Review Homework.

Slides:



Advertisements
Similar presentations
Scatter Diagrams and Linear Correlation
Advertisements

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.
Relationship of two variables
Residuals and Residual Plots Most likely a linear regression will not fit the data perfectly. The residual (e) for each data point is the ________________________.
Ch 3 – Examining Relationships YMS – 3.1
Correlation & Regression – Non Linear Emphasis Section 3.3.
Chapter 3 Section 3.1 Examining Relationships. Continue to ask the preliminary questions familiar from Chapter 1 and 2 What individuals do the data describe?
 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?
Regression Regression relationship = trend + scatter
Regression Lines. Today’s Aim: To learn the method for calculating the most accurate Line of Best Fit for a set of data.
Scatter Plots And Looking at scatter plots Or Bivariate Data.
3.2 - Least- Squares Regression. Where else have we seen “residuals?” Sx = data point - mean (observed - predicted) z-scores = observed - expected * note.
Get out the Notes from Monday Feb. 4 th, Example 2: Consider the table below displaying the percentage of recorded music sales coming from music.
Creating a Residual Plot and Investigating the Correlation Coefficient.
Section 2.6 – Draw Scatter Plots and Best Fitting Lines A scatterplot is a graph of a set of data pairs (x, y). If y tends to increase as x increases,
Algebra 3 Lesson 1.9 Objective: SSBAT identify positive, negative or no correlation. SSBAT calculate the line of best fit using a graphing calculator.
AP Statistics HW: p. 165 #42, 44, 45 Obj: to understand the meaning of r 2 and to use residual plots Do Now: On your calculator select: 2 ND ; 0; DIAGNOSTIC.
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)
Residuals.
LEAST-SQUARES REGRESSION 3.2 Role of s and r 2 in Regression.
1.5 Linear Models Warm-up Page 41 #53 How are linear models created to represent real-world situations?
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.
Section 1.3 Scatter Plots and Correlation.  Graph a scatter plot and identify the data correlation.  Use a graphing calculator to find the correlation.
REGRESSION MODELS OF BEST FIT Assess the fit of a function model for bivariate (2 variables) data by plotting and analyzing residuals.
Correlation & Linear Regression Using a TI-Nspire.
CHAPTER 3 Describing Relationships
CHAPTER 3 Describing Relationships
distance prediction observed y value predicted value zero
3.2A Least Squares Regression
Section 3.2: Least Squares Regression
MATH 2311 Section 5.5.
Residuals.
The following data represents the amount of Profit (in thousands of $) made by a trucking company dependent on gas prices. Gas $
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Introduction The fit of a linear function to a set of data can be assessed by analyzing residuals. A residual is the vertical distance between an observed.
Homework: Residuals Worksheet
Warm-Up . Math Social Studies P.E. Women Men 2 10
Day 13 Agenda: DG minutes.
Investigating Relationships
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Residuals Learning Target:
GET OUT p.161 HW!.
Examining Relationships
Warm-Up 8/50 = /20 = /50 = .36 Math Social Studies P.E.
Chapter 3 Describing Relationships Section 3.2
Residuals and Residual Plots
~adapted for Walch Education
Review Homework.
Review Homework.
Residuals, Influential Points, and Outliers
Review Homework.
Adequacy of Linear Regression Models
MATH 2311 Section 5.5.
Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A.
Ch 4.1 & 4.2 Two dimensions concept
Introduction The fit of a linear function to a set of data can be assessed by analyzing residuals. A residual is the vertical distance between an observed.
Section 3.2: Least Squares Regressions
Inference for Regression Slope
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.
Ch 9.
Chapter 3.2 Regression Wisdom.
Residuals (resids).
Chapter 9 Regression Wisdom.
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.
Scatter Plots Learning Goals
Residuals and Residual Plots
Presentation transcript:

Review Homework

Residuals From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A

Residual (or error) Observed y MINUS predicted y

Determines the effectiveness of the regression model Analyzing Residuals Determines the effectiveness of the regression model

A scatterplot of Residuals vs. X Residual Plots A scatterplot of Residuals vs. X

Residual Plots Determine If the model is appropriate, then the plot will have a random scatter. If another model is necessary, the plot will have a pattern. Pattern = Problem

Example of Random Scatter

Examples Determine, just by visual inspection, if the linear model is appropriate or inappropriate.

Linear model appropriate or inappropriate?

The only way to know is to see the residual plot. 1. Does their appear to be a pattern in the residual plot? Yes, quadratic. 2. Does this support your original guess? You must now see that a linear model does NOT fit this data.

Linear model appropriate or inappropriate?

The only way to know is to see the residual plot. 1. Does their appear to be a pattern in the residual plot? Yes, it fans out as x increases. 2. Does this support your original guess? You must now see that a linear model does NOT fit this data.

Linear model appropriate or inappropriate?

The only way to know is to see the residual plot. 1. Does their appear to be a pattern in the residual plot? Yes, it looks quadratic. 2. Does this support your original guess? This was very tricky. The scale was very small. You must now see that a linear model does NOT fit this data.

Linear model appropriate or inappropriate?

The only way to know is to see the residual plot. 1. Does their appear to be a pattern in the residual plot? Yes, it seems decrease as x increases. 2. Does this support your original guess? This was tricky. You must now see that a linear model does NOT fit this data.

Example: Calculate Residual Tracking Cell Phone Use over 10 days Total Time (minutes) Total Distance (miles Predicted Total Distance Residuals (observed – predicted) 32 51 54.4 -3.4 19 30 31.9 28 47 36 56 17 27 23 35 41 65 22 37 73 54 Data from TI Activity for NUMB3RS Episode 202

Example: Calculate Residual Tracking Cell Phone Use over 10 days Total Time (minutes) Total Distance (miles Predicted Total Distance Residuals (observed – predicted) 32 51 54.4 -3.4 19 30 31.9 -1.9 28 47 47.5 -0.5 36 56 61.3 -5.3 17 27 28.5 -1.5 23 35 38.8 -3.8 41 65 70.0 -5 22 37.1 3.9 37 73 63.1 9.9 54 6.5 Data from TI Activity for NUMB3RS Episode 202

Good fit or not?

Classwork Carnival Task

Homework Worksheet