in Data The Beginning Scatterplots Correlation The Least Squares Line Cautions Association vs. Causation Extrapolation Outliers Inference: Simple
More informationAMS 7 Correlation and Regression Lecture 8
AMS 7 Correlation and Regression Lecture 8 Department of Applied Mathematics and Statistics, University of California, Santa Cruz Suumer 2014 1 / 18 Correlation pairs of continuous observations. Correlation
More informationORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT
ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT Pursuant to Article 1 of the Convention signed in Paris on 14th December 1960, and which came into force on 30th September 1961, the Organisation
More informationChapter 6. Exploring Data: Relationships
Chapter 6 Exploring Data: Relationships For All Practical Purposes: Effective Teaching A characteristic of an effective instructor is fairness and consistenc in grading and evaluating student performance.
More informationHarvard University. Rigorous Research in Engineering Education
Statistical Inference Kari Lock Harvard University Department of Statistics Rigorous Research in Engineering Education 12/3/09 Statistical Inference You have a sample and want to use the data collected
More information1) A residual plot: A)
1) A residual plot: A) B) C) D) E) displays residuals of the response variable versus the independent variable. displays residuals of the independent variable versus the response variable. displays residuals
More informationCORRELATION. compiled by Dr Kunal Pathak
CORRELATION compiled by Dr Kunal Pathak Flow of Presentation Definition Types of correlation Method of studying correlation a) Scatter diagram b) Karl Pearson s coefficient of correlation c) Spearman s
More informationNorth-South Gap Mapping Assignment Country Classification / Statistical Analysis
North-South Gap Mapping Assignment Country Classification / Statistical Analysis Due Date: (Total Value: 55 points) Name: Date: Learning Outcomes: By successfully completing this assignment, you will be
More informationa. Length of tube: Diameter of tube:
CCA Ch 6: Modeling Two-Variable Data Name: 6.1.1 How can I make predictions? Line of Best Fit 6-1. a. Length of tube: Diameter of tube: Distance from the wall (in) Width of field of view (in) b. Make a
More informationSCATTERPLOTS. We can talk about the correlation or relationship or association between two variables and mean the same thing.
SCATTERPLOTS When we want to know if there is some sort of relationship between 2 numerical variables, we can use a scatterplot. It gives a visual display of the relationship between the 2 variables. Graphing
More informationShortfalls of Panel Unit Root Testing. Jack Strauss Saint Louis University. And. Taner Yigit Bilkent University. Abstract
Shortfalls of Panel Unit Root Testing Jack Strauss Saint Louis University And Taner Yigit Bilkent University Abstract This paper shows that (i) magnitude and variation of contemporaneous correlation are
More information2017 Source of Foreign Income Earned By Fund
2017 Source of Foreign Income Earned By Fund Putnam Emerging Markets Equity Fund EIN: 26-2670607 FYE: 08/31/2017 Statement Pursuant to 1.853-4: The fund is hereby electing to apply code section 853 for
More information