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Transcript of Agresti/Franklin Statistics, 1 of 141 Chapter 12 Multiple Regression Learn…. T o use Multiple...
Agresti/Franklin Statistics, 1 of 141
Chapter 12Multiple Regression
Learn….
To use Multiple Regression Analysis to predict a response variable using more than one explanatory variable.
Agresti/Franklin Statistics, 2 of 141
Section 12.1
How Can We Use Several Variables to Predict a
Response?
Agresti/Franklin Statistics, 3 of 141
Regression Models
The model that contains only two variables, x and y, is called a bivariate model
Agresti/Franklin Statistics, 4 of 141
Regression Models
The regression equation for the bivariate model is:
xy
Agresti/Franklin Statistics, 5 of 141
Regression Models
Suppose there are two predictors, denoted by x1 and x2
This is called a multiple regression model
Agresti/Franklin Statistics, 6 of 141
Regression Models
The regression equation for this multiple regression model with two predictors is:
2211xx
y
Agresti/Franklin Statistics, 7 of 141
Multiple Regression Model
The multiple regression model relates the mean µy of a quantitative response variable y to a set of explanatory variables x1, x2,….
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Multiple Regression Model
Example: For three explanatory variables, the multiple regression equation is:
332211xxx
y
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Multiple Regression Model
Example: The sample prediction equation with three explanatory variables is:
332211ˆ xbxbxbay
Agresti/Franklin Statistics, 10 of 141
Example: Predicting Selling Price Using House and Lot Size
The data set “house selling prices” contains observations on 100 home sales in Florida in November 2003
A multiple regression analysis was done with selling price as the response variable and with house size and lot size as the explanatory variables
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Example: Predicting Selling Price Using House and Lot Size
Output from the analysis:
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Example: Predicting Selling Price Using House and Lot Size
Prediction Equation:
where y = selling price, x1=house size and x2 = lot size
2184.28.53536,10ˆ xxy
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Example: Predicting Selling Price Using House and Lot Size
One house listed in the data set had house size = 1240 square feet, lot size = 18,000 square feet and selling price = $145,000
Find its predicted selling price:
276,107
)000,18(84.2)1240(8.53536,10ˆ
y
Agresti/Franklin Statistics, 14 of 141
Example: Predicting Selling Price Using House and Lot Size
Find its residual:
The residual tells us that the actual selling price was $37,724 higher than predicted
724,37276,107000,145ˆ yy
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The Number of Explanatory Variables
You should not use many explanatory variables in a multiple regression model unless you have lots of data
A rough guideline is that the sample size n should be at least 10 times the number of explanatory variables
Agresti/Franklin Statistics, 16 of 141
Plotting Relationships
Always look at the data before doing a multiple regression
Most software has the option of constructing scatterplots on a single graph for each pair of variables• This is called a scatterplot matrix
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Plotting Relationships
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Interpretation of Multiple Regression Coefficients
The simplest way to interpret a multiple regression equation looks at it in two dimensions as a function of a single explanatory variable
We can look at it this way by fixing values for the other explanatory variable(s)
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Interpretation of Multiple Regression Coefficients
Example using the housing data: Suppose we fix x1 = house size at 2000
square feet The prediction equation becomes:
2
2
2.84x97,022
84.2)2000(8.53536,10ˆ
xy
Agresti/Franklin Statistics, 20 of 141
Interpretation of Multiple Regression Coefficients
Since the slope coefficient of x2 is 2.84, the predicted selling price for 2000 square foot houses increases by $2.84 for every square foot increase in lot size
For a 1000 square-foot increase in lot size, the predicted selling price of 2000 sq. ft. houses increases by 1000(2.84) = $2840
Agresti/Franklin Statistics, 21 of 141
Interpretation of Multiple Regression Coefficients
Example using the housing data: Suppose we fix x2 = lot size at 30,000
square feet The prediction equation becomes:
1
1
53.874,676
)000,30(84.28.53536,10ˆ
x
xy
Agresti/Franklin Statistics, 22 of 141
Interpretation of Multiple Regression Coefficients
Since the slope coefficient of x1 is 53.8, the predicted selling price for houses with a lot size of 30,000 sq. ft. increases by $53.80 for every square foot increase in house size
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Interpretation of Multiple Regression Coefficients
In summary, an increase of a square foot in house size has a larger impact on the selling price ($53.80) than an increase of a square foot in lot size ($2.84)
We can compare slopes for these explanatory variables because their units of measurement are the same (square feet)
Slopes cannot be compared when the units differ
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Summarizing the Effect While Controlling for a Variable
The multiple regression model assumes that the slope for a particular explanatory variable is identical for all fixed values of the other explanatory variables
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Summarizing the Effect While Controlling for a Variable
For example, the coefficient of x1 in the prediction equation:
is 53.8 regardless of whether we plug in x2 = 10,000 or x2 = 30,000 or x2 = 50,000
2184.28.53536,10ˆ xxy
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Summarizing the Effect While Controlling for a Variable
Agresti/Franklin Statistics, 27 of 141
Slopes in Multiple Regression and in Bivariate Regression
In multiple regression, a slope describes the effect of an explanatory variable while controlling effects of the other explanatory variables in the model
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Slopes in Multiple Regression and in Bivariate Regression
Bivariate regression has only a single explanatory variable
A slope in bivariate regression describes the effect of that variable while ignoring all other possible explanatory variables
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Importance of Multiple Regression
One of the main uses of multiple regression is to identify potential lurking variables and control for them by including them as explanatory variables in the model
Agresti/Franklin Statistics, 30 of 141
For all students at Walden Univ., the prediction equation for y = college GPA and x1= H.S. GPA and x2= study time is:
Find the predicted college GPA of a student who has a H.S. GPA of 3.5 and who studies 3 hrs. per day.
a. 3.67b. 3.005c. 3.175d. 3.4
Agresti/Franklin Statistics, 31 of 141
For students with fixed study time, what is the change in predicted college GPA when H.S. GPA increases from 3.0 to 4.0?
a. 1.13b. 0.0078c. 0.643d. 1.00
For all students at Walden Univ., the prediction equation for y = college GPA and x1= H.S. GPA and x2= study time is:
Agresti/Franklin Statistics, 32 of 141
Section 12.2
Extending the Correlation and R-Squared for Multiple Regression
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Multiple Correlation
To summarize how well a multiple regression model predicts y, we analyze how well the observed y values correlate with the predicted y values
The multiple correlation is the correlation between the observed y values and the predicted y values• It is denoted by R
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Multiple Correlation
For each subject, the regression equation provides a predicted value
Each subject has an observed y-value and a predicted y-value
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Multiple Correlation
The correlation computed between all pairs of observed y-values and predicted y-values is the multiple correlation, R
The larger the multiple correlation, the better are the predictions of y by the set of explanatory variables
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Multiple Correlation
The R-value always falls between 0 and 1
In this way, the multiple correlation ‘R’ differs from the bivariate correlation ‘r’ between y and a single variable x, which falls between -1 and +1
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R-squared
For predicting y, the square of R describes the relative improvement from using the prediction equation instead of using the sample mean, y
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R-squared
The error in using the prediction equation to predict y is summarized by the residual sum of squares:
2)ˆ( yy
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R-squared The error in using y to predict y is
summarized by the total sum of squares:
2)( yy
Agresti/Franklin Statistics, 40 of 141
R-squared
The proportional reduction in error is:
2
22
2
)(
)ˆ()(
yy
yyyyR
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R-squared
The better the predictions are using the regression equation, the larger R2 is
For multiple regression, R2 is the square of the multiple correlation, R
Agresti/Franklin Statistics, 42 of 141
Example: How Well Can We Predict House Selling Prices?
For the 100 observations on y = selling price, x1 = house size, and x2 = lot size, a table, called the ANOVA (analysis of variance) table was created
The table displays the sums of squares in the SS column
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Example: How Well Can We Predict House Selling Prices?
The R2 value can be created from the sums of squares in the table
711.090,756
90,756-314,433
)(
)ˆ()(2
22
2
yy
yyyyR
Agresti/Franklin Statistics, 44 of 141
Example: How Well Can We Predict House Selling Prices?
Using house size and lot size together to predict selling price reduces the prediction error by 71%, relative to using y alone to predict selling price
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Example: How Well Can We Predict House Selling Prices?
Find and interpret the multiple correlation
There is a strong association between the observed and the predicted selling prices
House size and lot size very much help us to predict selling prices
84.0711.02 RR
Agresti/Franklin Statistics, 46 of 141
Example: How Well Can We Predict House Selling Prices?
If we used a bivariate regression model to predict selling price with house size as the predictor, the r2
value would be 0.58
If we used a bivariate regression model to predict selling price with lot size as the predictor, the r2 value would be 0.51
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Example: How Well Can We Predict House Selling Prices?
The multiple regression model has R2 0.71, so it provides better predictions than either bivariate model
Agresti/Franklin Statistics, 48 of 141
Properties of R2
The previous example showed that R2
for the multiple regression model was larger than r2 for a bivariate model using only one of the explanatory variables
A key factor of R2 is that it cannot decrease when predictors are added to a model
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Properties of R2
R2 falls between 0 and 1
The larger the value, the better the explanatory variables collectively predict y
R2 =1 only when all residuals are 0, that is, when all regression predictions are prefect
R2 = 0 when the correlation between y and
each explanatory variable equals 0
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Properties of R2
R2 gets larger, or at worst stays the same, whenever an explanatory variable is added to the multiple regression model
The value of R2 does not depend on the units of measurement
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R2 Values for Various Multiple Regression Models
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R2 Values for Various Multiple Regression Models
The single predictor in the data set that is most strongly associated with y is the house’s real estate tax assessment• (r2 = 0.679)
When we add house size as a second predictor, R2 goes up from 0.679 to 0.730
As other predictors are added, R2 continues to go up, but not by much
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R2 Values for Various Multiple Regression Models
R2 does not increase much after a few predictors are in the model
When there are many explanatory variables but the correlations among them are strong, once you have included a few of them in the model, R2 usually doesn’t increase much more when you add additional ones
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R2 Values for Various Multiple Regression Models
This does not mean that the additional variables are uncorrelated with the response variable
It merely means that they don’t add much new power for predicting y, given the values of the predictors already in the model
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In a data set used to predict body weight (in pounds), three predictors were used: height, percent body fat and age.
Their correlations with total body weight were:
Height: 0.745 Percent Body fat: 0.390 Age: -0.187
Which explanatory variable gives by itself the best prediction of weight?
a. Heightb. Percent body fatc. Age
Agresti/Franklin Statistics, 56 of 141
With height as the sole predictor, what is r2?
a. .745b. .555c. .625d. .825
In a data set used to predict body weight (in pounds), three predictors were used: height, percent body fat and age.
Their correlations with total body weight were:
Height: 0.745 Percent Body fat: 0.390 Age: -0.187
Agresti/Franklin Statistics, 57 of 141
If Percent Body Fat is added to the model R2 = 0.66. If Age is then added to the model R2=0.67. Once you know height and % body fat, does age seem to help in predicting weight?
a. Nob. Yes
In a data set used to predict body weight (in pounds), three predictors were used: height, percent body fat and age.
Their correlations with total body weight were:
Height: 0.745 Percent Body fat: 0.390 Age: -0.187
Agresti/Franklin Statistics, 58 of 141
Section 12.3
How Can We Use Multiple Regression to Make Inferences?
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Inferences about the Population
Assumptions required when using a multiple regression model to make inferences about the population:• The regression equation truly holds for the
population means
• This implies that there is a straight-line relationship between the mean of y and each explanatory variable, with the same slope at each value of the other predictors
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Inferences about the Population
Assumptions required when using a multiple regression model to make inferences about the population:• The data were gathered using
randomization
• The response variable y has a normal distribution at each combination of values of the explanatory variables, with the same standard deviation
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Inferences about Individual Regression Parameters
Consider a particular parameter, β1
If β1= 0, the mean of y is identical for all values of x1, at fixed values of the other explanatory variables
So, H0: β1= 0 states that y and x1 are statistically independent, controlling for the other variables
This means that once the other explanatory variables are in the model, it doesn’t help to have x1 in the model
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Significance Test about a Multiple Regression Parameter
1. Assumptions:• Each explanatory variable has a straight-
line relation with µy with the same slope for all combinations of values of other predictors in the model
• Data gathered with randomization
• Normal distribution for y with same standard deviation at each combination of values of other predictors in model
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Significance Test about a Multiple Regression Parameter
2. Hypotheses:
• H0: β1= 0
• Ha: β1≠ 0
• When H0 is true, y is independent of x1, controlling for the other predictors
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Significance Test about a Multiple Regression Parameter
3. Test Statistic:
se
bt
01
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Significance Test about a Multiple Regression Parameter
4. P-value: Two-tail probability from t- distribution of values larger than observed t test statistic (in absolute value)
The t-distribution has:
df = n – number of parameters in the regression equation
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Significance Test about a Multiple Regression Parameter
5. Conclusion: Interpret P-value; compare to significance level if decision needed
Agresti/Franklin Statistics, 67 of 141
Example: What Helps Predict a Female Athlete’s Weight?
The “College Athletes” data set comes from a study of 64 University of Georgia female athletes
The study measured several physical characteristics, including total body weight in pounds (TBW), height in inches (HGT), the percent of body fat (%BF) and age
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Example: What Helps Predict a Female Athlete’s Weight?
The results of fitting a multiple regression model for predicting weight using the other variables:
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Example: What Helps Predict a Female Athlete’s Weight?
Interpret the effect of age on weight in the multiple regression equation:
321
32
1
96.036.143.37.97ˆThen
age and fat,body %
height, weight,predicted ˆLet
xxxy
xx
xy
Agresti/Franklin Statistics, 70 of 141
Example: What Helps Predict a Female Athlete’s Weight?
The slope coefficient of age is -0.96
For athletes having fixed values for x1
and x2, the predicted weight decreases by 0.96 pounds for a 1-year increase in age, and the ages vary only between 17 and 23
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Example: What Helps Predict a Female Athlete’s Weight?
Run a hypothesis test to determine whether age helps to predict weight, if you already know height and percent body fat
Agresti/Franklin Statistics, 72 of 141
Example: What Helps Predict a Female Athlete’s Weight?
1. Assumptions:
• The 64 female athletes were a convenience sample, not a random sample
• Caution should be taken when making inferences about all female college athletes
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Example: What Helps Predict a Female Athlete’s Weight?
2. Hypotheses:
• H0: β3= 0
• Ha: β3≠ 0
3. Test statistic:
48.1648.0
960.003 se
bt
Agresti/Franklin Statistics, 74 of 141
Example: What Helps Predict a Female Athlete’s Weight?
4. P-value: This value is reported in the output as 0.14
5. Conclusion:
• The P-value of 0.14 does not give much evidence against the null hypothesis that β3 = 0• Age does not significantly predict weight
if we already know height and % body fat
Agresti/Franklin Statistics, 75 of 141
Confidence Interval for a Multiple Regression Parameter
A 95% confidence interval for a β slope parameter in multiple regression equals:
The t-score has: df = (n - # of parameters in the model)
)( t slope Estimated.025
se
Agresti/Franklin Statistics, 76 of 141
Example: What’s Plausible for the Effect of Age on Weight?
Construct and interpret a 95% CI for β3, the effect of age while controlling for height and % body fat
)3.0,3.2(30.196.0
)648.0(00.296.0)(025.3
setb
Agresti/Franklin Statistics, 77 of 141
Example: What’s Plausible for the Effect of Age on Weight?
At fixed values of x1 and x2, we infer that the population mean of weight changes very little (and maybe not at all) for a 1-year increase in age
The confidence interval contains 0• Age may have no effect on weight, once
we control for height and % body fat
Agresti/Franklin Statistics, 78 of 141
Estimating Variability Around the Regression Equation
A standard deviation parameter, σ, describes variability of the observations around the regression equation
Its sample estimate is:
eq.) reg.in parameters of (#
)ˆ(
esidual
2
n
yy
df
SSRs
Agresti/Franklin Statistics, 79 of 141
Example: Estimating Variability of Female Athletes’ Weight
Anova Table for the “college athletes” data set:
Agresti/Franklin Statistics, 80 of 141
Example: Estimating Variability of Female Athletes’ Weight For female athletes at particular values of height,
% of body fat, and age, estimate the standard deviation of their weights
Begin by finding the Mean Square Error:
Notice that this value (102.2) appears in the MS column in the ANOVA table
2.10260
0.6131 2 df
SSresiduals
Agresti/Franklin Statistics, 81 of 141
Example: Estimating Variability of Female Athletes’ Weight
The standard deviation is:
This value is also displayed in the ANOVA table
For athletes with certain fixed values of height, % body fat, and age, the weights vary with a standard deviation of about 10 pounds
1.102.102 s
Agresti/Franklin Statistics, 82 of 141
Example: Estimating Variability of Female Athletes’ Weight
If the conditional distributions of weight are approximately bell-shaped, about 95% of the weight values fall within about 2s = 20 pounds of the true regression line
Agresti/Franklin Statistics, 83 of 141
Do the Explanatory Variables Collectively Have an Effect?
Example: With 3 predictors in a model, we can check this by testing:
0parameter oneleast At :
0:3210
aH
H
Agresti/Franklin Statistics, 84 of 141
Do the Explanatory Variables Collectively Have an Effect?
The test statistic for H0 is denoted by F
e errorMean squar
regressionforsquareMeanF
Agresti/Franklin Statistics, 85 of 141
Do the Explanatory Variables Collectively Have an Effect?
When H0 is true, the expected value of the F test statistic is approximately 1
When H0 is false, F tends to be larger than 1
The larger the F test statistic, the stronger the evidence against H0
Agresti/Franklin Statistics, 86 of 141
Summary of F Test That All βeta Parameters = 0
1. Assumptions: Multiple regression equation holds, data gathered randomly, normal distribution for y with same standard deviation at each combination of predictors
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Summary of F Test That All βeta Parameters = 0
2.
3. Test statistic:
0parameter oneleast At :
0:3210
aH
H
e errorMean squar
regressionforsquareMeanF
Agresti/Franklin Statistics, 88 of 141
Summary of F-Test That All βeta Parameters = 0
4. P-value: Right-tail probability above observed F-test statistic value from F- distribution with:
• df1 = number of explanatory variables
• df2 = n – (number of parameters in regression equation)
Agresti/Franklin Statistics, 89 of 141
Summary of F-Test That All βeta Parameters = 0
5. Conclusion: The smaller the P-value, the stronger the evidence that at least one explanatory variable has an effect on y
• If a decision is needed, reject H0 if P-value ≤ significance level, such as 0.05
Agresti/Franklin Statistics, 90 of 141
Example: The F-Test for Predictors of Athletes’ Weight
For the 64 female college athletes, the regression model for predicting y = weight using x1 = height, x2 = % body fat and x3 = age is summarized in the ANOVA table on the next page
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Example: The F-Test for Predictors of Athletes’ Weight
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Example: The F-Test for Predictors of Athletes’ Weight
Use the output in the ANOVA table to test the hypothesis:
0parameter oneleast At :
0:3210
aH
H
Agresti/Franklin Statistics, 93 of 141
Example: The F-Test for Predictors of Athletes’ Weight
The observed F statistic is 40.48 The corresponding P-value is 0.000 We can reject H0 at the 0.05
significance level We conclude that at least one
predictor has an effect on weight
Agresti/Franklin Statistics, 94 of 141
Example: The F-Test for Predictors of Athletes’ Weight
The F-test tells us that at least one explanatory variable has an effect
If the explanatory variables are chosen sensibly, at least one should have some predictive power
The F-test result tells us whether there is sufficient evidence to make it worthwhile to consider the individual effects, using t-tests
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Example: The F-Test for Predictors of Athletes’ Weight
The individual t-tests identify which of the variables are significant (controlling for the other variables)
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Example: The F-Test for Predictors of Athletes’ Weight
If a variable turns out not to be significant, it can be removed from the model
In this example, ‘age’ can be removed from the model
Agresti/Franklin Statistics, 97 of 141
Section 12.4
Checking a Regression Model Using Residual Plots
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Assumptions for Inference with a Multiple Regression Model
• The regression equation approximates well the true relationship between the predictors and the mean of y
• The data were gathered randomly
• y has a normal distribution with the same standard deviation at each combination of predictors
Agresti/Franklin Statistics, 99 of 141
Checking Shape and Detecting Unusual Observations
To test Assumption 3 (the conditional distribution of y is normal at any fixed values of the explanatory variables):• Construction a histogram of the standardized
residuals
• The histogram should be approximately bell-shaped
• Nearly all the standardized residuals should fall between -3 and +3. Any residual outside these limits is a potential outlier
Agresti/Franklin Statistics, 100 of 141
Example: Residuals for House Selling Price
For the house selling price data, a MINITAB histogram of the standardized residuals for the multiple regression model predicting selling price by the house size and the lot size was created and is displayed on the following page
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Example: Residuals for House Selling Price
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Example: Residuals for House Selling Price
The residuals are roughly bell shaped about 0
They fall between about -3 and +3 No severe nonnormality is indicated
Agresti/Franklin Statistics, 103 of 141
Plotting Residuals against Each Explanatory Variable
Plots of residuals against each explanatory variable help us check for potential problems with the regression model
Ideally, the residuals should fluctuate randomly about 0
There should be no obvious change in trend or change in variation as the values of the explanatory variable increases
Agresti/Franklin Statistics, 104 of 141
Plotting Residuals against Each Explanatory Variable
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Section 12.5
How Can Regression Include Categorical Predictors?
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Indicator Variables
Regression models specify categories of a categorical explanatory variable using artificial variables, called indicator variables
The indicator variable for a particular category is binary• It equals 1 if the observation falls into that
category and it equals 0 otherwise
Agresti/Franklin Statistics, 107 of 141
Indicator Variables
In the house selling prices data set, the city region in which a house is located is a categorical variable
The indicator variable x for region is • x = 1 if house is in NW (northwest region)
• x = 0 if house is not in NW
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Indicator Variables
The coefficient β of the indicator variable x is the difference between the mean selling prices for homes in the NW and for homes not in the NW
Agresti/Franklin Statistics, 109 of 141
Example: Including Region in Regression for House Selling Price
Output from the regression model for selling price of home using house size and region
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Example: Including Region in Regression for House Selling Price
Find and plot the lines showing how predicted selling price varies as a function of house size, for homes in the NW and for homes no in the NW
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Example: Including Region in Regression for House Selling Price
The regression equation from the MINITAB output is:
21569,300.78258,15ˆ xxy
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Example: Including Region in Regression for House Selling Price
For homes not in the NW, x2 = 0 The prediction equation then simplifies to:
1
1
78.015,258-
)0(569,300.78258,15ˆ
x
xy
Agresti/Franklin Statistics, 113 of 141
Example: Including Region in Regression for House Selling Price
For homes in the NW, x2 = 1 The prediction equation then simplifies to:
1
1
78.015,311
)1(569,300.78258,15ˆ
x
xy
Agresti/Franklin Statistics, 114 of 141
Example: Including Region in Regression for House Selling Price
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Example: Including Region in Regression for House Selling Price
Both lines have the same slope, 78 For homes in the NW and for homes
not in the NW, the predicted selling price increases by $78 for each square-foot increase in house size
The figure portrays a separate line for each category of region (NW, not NW)
Agresti/Franklin Statistics, 116 of 141
Example: Including Region in Regression for House Selling Price
The coefficient of the indicator variable is 30569
For any fixed value of house size, we predict that the selling price is $30,569 higher for homes in the NW
Agresti/Franklin Statistics, 117 of 141
Example: Including Region in Regression for House Selling Price
The line for homes in the NW is above the line for homes not in the NW
The predicted selling price is higher for homes in the NW
The P-value of 0.000 for the test for the coefficient of the indicator variable suggests that this difference is statistically significant
Agresti/Franklin Statistics, 118 of 141
Is There Interaction?
For two explanatory variables, interaction exists between them in their effects on the response variable when the slope of the relationship between µy and one of them changes as the value of the other changes
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Is There Interaction?
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Section 12.6
How Can We Model a Categorical Response?
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Modeling a Categorical Response Variable
When y is categorical, a different regression model applies, called a logistic regression
Agresti/Franklin Statistics, 122 of 141
Examples of Logistic Regression
A voter’s choice in an election (Democrat or Republican), with explanatory variables: annual income, political ideology, religious affiliation, and race
Whether a credit card holder pays their bill on time (yes or no), with explanatory variables: family income and the number of months in the past year that the customer paid the bill on time
Agresti/Franklin Statistics, 123 of 141
The Logistic Regression Model Denote the possible outcomes for y as 0 and 1 Use the generic terms failure (for outcome = 0)
and success (for outcome =1)
The population mean of the scores equals the population proportion of ‘1’ outcomes (successes) • That is, µy = p
The proportion, p, also represents the probability that a randomly selected subject has a success outcome
Agresti/Franklin Statistics, 124 of 141
The Logistic Regression Model
The straight-line model is usually inadequate
A more realistic model has a curved S-shape instead of a straight-line trend
Agresti/Franklin Statistics, 125 of 141
The Logistic Regression Model
A regression equation for an S-shaped curve for the probability of success p is:
)(
)(
1 x
x
e
ep
Agresti/Franklin Statistics, 126 of 141
Example: Annual Income and Having a Travel Credit Card
An Italian study with 100 randomly selected Italian adults considered factors that are associated with whether a person possesses at least one travel credit card
The table on the next page shows results for the first 15 people on this response variable and on the person’s annual income (in thousands of euros)
Agresti/Franklin Statistics, 127 of 141
Example: Annual Income and Having a Travel Credit Card
Agresti/Franklin Statistics, 128 of 141
Example: Annual Income and Having a Travel Credit Card
Let x = annual income and let y = whether the person possesses a travel credit card (1 = yes, 0 = no)
Agresti/Franklin Statistics, 129 of 141
Example: Annual Income and Having a Travel Credit Card
Substituting the α and β estimates into the logistic regression model formula yields:
)105.052.3(
)105.052.3(
1ˆ
x
x
e
ep
Agresti/Franklin Statistics, 130 of 141
Example: Annual Income and Having a Travel Credit Card
Find the estimated probability of possessing a travel credit card at the lowest and highest annual income levels in the sample, which were x = 12 and x = 65
Agresti/Franklin Statistics, 131 of 141
Example: Annual Income and Having a Travel Credit Card
For x = 12 thousand euros, the estimated probability of possessing a travel credit card is:
09.01.104
0.104
11ˆ
26.2
26.2
)12(05.152.3
)12(105.052.3
e
e
e
ep
Agresti/Franklin Statistics, 132 of 141
Example: Annual Income and Having a Travel Credit Card
For x = 65 thousand euros, the estimated probability of possessing a travel credit card is:
97.028.2485
27.2485
11ˆ
305.3
305.3
)65(05.152.3
)65(105.052.3
e
e
e
ep
Agresti/Franklin Statistics, 133 of 141
Example: Annual Income and Having a Travel Credit Card
Annual income has a strong positive effect on having a credit card
The estimated probability of having a travel credit card changes from 0.09 to 0.97 as annual income changes over its range
Agresti/Franklin Statistics, 134 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
A three-variable contingency table from a survey of senior high-school students in shown on the next page
The students were asked whether they had ever used: alcohol, cigarettes or marijuana
Agresti/Franklin Statistics, 135 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
Agresti/Franklin Statistics, 136 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
Let y indicate marijuana use, coded: (1 = yes, 0 = no)
Let x1 be an indicator variable for alcohol use (1 = yes, 0 = no)
Let x2 be an indicator variable for cigarette use (1 = yes, 0 = no)
Agresti/Franklin Statistics, 137 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
Agresti/Franklin Statistics, 138 of 141
Example:Estimating Proportion of Students Who’ve Used Marijuana
The logistic regression prediction equation is:
21
21
85.299.231.5
85.299.231.5
1ˆ
xx
xx
e
ep
Agresti/Franklin Statistics, 139 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
For those who have not used alcohol or cigarettes, x1= x2 = 0 and:
005.01
ˆ)0(85.2)0(99.231.5
)0(85.2)0(99.231.5
e
ep
Agresti/Franklin Statistics, 140 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
For those who have used alcohol and cigarettes, x1= x2 = 1 and:
628.01
ˆ)1(85.2)1(99.231.5
)1(85.2)1(99.231.5
e
ep
Agresti/Franklin Statistics, 141 of 141
Example: Estimating Proportion of Students Who’ve Used Marijuana
The probability that students have tried marijuana seems to depend greatly on whether they’ve used alcohol and cigarettes