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Definition. The linear correlation coefficient r measures the strength of the linear relationship between the paired quantitative x- and y- values in a sample. Requirements. 1. The sample of paired ( x, y ) data is a simple random sample of quantitative data.

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Definition

The linear correlation coefficientr measures the strength of the linear relationship between the paired quantitative x- and y-values in a sample.


Requirements
Requirements

1. The sample of paired (x, y) data is a simple random sample of quantitative data.

2. Visual examination of the scatterplot must confirm that the points approximate a straight-line pattern.

3. The outliers must be removed if they are known to be errors. The effects of any other outliers should be considered by calculating r with and without the outliers included.


Notation for the linear correlation coefficient
Notation for the Linear Correlation Coefficient

n = number of pairs of sample data

 denotes the addition of the items indicated.

x denotes the sum of all x-values.

x2 indicates that each x-value should be squared and then those squares added.

(x)2 indicates that the x-values should be added and then the total squared.


Notation for the linear correlation coefficient1
Notation for the Linear Correlation Coefficient

xy indicates that each x-value should be first multiplied by its corresponding y-value. After obtaining all such products, find their sum.

r = linear correlation coefficient for sample data.

 = linear correlation coefficient for population data.


nxy–(x)(y)

r=

n(x2)– (x)2n(y2)– (y)2

Formula

The linear correlation coefficientr measures the strength of a linear relationship between the paired values in a sample.

Computer software or calculators can compute r


Properties of the linear correlation coefficient r
Properties of the Linear Correlation Coefficient r

1. –1 r 1

2. if all values of either variable are converted to a different scale, the value of r does not change.

3. The value of r is not affected by the choice of x and y. Interchange all x- and y-valuesand the value of r will not change.

4. r measures strength of a linear relationship.

5. r is very sensitive to outliers, they can dramatically affect its value.


Example:

Using software or a calculator, r is automatically calculated:


Example:

Using the pizza subway fare costs, we have found that the linear correlation coefficient is r = 0.988. What proportion of the variation in the subway fare can be explained by the variation in the costs of a slice of pizza?

With r = 0.988, we get r2 = 0.976.

We conclude that 0.976 (or about 98%) of the variation in the cost of a subway fares can be explained by the linear relationship between the costs of pizza and subway fares. This implies that about 2% of the variation in costs of subway fares cannot be explained by the costs of pizza.


Common errors involving correlation
Common Errors Involving Correlation

1. Causation: It is wrong to conclude that correlation implies causality.

2. Averages: Averages suppress individual variation and may inflate the correlation coefficient.

3. Linearity: There may be some relationship between x and y even when there is no linear correlation.



Regression
Regression

The regression equation expresses a relationship between x (called the explanatory variable, predictor variable or independent variable), and y (called the response variable or dependent variable).

^

The typical equation of a straight liney = mx + b is expressed in the formy = b0 + b1x, where b0 is the y-intercept and b1 is the slope.

^


Definitions
Definitions

Regression Equation

^

y= b0 + b1x

Given a collection of paired data, the regression equation

algebraically describes the relationship between the two variables.

  • Regression Line

  • The graph of the regression equation is called the regression line (or line of best fit, or leastsquares line).


Notation for regression equation
Notation for Regression Equation

y-intercept of regression equation

Slope of regression equation

Equation of the regression line

0b0

1b1

y = 0 + 1xy = b0 + b1x

^

Population

Parameter

Sample

Statistic


Requirements1
Requirements

1. The sample of paired (x, y) data is a random sample of quantitative data.

2. Visual examination of the scatterplot shows that the points approximate a straight-line pattern.

3. Any outliers must be removed if they are known to be errors. Consider the effects of any outliers that are not known errors.


Formulas for b 0 and b 1
Formulas for b0 and b1

calculators or computers can compute these values

(slope)

(y-intercept)


Example:

Refer to the sample data given in Table below. Use technology to find the equation of the regression line in which the explanatory variable (or x variable) is the cost of a slice of pizza and the response variable (or y variable) is the corresponding cost of a subway fare.


Example:

Requirements are satisfied: simple random sample; scatterplot approximates a straight line; no outliers

Here are results from four different technologies technologies


All of these technologies show that the regression equation can be expressed asy = 0.0346 +0.945x, where y is the predicted cost of a subway fare and x is the cost of a slice of pizza.

^

^

Example:


Example: can be expressed as

Graph the regression equation(from the preceding Example) on the scatterplot of the pizza/subway fare data and examine the graph to subjectively determine how well the regression line fits the data.


Example: can be expressed as


Definition

For a pair of sample can be expressed asx and y values, the residual is the difference between the observed sample value of y and the y-value that is predicted by using the regression equation. That is,

Definition

^

residual = observed y – predicted y = y – y


Residuals
Residuals can be expressed as


Definitions1

A straight line satisfies the can be expressed asleast-squares property if the sum of the squaresof the residuals is the smallest sum possible.

Definitions


Definitions2

A can be expressed asresidual plot is a scatterplot of the (x, y) values after each of they-coordinate values has been replaced by the residual value y – y (where y denotes the predicted value of y). That is, a residual plot is a graph of the points (x, y – y).

Definitions

^

^

^


Residual Plot Analysis can be expressed as

When analyzing a residual plot, look for a pattern in the way the points are configured, and use these criteria:

The residual plot should not have an obvious pattern that is not a straight-line pattern.

The residual plot should not become thicker (or thinner) when viewed from left to right.


Residuals plot pizza subway
Residuals Plot - Pizza/Subway can be expressed as


Residual Plots can be expressed as


Residual Plots can be expressed as


Residual Plots can be expressed as


Definition1
Definition can be expressed as

Coefficient of determination

is the amount of the variation in y that

is explained by the regression line.

explained variation.

r2=

total variation

The value of r2 is the proportion of the variation in y that is explained by the linear relationship between x and y.


Alternate formula for r

( can be expressed as

)

å

2

x

å

=

SS

(

x

)

“sum of squ

ares for

x”

2

=

-

x

n

(

)

å

2

y

å

=

SS

(

y

)

“sum of squ

ares for

y”

2

=

-

y

n

å

å

x

y

å

=

SS

(

xy

)

“sum of squ

ares for

xy”

=

-

xy

n

Alternate Formula for r


Example
Example can be expressed as

  • Example: The table below presents the weight (in thousands of pounds) x and the gasoline mileage (miles per gallon) y for ten different automobiles. Find the linear correlation coefficient:


Completing the calculation for r

- can be expressed as

SS

(

xy

)

42

.

79

=

=

=

-

r

0.

47

SS

(

x

)

SS

(

y

)

(

7

.

449

)(

1116

.

9

)

Completing the Calculation for r


The line of best fit equation

The equation is determined by: can be expressed as

b0: y-intercept

b1: slope

  • Values that satisfy the least squares criterion:

The Line of Best Fit Equation


Example1
Example can be expressed as

  • Example: A recent article measured the job satisfaction of subjects with a 14-question survey. The data below represents the job satisfaction scores, y, and the salaries, x, for a sample of similar individuals:

1) Draw a scatter diagram for this data

2) Find the equation of the line of best fit


Line of best fit

^ can be expressed as

y

SS

(

xy

)

118

.

75

=

=

=

b

0.

5174

1

SS

(

x

)

229

.

5

(

)

å

å

-

×

y

b

x

-

133

(0.

5174

)(

234

)

1

=

=

=

b

1

.

4902

0

n

8

=

+

1

.

49

0.

517

x

Solution 1)

Equation o

f the line

of best f

it:

Line of Best Fit


Scatter diagram

Job Satisfaction Survey can be expressed as

Solution 2)

22

21

20

19

18

Job

Satisfaction

17

16

15

14

13

12

21

23

25

27

29

31

33

35

37

Salary

Scatter Diagram


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