Introduction to Linear Regression. Wake Forest Men’s BBall HeightWeightHeightWeight 74”17579”205 74”19584”235 78”20084”230 73”18577”210 79”20572”170 80”25072”175.

Slides:

Advertisements

Similar presentations

NFL Franchise Activity Push/Pull Factors NFL Franchises Akron Professionals Buffalo All-Americans Canton Bulldogs Chicago Cardinals Chicago Tigers.

Advertisements

A student wonders if tall women tend to date taller men than do short women. She measures herself, her dormitory roommate, and the women in the adjoining.

Regression BPS chapter 5 © 2006 W.H. Freeman and Company.

Calculations in the Bivariate Normal Distribution James H. Steiger.

Warm up Use calculator to find r,, a, b. Chapter 8 LSRL-Least Squares Regression Line.

Copyright ©2006 Brooks/Cole, a division of Thomson Learning, Inc. Relationships Between Quantitative Variables Chapter 5.

LSRL Least Squares Regression Line

Relationships Between Quantitative Variables

NFL Football Mark: The History of the NFL andSherrie: How the game is played, the players, and teams.

Least Squares Regression

Linear Regression Analysis

In the above correlation matrix, what is the strongest correlation? What does the sign of the correlation tell us? Does this correlation allow us to say.

Biostatistics Unit 9 – Regression and Correlation.

Introduction A correlation between two events simply means that there is a consistent relationship between two events, and that a change in one event implies.

2-5 Using Linear Models Make predictions by writing linear equations that model real-world data.

A student wonders if tall women tend to date taller men than do short women. She measures herself, her dormitory roommate, and the women in the adjoining.

Money V Success in Sport Exercise on Correlation.

Linear Regression Least Squares Method: the Meaning of r 2.

Brett Favre was born on October 10, 1969.

Correlation Correlation is used to measure strength of the relationship between two variables.

N.F.C. teams : Dallas Cowboys Washington Redskins New York Giants Philadelphia Eagles Minnesota Vikings Green Bay Packers Chicago Bears Detroit Lions.

Regression BPS chapter 5 © 2010 W.H. Freeman and Company.

PS 225 Lecture 20 Linear Regression Equation and Prediction.

CHAPTER 5 Regression BPS - 5TH ED.CHAPTER 5 1. PREDICTION VIA REGRESSION LINE NUMBER OF NEW BIRDS AND PERCENT RETURNING BPS - 5TH ED.CHAPTER 5 2.

5.7 Scatter Plots and Line of Best Fit I can write an equation of a line of best fit and use a line of best fit to make predictions.

Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.

Least Squares Regression.   If we have two variables X and Y, we often would like to model the relation as a line  Draw a line through the scatter.

Football originated during the 1823 (it was called rugby) in England. Rugby was then introduced to the North Americans by the British army. It was introduced.

Least Squares Regression Lines Text: Chapter 3.3 Unit 4: Notes page 58.

Section 1.6 Fitting Linear Functions to Data. Consider the set of points {(3,1), (4,3), (6,6), (8,12)} Plot these points on a graph –This is called a.

Simple Linear Regression The Coefficients of Correlation and Determination Two Quantitative Variables x variable – independent variable or explanatory.

Scatter plots. Like a line graph Has x and y axis Plot individual points.

UNIT 2 BIVARIATE DATA. BIVARIATE DATA – THIS TOPIC INVOLVES…. y-axis DEPENDENT VARIABLE x-axis INDEPENDENT VARIABLE.

I CAN… Make an arguable claim that can be supported with evidence from the text. “How to” 1.Read the passage/analyze the graph. 2.Determine what its main.

Making Predictions from Graphs. Trends  Graphs can show a general trend Sometimes you can make a prediction from the data shown on a graph.

The NFL is split into two CONFERENCES. And each conference is broken down into four DIVISIONS. American Football Conference (AFC) National Football Conference.

1.How much longer does it take for object B to travel 40 yards than it takes for object A? 2.How much further has object A traveled in 10 seconds than.

The Least Squares Regression Line. The problem with drawing line of best fit by eye is that the line drawn will vary from person to person. Instead, use.

Over Lesson 4–5 5-Minute Check 1 A.positive B.negative C.no correlation The table shows the average weight for given heights. Does the data have a positive.

Chapter 3 LSRL. Bivariate data x – variable: is the independent or explanatory variable y- variable: is the dependent or response variable Use x to predict.

Chapter 5 LSRL. Bivariate data x – variable: is the independent or explanatory variable y- variable: is the dependent or response variable Use x to predict.

NFL Quarterback Salary and Passer Rating

Lesson 4.5 Topic/ Objective: To use residuals to determine how well lines of fit model data. To use linear regression to find lines of best fit. To distinguish.

Chapter 4.2 Notes LSRL.

Least Squares Regression Line.

4-5 Predicting with Linear Models

FOOTBALL This is an example text. Go ahead an replace it with your own text. This is an example text END ZONE.

The Super Bowl The Super Bowl takes place from

Chapter 5 LSRL.

LSRL Least Squares Regression Line

Chapter 4 Correlation.

Chapter 3.2 LSRL.

Module 15-2 Objectives Determine a line of best fit for a set of linear data. Determine and interpret the correlation coefficient.

r = The correlation between price and calories is 0. 79

Linear Regression.

Two Quantitative Variables: Linear Regression

Least Squares Regression Line LSRL Chapter 7-continued

4.5 Analyzing Lines of Fit.

Lesson 5.7 Predict with Linear Models The Zeros of a Function

4-5 Predicting with Linear Models

Chapter 5 LSRL.

Chapter 5 LSRL.

Chapter 5 LSRL.

Descriptive Statistics Univariate Data

Monday, March 10th Warm Up To find the residual you take the ACTUAL data and ______________ the PREDICTED data. If the residual plot creates a pattern.

Honors Statistics Review Chapters 7 & 8

Presentation transcript:

Introduction to Linear Regression

Wake Forest Men’s BBall HeightWeightHeightWeight 74”17579”205 74”19584”235 78”20084”230 73”18577”210 79”20572”170 80”25072”175 80”21578”195 77”235 Height (in inches) Weight (in pounds)

Correlation vs. Causation What type of correlation is there between height and weight? Does that mean causation? Turn Diagnostics On and Find the Linear Regression for the data. Pearson’s Correlation Coefficient: “r- value” Strong negative No Strong Positive Correlation Correlation Correlation

Graph and Residuals Residual – The distance from the data points to the line. Which points have positive residual? Which points have negative residual? To find the “Least Squares Regression” statisticians try to minimize the distance between the residuals and the line of best fit. (Tomorrow’s activity)

Interpolation vs. Extrapolation If another basketball player joined the team and he was 69 inches tall, how much would he weigh according to the linear regression line? –Approximately 163 pounds This is EXTRAPOLATION because we are using a value outside of the data’s range. May or may not be a good prediction.

Interpolation vs. Extrapolation What if a player weighed 225 pounds, how tall would he be? Then according to the data he would be around 81 inches tall. This is INTERPOLATION, because the value is within the ranges of the data. The answer would be more likely to be correct for a prediction.

NFL Avg Points per Game and Yards/Completion TeamPoints per Game Yards per Completion New England Patriots Atlanta Falcons Pittsburgh Steelers Green Bay Packers Chicago Bears Buffalo Bills Dallas Cowboys Philadelphia Eagles Minnesota Vikings New York Jets

Pearson’s Correlation Coefficient How strong is the correlation? Is there one? Would this be a good model for prediction? Why or why not?

GPA and TV Hours per week # of hours of TV per week GPA

Predict What would someone’s GPA be if he/she watched 5 hours of TV. Is this a good model to use for this prediction? If someone’s GPA was a 3.1, what would this data say about the number of hours he/she watched TV?