1. Least-Squares Regression: Linear Regression
Section 3.2
Reference Text:
The Practice of Statistics, Fourth Edition.
Starnes, Yates, Moore
Lesson 3.3.1

2. Warm up/ quiz Draw a quick sketch of three scatterplots:
Draw a plot with r ≈ .9
Draw a plot with r ≈ -.5
Draw a plot with r ≈ 0

3. Today’s Objective Regression line, introducing “y-hat” Predicted value
Slope
Y-intercept
Extrapolation
Residuals
Least-squares Regression Line
How to use your calculator effectively for time

4. Your Poster! In math-land this is known as a “line of best fit”
Take a look at your poster: Do you think you could draw a straight line that would go straight through the middle where you have ½ your points above and ½ your points below?
Calculate your line:
m = y2-y1 / x2-x1
Point slope form: y – y1 = m ( x - x1)
In math-land this is known as a “line of best fit”

5. Regression Line In statistics, this is called a regression line!
A line that describes how a response variables y changes as an explanatory variable x changes. We often use a regression line to predict the value of y for a given value of x.

6. Formulas for Regression Line
The RL is linear, so it follows the form y = mx + b
In Statistics, we say
In this context, is called the predicted value
WARNING: we are entering “predicting” statistics, using the “ ” symbol is very important
[story about AP training]
So is the predicted value
And ‘a’ is the y-intercept, the predicted value of y when x=0
And ‘b’ is the slope

7. Equation of Regression

8. The Meaning of Slope
In a simple algebraic function like y = 2x + 17, what is the real meaning of the slope?
For every increase in x of 1 unit, y increases by 2
In the function y = 2x + 17 what is the meaning of the y intercept?
It is the value y takes on when x = 0
In statistics if the regression line is = x
What is the slope?
What is the y-intercept?

9. Context

10. Extrapolation Take a look at your poster!
Take a look at the range of your data.
Your line is linear- so it does on and on even past your data points
Predict an output value when you input a large number outside your data range
Put it into context: examples?
This is whats known as extrapolating!

11. Extrapolation
Is the use of a regression line for prediction outside the interval of values of the explanatory variable x used to obtain the line. Such predictions are often not accurate.
“Just because your line behaves the way it does within the confines, does not mean its gets all squirrely later on! We cant predict the behavior of data to extremes.”

12. Example
Some data were collected on the weight of a male white laboratory rat for the first 25 weeks after its birth. A scatterplot of the weight (in grams) and time since birth (in weeks) shows a fairly strong, positive relationship. The linear regression equation weight = (time) models the data fairly well.
1) What is the slope of the regression line? Explain what it means in context
2) what’s the y intercept? Explain in context
3) predict the rat’s weight after 16 weeks, show your work
4) Should you use the line to predict the rat’s weight at age 2 years?

13. Residuals
Look at your graph, how far away are your points from your graph?
Residuals is the difference between an observed value of the response variable and the value predicted by the regression line.
Residual = observed y – predicted y

14. Finding a residual
Find and interpret the residual for the hiker who weighed 187 pounds.
Regression line:
Pack weight = ( 187) = lbs
His actual pack weight was 30 pounds.
Residual = observed – predicted
Residual = 30 – = -3.28
The (-) sign tells us that the observed is below the predicted by 3.28 pounds.
Negative: Below predicted, Positive: above Predicted
Lesson 3.3.1

15. Least-Squares Regression Line
We’ve been using the least-squares regression line this whole time!
We will talk about where the LSRL comes from next class!
But for now…lets learn how to use our tool to make our Stats crunching FAST!

16. LSRL TI-83/ TI-89 TI-83 TI-89 Put your data in L1, and L2
STAT> CALC>#8 >Enter
Did you know your TI-83 will default to using L1 and L2 as our lists, so as long as you put your data in L1 and L2, you don’t have to tell it!
TI-89
Statistics/List Editor> F4 (CALC)>#3> #1
Practice next slide!

17. Practice with your TI Calculator
Body Weight (lbs)
120
187
109
103
131
165
158
116
Backpack Weight (lbs) 26 30 24 29 35 31 28
You Should get: = x

18. Today’s Objective Regression line, introducing “y-hat” Predicted value
Slope
Y-intercept
Extrapolation
Residuals
Least-squares Regression Line
How to use your calculator effectively for time

19. Homework
Worksheet