Notes for an intro on linear regression. Topics include scatterplots of standardized variables, correlation, and prediction.

1. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
v r r2 sx sy
y = + x
____ % of the variability in can CONTEXT
be
explained by variability in . CONTEXT
Gallery
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2. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
CONTEXT CONTEXT
For every EXPLANATORY RESPONSE
,
UNITS UNITS
Oct 18­7:03 AM
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3. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
OBJECTIVES REMINDERS
1. HW Discussion IT Due Tuesday October 23
2. z­score's change with correlation
3. Describing Bivariate Data
(Writing and Quantiatively)
HW
Opening Screen
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4. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
Formulas
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5. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
r = 0.85
zy
Best Fit Line
slope =
y­intercept =
zx
Equation
Moving _____ standard deviations from
the mean in x, moves us _____ standard
deviations from the mean in y.
Standard Deviations
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6. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
Oct 19­10:28 AM
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7. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
Oct 19­10:25 AM
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8. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
r = 0.85
2
x = house size (thousands of ft )
y = house price (in thousands of $)
1. If a house is 1 SD above the mean in size, how many
SD's above the mean would you predict its price to be?
2. What would you predict about a house that's 2 SDs
below average in size?
Application of r to prediction
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9. Ch. 8_1_Linear Regression Intro.notebook October 19, 2007
Wiki Assignment
Post some new vocabulary, a graphing calculator
skill, or answer one of the chapter questions in
the Chapter Outlines Tab.
Wiki Assignments
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