The Height and Span Juxtaposition

Slides:



Advertisements
Similar presentations
Line of Best Fit Warm Up Lesson Presentation Lesson Quiz
Advertisements

Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. Relationships Between Quantitative Variables Chapter 5.
Chapter 4 Describing the Relation Between Two Variables
LINEAR REGRESSION: What it Is and How it Works Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
LINEAR REGRESSION: What it Is and How it Works. Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r.
LINEAR REGRESSION: What it Is and How it Works. Overview What is Bivariate Linear Regression? The Regression Equation How It’s Based on r Assumptions.
REGRESSION What is Regression? What is the Regression Equation? What is the Least-Squares Solution? How is Regression Based on Correlation? What are the.
REGRESSION Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured.
Chapter 2 – Simple Linear Regression - How. Here is a perfect scenario of what we want reality to look like for simple linear regression. Our two variables.
SPSS Session 4: Association and Prediction Using Correlation and Regression.
Regression Basics For Business Analysis If you've ever wondered how two or more things relate to each other, or if you've ever had your boss ask you to.
Line of Best Fit 4-8 Warm Up Lesson Presentation Lesson Quiz
Further Topics in Regression Analysis Objectives: By the end of this section, I will be able to… 1) Explain prediction error, calculate SSE, and.
Correlation is a statistical technique that describes the degree of relationship between two variables when you have bivariate data. A bivariate distribution.
Linear Regression Least Squares Method: the Meaning of r 2.
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?
Sullivan – Fundamentals of Statistics – 2 nd Edition – Chapter 4 Section 2 – Slide 1 of 20 Chapter 4 Section 2 Least-Squares Regression.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
Section 12.3 Regression Analysis HAWKES LEARNING SYSTEMS math courseware specialists Copyright © 2008 by Hawkes Learning Systems/Quant Systems, Inc. All.
Examining Bivariate Data Unit 3 – Statistics. Some Vocabulary Response aka Dependent Variable –Measures an outcome of a study Explanatory aka Independent.
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
7.1 Draw Scatter Plots and Best Fitting Lines Pg. 255 Notetaking Guide Pg. 255 Notetaking Guide.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
Unit 3 – Association: Contingency, Correlation, and Regression Lesson 3-3 Linear Regression, Residuals, and Variation.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
Describing Bivariate Relationships. Bivariate Relationships When exploring/describing a bivariate (x,y) relationship: Determine the Explanatory and Response.
Chapter 13 Linear Regression and Correlation. Our Objectives  Draw a scatter diagram.  Understand and interpret the terms dependent and independent.
Department of Mathematics
Statistics 200 Lecture #6 Thursday, September 8, 2016
Inference for Regression
1 Functions and Applications
Sections Review.
Chapter 13 Multiple Regression
distance prediction observed y value predicted value zero
Line of Best Fit Warm Up Lesson Presentation Lesson Quiz
SCATTERPLOTS, ASSOCIATION AND RELATIONSHIPS
Multiple Regression.
Correlation and Regression
Module 15-2 Objectives Determine a line of best fit for a set of linear data. Determine and interpret the correlation coefficient.
Section 13.7 Linear Correlation and Regression
BUS 308 HELPS Education for Service-- bus308helps.com.
1) A residual: a) is the amount of variation explained by the LSRL of y on x b) is how much an observed y-value differs from a predicted y-value c) predicts.
Lecture Slides Elementary Statistics Thirteenth Edition
Two Way Frequency Tables
Describing Bivariate Relationships
Introduction to bivariate data
Line of Best Fit Warm Up Lesson Presentation Lesson Quiz
STA 282 – Regression Analysis
The Weather Turbulence
3 4 Chapter Describing the Relation between Two Variables
two variables two sets of data
Least Squares Method: the Meaning of r2
Correlation Coefficient
6/23 the name game 2/22/2019 Algebra 1 Institute.
Correlation and Regression
SCATTER PLOTS.
Line of Best Fit Warm Up Lesson Presentation Lesson Quiz
Line of Best Fit 4-8 Warm Up Lesson Presentation Lesson Quiz
Line of Best Fit 4-8 Warm Up Lesson Presentation Lesson Quiz
Line of Best Fit 4-8 Warm Up Lesson Presentation Lesson Quiz
Line of Best Fit Warm Up Lesson Presentation Lesson Quiz
Homework: pg. 180 #6, 7 6.) A. B. The scatterplot shows a negative, linear, fairly weak relationship. C. long-lived territorial species.
Line of Best Fit Warm Up Lesson Presentation Lesson Quiz
Unit 2 – Graphical Representation
7.1 Draw Scatter Plots and Best Fitting Lines
Descriptive Statistics Univariate Data
Scatterplots Regression, Residuals.
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;
MATH 2311 Section 5.1.
Presentation transcript:

The Height and Span Juxtaposition Unit 4 – Bivariate Data 2/24/2019 Algebra 1 Institute

Another TED Talk Hans Rosling 2/24/2019 Algebra 1 Institute

Bivariate Data Association between two variables Positive: an increase in one variable generally produces an increase in the other. Example: association between a student's grades and the number of hours per week that student spends studying Negative: an increase in one variable generally produces a decrease in the other. Example: association between the number of doctors in a country and the percentage of the population that dies before adulthood is generally a negative one. 2/24/2019 Algebra 1 Institute

Collect Data In groups of 2 2/24/2019 Algebra 1 Institute

Compile the Data 2/24/2019 Algebra 1 Institute

Scatterplot in GeoGebra Enter the Arm Span information in Column A and the Height in Column B. Highlight the cells in Column A and B that contain the information. Click on the tool “Two Variable Regression Analysis” On the pop-up window, click “Analyze” A Scatterplot will be produced 2/24/2019 Algebra 1 Institute

Association? Does an increase in arm span generally lead to an increase in height? How strong is the association (correlation)? 2/24/2019 Algebra 1 Institute

Find the Means Is your arm span and height above the average of these 40 adults? How many of the 40 people have above-average arm spans? How many of the 40 people have above-average heights? It is possible to divide the 40 people into four categories: Above-average arm span and above-average height; Above-average arm span and below-average height; Below-average arm span and above-average height; and Below-average arm span and below-average height. 2/24/2019 Algebra 1 Institute

Use the Means to add Lines Regression is about the mean, but you could ask the students what would change if you replace those lines with the medians instead of the means? 2/24/2019 Algebra 1 Institute

How Many People in each Quadrant? Do most people with below-average arm spans also have below-average heights? Do most people with above-average arm spans also have above-average heights? What do these answers suggest? 2/24/2019 Algebra 1 Institute

Height – Arm Span How many of the 40 people have heights that are greater than their arm spans? How many of the 40 people have heights that are less than their arm spans? How many of the 40 people have heights that are equal to their arm spans? Which six people are the closest to being square without being perfectly square? Which five are the farthest from being square? 2/24/2019 Algebra 1 Institute

Draw Line y = x in Scatterplot What do the points above the line represent? What do the points on the line represent? What do the points below the line represent? Why is it helpful to draw the line height = arm span? How does this line help us analyze differences? 2/24/2019 Algebra 1 Institute

Draw More Lines Which one is a better fit? Y = x Y = x + 1 Y = x – 1 Any other? 2/24/2019 Algebra 1 Institute

Distance = |Y - YL| = |Error| Error = Y - YL Y = actual observed height (Y) YL = predicted height (on the line) Error = Actual Observed Height - Predicted Height Distance = |Y - YL| = |Error| 2/24/2019 Algebra 1 Institute

Square of the Error When comparing two lines, the line with the smaller total of the sum of the squared errors (SSE) is the "better" line in terms of how well it describes the linear relationship between the two variables. |Y-YL|2 2/24/2019 Algebra 1 Institute

For our Data Judging on the basis of the SSE, which is the best line? Which is the worst? What other ways could we change the line equation in an attempt to further reduce the SSE? Is it possible to reduce the SSE to 0? Why or why not? 2/24/2019 Algebra 1 Institute