STAT E50  Introduction to Statistics
Exploring Relationships Between Variables
Correlation
examines the linear association between two quantitative variables, and
the strength of that relationship:
Is there a linear relationship between the variables?
Is it positive or negative?
How strong is it?
Regression
describes the linear relationship between the two variables, and makes
it possible to predict the value of the response variable from the value of the
explanatory variable:
What is the relationship?
What does the slope of this linear model tell us?
When is it appropriate to use this linear model to make predictions?
The data shown below was published in
Consumer Reports
in January 1997.
It
includes information about seven kinds of pizza.
The serving size for each is 5 oz.
Pizza
calories
fat (g)
cost ($)
Pizza Hut's Hand Tossed
305
9
1.51
Domino's Deep Dish
382
16
1.53
Pizza Hut's Pan
338
14
1.51
Domino's Hand Tossed
327
9
1.90
Little Caesar's Pan! Pan!
309
10
1.23
Little Caesar's Pizza! Pizza!
313
11
1.28
Pizza Hut's Stuffed
349
13
1.23
1. Suppose you want to see if there is a relationship between calories and fat
content.
Which variable is the explanatory variable?
Which variable is the response variable?
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2. Draw a scatter diagram for this data.
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calories
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 Spring '08
 WEINSTEIN
 Statistics, Correlation, Linear Regression, Regression Analysis, Calories, Errors and residuals in statistics, regression line

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