Warm Up I. Pilots or Senators: Which team has played better? vs. East vs. West vs. East vs. West WINSLOSSES PCT. WINS LOSSES PCT. WINSLOSSES PCT. WINS.

Slides:



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

The Question of Causation YMS3e 4.3:Establishing Causation AP Statistics Mr. Molesky.
Relationship between Variables Assessment Statement Explain that the existence of a correlation does not establish that there is a causal relationship.
Chapter 4 Review: More About Relationship Between Two Variables
Chapter 4: More on Two- Variable Data.  Correlation and Regression Describe only linear relationships Are not resistant  One influential observation.
Unit 1: Science of Psychology
Correlation vs. Causation. In a Gallup poll, surveyors asked, “Do you believe correlation implies causation?’” 64% of American’s answered “Yes”. 38% replied.
Correlation and Regression History Sir Francis Galton Geographer, meteorologist, tropical explorer, inventor of fingerprint identification,
Comparitive Graphs.
AP Statistics Section 4.3 Establishing Causation
AP Statistics Causation & Relations in Categorical Data.
Effect of Home Dynamics and Parental Involvement on School Achievement and Behavior By: Katie, Courtney, & Christine By: Katie, Courtney, & Christine.
BPSChapter 61 Two-Way Tables. BPSChapter 62 To study associations between quantitative variables  correlation & regression (Ch 4 & Ch 5) To study associations.
Ch 2 and 9.1 Relationships Between 2 Variables
10. Introduction to Multivariate Relationships Bivariate analyses are informative, but we usually need to take into account many variables. Many explanatory.
AP STATS: 50 point quiz Sit with your partner. This is open notes/textbook. Work for 20 minutes with your partner on the quiz. Each person will have to.
Women24 Parenting Survey October aims To investigate trends in parenting To test with data some assumptions frequently made by parents on parenting.
Chapter 5 Regression. Chapter outline The least-squares regression line Facts about least-squares regression Residuals Influential observations Cautions.
What you should write after every trip to your site.
Chapter 4 Section 3 Establishing Causation
The Question of Causation
HW#9: read Chapter 2.6 pages On page 159 #2.122, page 160#2.124,
Correlation, What Makes a Perfect Parent? and a Perfect Spouse?
C HAPTER 4: M ORE ON T WO V ARIABLE D ATA Sec. 4.2 – Cautions about Correlation and Regression.
UNDERSTANDING GENDER 1.GENDER FORMATION –developing a sense of who you are as boys or girls through everyday interactions with family, friends, media,
The Practice of Statistics Third Edition Chapter 4: More about Relationships between Two Variables Copyright © 2008 by W. H. Freeman & Company Daniel S.
1 Chapter 4: More on Two-Variable Data 4.1Transforming Relationships 4.2Cautions 4.3Relations in Categorical Data.
GEORGE L. ASKEW, MD, FAAP OFFICE OF THE ASSISTANT SECRETARY ADMINISTRATION FOR CHILDREN AND FAMILIES U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES AMERICAN.
Math notebook, pencil and calculator Conditional Relative Frequencies and Association.
What Makes a Perfect Parent? and Missed Opportunities.
WHS AP Psychology Research Methods: Correlation. I CAN ANSWER How do psychologists use the scientific method to study behavior and mental processes? What.
1 Chapter 4: More on Two-Variable Data 4.1Transforming Relationships 4.2Cautions 4.3Relations in Categorical Data.
Slide Slide 1 Warm Up Page 536; #16 and #18 For each number, answer the question in the book but also: 1)Prove whether or not there is a linear correlation.
AP Psychology Chapter 1: Science of Psychology Objective :Describe a correlational research study taking into account operational definitions, random sampling,
Spectators of Finnish baseball: comparing women’s and men’s games Seppo Suominen.
Does Association Imply Causation? Sometimes, but not always! What about: –x=mother's BMI, y=daughter's BMI –x=amt. of saccharin in a rat's diet, y=# of.
4.2 - Experiments. Observational Studies measures variables of interest without attempting to influence the responses. sample surveys watching animals.
What Makes a Perfect Parent? and a Perfect Spouse? The Role of Incentives.
CHAPTER 6: Two-Way Tables. Chapter 6 Concepts 2  Two-Way Tables  Row and Column Variables  Marginal Distributions  Conditional Distributions  Simpson’s.
Relationship between Variables Assessment Statement Explain that the existence of a correlation does not establish that there is a causal relationship.
Business Statistics for Managerial Decision Making
CHS AP Psychology Unit 1: Science of Psychology Essential Task 1-6:Describe a correlational research study taking into account correlational coefficient,
Chapter 4 Day Six Establishing Causation. Beware the post-hoc fallacy “Post hoc, ergo propter hoc.” To avoid falling for the post-hoc fallacy, assuming.
1.5 Cause and Effect. Consider the following Drivers of red cars are twice as likely to be involved in an accident as drivers of blue cars. Does this.
UNIT 4 Bivariate Data Scatter Plots and Regression.
By Collin Falloway, Will Foreman, Landon Marcum, Alexa Burke.
Cautions About Correlation and Regression Section 4.2.
Correlation vs. Causation. In a Gallup poll, surveyors asked, “Do you believe correlation implies causation?’” 64% of American’s answered “Yes”. 38% replied.
Section Causation AP Statistics ww.toddfadoir.com/apstats.
Data Analysis Causation Goal: I can distinguish between correlation and causation. (S-ID.9)
The Question of Causation 4.2:Establishing Causation AP Statistics.
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
Unit 1: Science of Psychology
Association vs. Causation
Lesson 13: Things To Watch out for
Establishing Causation
Section 4.3 Types of Association
Chapter 2 Looking at Data— Relationships
TOPIC 1: STATISTICAL ANALYSIS
DRILL Put these correlations in order from strongest to weakest.
Science of Psychology AP Psychology
7 Minutes of Silence Determine if the data is linear or exponential.
The Question of Causation
Chapter 5 Correlation.
CHAPTER 3 Describing Relationships
EQ: What gets in the way of a good model?
3.3 Cautions Correlation and Regression Wisdom Correlation and regression describe ONLY LINEAR relationships Extrapolations (using data to.
Section 6.2 Establishing Causation
Chapter 4: More on Two-Variable Data
Presentation transcript:

Warm Up I. Pilots or Senators: Which team has played better? vs. East vs. West vs. East vs. West WINSLOSSES PCT. WINS LOSSES PCT. WINSLOSSES PCT. WINS LOSSES PCT. PILOTS SENATORS __ ) Make a new table of the teams’ overall performance (wins/losses/pct.) 2) Which team had a better winning percentage when divvied up by East/West division? 3) Which team had a better overall winning percentage? 4) What happened? What is this called? 5) What do you notice about team records that might cause this? II. Consider the following two-way table: Type of college Gender 4 yr private 4 yr public Community Male579 Female ) Identify the row and column variables 2) Find the marginal distributions by percents 3) Find the conditional distribution of males and females going to a community college 4) Find the conditional distribution of community college attendees who are male and female.

Consider the following list of sixteen factors. Eight of the factors have a strong correlation (+ or -) with test scores; the other eight don’t seem to matter. Its taken from the 2005 best selling book Freakanomics. Try to guess which are which: The child has highly educated parents. The child has highly educated parents. The child’s family is intact. The child’s family is intact. The child’s parents have high socioeconomic status. The child’s parents have high socioeconomic status. The child’s parents recently moved into a better neighborhood. The child’s parents recently moved into a better neighborhood. The child’s mother was 30 or older at the time of the first child’s birth. The child’s mother was 30 or older at the time of the first child’s birth. The child’s mother didn’t work between birth and kindergarten. The child’s mother didn’t work between birth and kindergarten. The child had low birth weight. The child had low birth weight. The child attended Head Start. The child attended Head Start. The child’s parents speak English in the home. The child’s parents speak English in the home. The child’s parents regularly take him/her to museums. The child’s parents regularly take him/her to museums. The child is adopted. The child is adopted. The child is regularly spanked. The child is regularly spanked. The child’s parents are involved in the PTA. The child’s parents are involved in the PTA. The child frequently watches television. The child frequently watches television. The child has many books in his home. The child has many books in his home. The child’s parents read to him nearly every day. The child’s parents read to him nearly every day. Which ones correlate with test scores? Which ones correlate with test scores?

Lurking Variables Often the relationship between 2 variables is strongly influenced by one or more lurking variables. Often the relationship between 2 variables is strongly influenced by one or more lurking variables. Ex: Studies show that men who complain of chest pain are more likely to get detailed tests and aggressive treatment such as bypass surgery than are women with similar complaints. Is this association between gender and treatment due to discrimination? Ex: Studies show that men who complain of chest pain are more likely to get detailed tests and aggressive treatment such as bypass surgery than are women with similar complaints. Is this association between gender and treatment due to discrimination?

4.3: Establishing Causation (Types of Associations) Causation: Changes in x cause changes in y Causation: Changes in x cause changes in y Common response: Both x and y respond to changes in some unobserved variable Common response: Both x and y respond to changes in some unobserved variable Confounding: The effect of x on y is hopelessly mixed up with the effects of other variables. Confounding: The effect of x on y is hopelessly mixed up with the effects of other variables.

Causation Examples of observed associations between x and y 1) x = mother’s body mass index y = daughter’s body mass index y = daughter’s body mass index 2) x = amount of artificial sweetener saccharin in a rat’sdiet y = count of tumors in a rat’s bladder

Careful: A strong association is not necessarily causation! An article in a woman’s mag reported that mother’s who nurse their babies feel more receptive toward their infants than mothers who bottle-feed. The author concluded that breast-feeding (x) led to a more positive attitude (y) toward the child. Problems with this? An article in a woman’s mag reported that mother’s who nurse their babies feel more receptive toward their infants than mothers who bottle-feed. The author concluded that breast-feeding (x) led to a more positive attitude (y) toward the child. Problems with this?

Common Response 1) x = Ice Cream Sales y = # of shark attacks in swimmers y = # of shark attacks in swimmers 2) x = Skirt Length y = Stock Prices y = Stock Prices 3) x = # of cavities in elementary school kids y = vocabulary knowledge y = vocabulary knowledge 4) x = a high school senior’s SAT score y = the student’s first-year college grade point y = the student’s first-year college grade point average average 5) x = monthly flow of money into stock market funds y = monthly rate of return for the stock market y = monthly rate of return for the stock market

CONFOUNDING ONLY EXISTS if there is CONFUSION about whether changes in the confounding variable on the explanatory variable are leading to observed changes in the response variable. 1) x = whether a person regularly attends religious services y = how long the person lives 2) x = the number of years a worker has y = the worker’s income Confounding

. Example of a (spurious) correlation between the number of Methodist ministers in New England and the amount of Cuban rum imported to Boston over the years (by # of barrels). Example of a (spurious) correlation between the number of Methodist ministers in New England and the amount of Cuban rum imported to Boston over the years (by # of barrels). 1) Calculate r 2) Is the increasing number of ministers causing people to drink more? What could be the lurking variable? 3) What type of association is this? YEARMinistersRum