chapter 3 association: contingency, correlation, and regression

Agresti/Franklin Statistics, 1 of 52

Chapter 3Association: Contingency,

Correlation, and Regression

Learn ….

How to examine links between two variables


Section 3.2

How Can We Explore the Association Between Two Quantitative

Variables?


Scatterplot Graphical display of two quantitative

variables:

• Horizontal Axis: Explanatory variable, x

• Vertical Axis: Response variable, y


Example: Internet Usage and Gross National Product (GDP)


Positive Association Two quantitative variables, x and y, are

said to have a positive association when high values of x tend to occur with high values of y, and when low values of x tend to occur with low values of y


Negative Association

Two quantitative variables, x and y, are said to have a negative association when high values of x tend to occur with low values of y, and when low values of x tend to occur with high values of y


Example: Did the Butterfly Ballot Cost Al Gore the 2000 Presidential Election?


Linear Correlation: r Measures the strength of the linear

association between x and y

• A positive r-value indicates a positive association• A negative r-value indicates a negative association• An r-value close to +1 or -1 indicates a strong linear

association• An r-value close to 0 indicates a weak association


Calculating the correlation, r

))((11

yx syy

sxx

nr


Example: 100 cars on the lot of a used-car dealership

Would you expect a positive association, a negative association or no association between the age of the car and the mileage on the odometer? Positive association Negative association No association


Section 3.3

How Can We Predict the Outcome of a Variable?


Regression Line Predicts the value for the response

variable, y, as a straight-line function of the value of the explanatory variable, x

bxay ˆ


Example: How Can Anthropologists Predict Height Using Human Remains?

Regression Equation:

is the predicted height and is the length of a femur (thighbone), measured in centimeters

xy 4.24.61ˆ

y x


Example: How Can Anthropologists Predict Height Using Human Remains?

Use the regression equation to predict the height of a person whose femur length was 50 centimeters

ˆ 61.4 2.4(50)y


Interpreting the y-Intercept

y-Intercept: • the predicted value for y when x = 0

• helps in plotting the line

• May not have any interpretative value if no observations had x values near 0


Interpreting the Slope Slope: measures the change in the

predicted variable for every unit change in the explanatory variable

Example: A 1 cm increase in femur length results in a 2.4 cm increase in predicted height


Slope Values: Positive, Negative, Equal to 0


Residuals Measure the size of the prediction

errors

Each observation has a residual

Calculation for each residual:ˆy y


Residuals

A large residual indicates an unusual observation

Large residuals can easily be found by constructing a histogram of the residuals


“Least Squares Method” Yields the Regression Line

Residual sum of squares:

The optimal line through the data is the line that minimizes the residual sum of squares

2 2ˆ( ) ( )residuals y y


Regression Formulas for y-Intercept and Slope

Slope:

Y-Intercept:

( )yx

sb r

s

( )a y b x


The Slope and the Correlation Correlation:

• Describes the strength of the association between 2 variables

• Does not change when the units of measurement change

• It is not necessary to identify which variable is the response and which is the explanatory


The Slope and the Correlation Slope:

• Numerical value depends on the units used to measure the variables

• Does not tell us whether the association is strong or weak

• The two variables must be identified as response and explanatory variables

• The regression equation can be used to predict the response variable


Section 3.4

What Are Some Cautions in Analyzing Associations?


Extrapolation Extrapolation: Using a regression line

to predict y-values for x-values outside the observed range of the data• Riskier the farther we move from the range

of the given x-values• There is no guarantee that the relationship

will have the same trend outside the range of x-values


Regression Outliers

Construct a scatterplot

Search for data points that are well removed from the trend that the rest of the data points follow


Influential Observation An observation that has a large effect on

the regression analysis

Two conditions must hold for an observation to be influential:

Its x-value is relatively low or high compared to the rest of the data

It is a regression outlier, falling quite far from the trend that the rest of the data follow


Which Regression Outlier is Influential?


Example: Does More Education Cause More Crime?


Correlation does not Imply Causation

A correlation between x and y means that there is a linear trend that exists between the two variables

A correlation between x and y, does not mean that x causes y


Lurking Variable

A lurking variable is a variable, usually unobserved, that influences the association between the variables of primary interest


Simpson’s Paradox

The direction of an association between two variables can change after we include a third variable and analyze the data at separate levels of that variable


Example: Is Smoking Actually Beneficial to Your Health?



An association can look quite different after adjusting for the effect of a third variable by grouping the data according to the values of the third variable


Data are available for all fires in Chicago last year on x = number of firefighters at the fires and y = cost of damages due to fire

Would you expect the correlation to be negative, zero, or positive?

a. Negativeb. Zeroc. Positive


If the correlation is positive, does this mean that having more firefighters at a fire causes the damages to be worse?

a. Yesb.No



Identify a third variable that could be considered a common cause of x and y:

a. Distance from the fire stationb. Intensity of the firec. Time of day that the fire was

discovered


chapter 3 association: contingency, correlation, and regression

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