Residuals.

Slides:



Advertisements
Similar presentations
Chapter 3 Examining Relationships Lindsey Van Cleave AP Statistics September 24, 2006.
Advertisements

Estimating Total Cost for A Single Product
Correlation and Linear Regression
From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
1. What is the probability that a randomly selected person is a woman who likes P.E.? 2. Given that you select a man, what is the probability that he likes.
Describing Bivariate Relationships Chapter 3 Summary YMS3e AP Stats at LSHS Mr. Molesky Chapter 3 Summary YMS3e AP Stats at LSHS Mr. Molesky.
2.4: Cautions about Regression and Correlation. Cautions: Regression & Correlation Correlation measures only linear association. Extrapolation often produces.
AP STATISTICS LESSON 3 – 3 LEAST – SQUARES REGRESSION.
Linear Regression Chapter 8.
 The equation used to calculate Cab Fare is y = 0.75x where y is the cost and x is the number of miles traveled. 1. What is the slope in this equation?
Scatter Plots And Looking at scatter plots Or Bivariate Data.
WARM-UP Do the work on the slip of paper (handout)
 Find the Least Squares Regression Line and interpret its slope, y-intercept, and the coefficients of correlation and determination  Justify the regression.
An Exercise in Regression.  I borrow thermometers from the science teachers and bring a LARGE container of hot water. Reserve the hot chocolate mix.
Review #2.
AP STATISTICS Section 3.2 Least Squares Regression.
A P STATISTICS LESSON 3 – 3 (DAY 3) A P STATISTICS LESSON 3 – 3 (DAY 3) RISIDUALS.
Copyright © 2003, N. Ahbel Residuals. Copyright © 2003, N. Ahbel Predicted Actual Actual – Predicted = Error Source:
Warm-up O Turn in HW – Ch 8 Worksheet O Complete the warm-up that you picked up by the door. (you have 10 minutes)
AP Statistics Section 4.1 A Transforming to Achieve Linearity.
Ch 5 Relationships Between Quantitative Variables (pg 150) –Will use 3 tools to describe, picture, and quantify 1) scatterplot 2) correlation 3) regression.
Residuals.
Residual Plots Unit #8 - Statistics.
LEAST-SQUARES REGRESSION 3.2 Role of s and r 2 in Regression.
Chapter 8 Linear Regression. Fat Versus Protein: An Example 30 items on the Burger King menu:
Copyright © 2003, N. Ahbel Residuals. Copyright © 2003, N. Ahbel Predicted Actual Actual – Predicted = Error Source:
Residuals. Why Do You Need to Look at the Residual Plot? Because a linear regression model is not always appropriate for the data Can I just look at the.
Simple Linear Regression Relationships Between Quantitative Variables.
Describing Bivariate Relationships. Bivariate Relationships When exploring/describing a bivariate (x,y) relationship: Determine the Explanatory and Response.
Introductory Statistics. Inference for Bivariate Data Intro to Inference in Regression Requirements for Linear Regression Linear Relationship Constant.
Chapter 8 Part I Answers The explanatory variable (x) is initial drop, measured in feet, and the response variable (y) is duration, measured in seconds.
Section 11.1 Day 3.
Topics
Is there a relationship between the lengths of body parts?
Statistics 101 Chapter 3 Section 3.
distance prediction observed y value predicted value zero
EXAMPLE 1 Describe correlation Telephones
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Happiness comes not from material wealth but less desire.
Warm-Up . Math Social Studies P.E. Women Men 2 10
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Residuals Learning Target:
Describing Bivariate Relationships
1. Describe the Form and Direction of the Scatterplot.
EQ: How well does the line fit the data?
AP Statistics, Section 3.3, Part 1
GET OUT p.161 HW!.
value of monitored variable
Least Squares Method: the Meaning of r2
The greatest blessing in life is
Examining Relationships
Warm-Up 8/50 = /20 = /50 = .36 Math Social Studies P.E.
The Least-Squares Line Introduction
Residuals and Residual Plots
Review Homework.
Review Homework.
Scatter Plots and Equations of Lines
Review Homework.
Review Homework.
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Inference for Regression Slope
Homework: pg. 276 #5, 6 5.) A. The relationship is strong, negative, and curved. The ratios are all Since the ratios are all the same, the exponential.
A medical researcher wishes to determine how the dosage (in mg) of a drug affects the heart rate of the patient. Find the correlation coefficient & interpret.
Draw Scatter Plots and Best-Fitting Lines
Introductory Statistics Introductory Statistics
Warm-Up . Math Social Studies P.E. Women Men 2 10
Types of Errors And Error Analysis.
Concavity and Rates of Change
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Residuals and Residual Plots
Presentation transcript:

Residuals

Actual – Predicted = Error Predicted “Residual” in statistics is the same as “error” Residual = Actual y – Predicted y Source: http://www.studyworksonline.com, 8/13/03 Actual Actual – Predicted = Error Predicted

“Residual” in statistics is the same as “error” Residual = Actual y – Predicted y Source: http://www.studyworksonline.com, 8/13/03 Positive residual

“Residual” in statistics is the same as “error” Residual = Actual y – Predicted y Source: http://www.studyworksonline.com, 8/13/03 Negative residual

Residual plot

1-1 correspondence between every point on the scatterplot and the corresponding point on the residual plot

Residuals No Pattern Clear Pattern  Appropriate Model Concave up or down No Pattern Clear Pattern Increasing or decreasing Too high or low  Appropriate Model  Inappropriate Model Small Residuals Large Residuals  Appropriate Model  Appropriate Model, But Not Very Useful

Pattern – Clear concave down pattern Magnitude – Small residuals Conclusion - Not an appropriate model

Pattern – No clear pattern Magnitude – Fairly large residuals Conclusion - Appropriate , but not very useful model

Pattern – No clear pattern Magnitude – Small residuals Conclusion - Very appropriate model

Pattern – Clear decreasing pattern Magnitude – Small residuals Conclusion - Not an appropriate model

Pattern – Clear pattern, residuals too low Magnitude – Small residuals Conclusion - Not an appropriate model

Residuals