• 8.33 David Moste
• Chitrarth Kaushik

allbacks <- read.csv('../course_data/allbacks.csv')
head(allbacks)

##   X volume area weight cover
## 1 1    885  382    800    hb
## 2 2   1016  468    950    hb
## 3 3   1125  387   1050    hb
## 4 4    239  371    350    hb
## 5 5    701  371    750    hb
## 6 6    641  367    600    hb

From: Maindonald, J.H. & Braun, W.J. (2007). Data Analysis and Graphics Using R, 2nd ed.

summary(lm.out)

## 
## Call:
## lm(formula = weight ~ volume, data = allbacks)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -189.97 -109.86   38.08  109.73  145.57 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 107.67931   88.37758   1.218    0.245    
## volume        0.70864    0.09746   7.271 6.26e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 123.9 on 13 degrees of freedom
## Multiple R-squared:  0.8026, Adjusted R-squared:  0.7875 
## F-statistic: 52.87 on 1 and 13 DF,  p-value: 6.262e-06

lm.out2 <- lm(weight ~ volume + cover, data=allbacks)
summary(lm.out2)

## 
## Call:
## lm(formula = weight ~ volume + cover, data = allbacks)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -110.10  -32.32  -16.10   28.93  210.95 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  197.96284   59.19274   3.344 0.005841 ** 
## volume         0.71795    0.06153  11.669  6.6e-08 ***
## coverpb     -184.04727   40.49420  -4.545 0.000672 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 78.2 on 12 degrees of freedom
## Multiple R-squared:  0.9275, Adjusted R-squared:  0.9154 
## F-statistic: 76.73 on 2 and 12 DF,  p-value: 1.455e-07

\[ \hat{weight} = 198 + 0.72 volume - 184 coverpb \]

1. For hardcover books: plug in 0 for cover.
   \[ \hat{weight} = 197.96 + 0.72 volume - 184.05 \times 0 = 197.96 + 0.72 volume \]

2. For paperback books: put in 1 for cover. \[ \hat{weight} = 197.96 + 0.72 volume - 184.05 \times 1 \]

• Slope of volume: All else held constant, books that are 1 more cubic centimeter in volume tend to weigh about 0.72 grams more.
• Slope of cover: All else held constant, the model predicts that paperback books weigh 184 grams lower than hardcover books.
• Intercept: Hardcover books with no volume are expected on average to weigh 198 grams.
  • Obviously, the intercept does not make sense in context. It only serves to adjust the height of the line.

poverty <- read.table("../course_data/poverty.txt", h = T, sep = "\t")
names(poverty) <- c("state", "metro_res", "white", "hs_grad", "poverty", "female_house")
poverty <- poverty[,c(1,5,2,3,4,6)]
head(poverty)

##        state poverty metro_res white hs_grad female_house
## 1    Alabama    14.6      55.4  71.3    79.9         14.2
## 2     Alaska     8.3      65.6  70.8    90.6         10.8
## 3    Arizona    13.3      88.2  87.7    83.8         11.1
## 4   Arkansas    18.0      52.5  81.0    80.9         12.1
## 5 California    12.8      94.4  77.5    81.1         12.6
## 6   Colorado     9.4      84.5  90.2    88.7          9.6

From: Gelman, H. (2007). Data Analysis using Regression and Multilevel/Hierarchial Models. Cambridge University Press.

lm.poverty <- lm(poverty ~ female_house, data=poverty)
summary(lm.poverty)

## 
## Call:
## lm(formula = poverty ~ female_house, data = poverty)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7537 -1.8252 -0.0375  1.5565  6.3285 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    3.3094     1.8970   1.745   0.0873 .  
## female_house   0.6911     0.1599   4.322 7.53e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.664 on 49 degrees of freedom
## Multiple R-squared:  0.276,  Adjusted R-squared:  0.2613 
## F-statistic: 18.68 on 1 and 49 DF,  p-value: 7.534e-05

\(R^2\) can be calculated in three ways:

1. square the correlation coefficient of x and y (how we have been calculating it)
2. square the correlation coefficient of y and \(\hat{y}\)
3. based on definition:
   \[ R^2 = \frac{explained \quad variability \quad in \quad y}{total \quad variability \quad in \quad y} \]

Using ANOVA we can calculate the explained variability and total variability in y.

anova.poverty <- anova(lm.poverty)
print(xtable(anova.poverty, digits = 2), type='html')

Sum of squares of y: \({ SS }_{ Total }=\sum { { \left( y-\bar { y } \right) }^{ 2 } } =480.25\) → total variability

Sum of squares of residuals: \({ SS }_{ Error }=\sum { { e }_{ i }^{ 2 } } =347.68\) → unexplained variability

Sum of squares of x: \({ SS }_{ Model }={ SS }_{ Total }-{ SS }_{ Error } = 132.57\) → explained variability

\[ R^2 = \frac{explained \quad variability \quad in \quad y}{total \quad variability \quad in \quad y} = \frac{132.57}{480.25} = 0.28 \]

• For single-predictor linear regression, having three ways to calculate the same value may seem like overkill.
• However, in multiple linear regression, we can’t calculate \(R^2\) as the square of the correlation between x and y because we have multiple xs.
• And next we’ll learn another measure of explained variability, adjusted \(R^2\), that requires the use of the third approach, ratio of explained and unexplained variability.

lm.poverty2 <- lm(poverty ~ female_house + white, data=poverty)
print(xtable(lm.poverty2), type='html')

anova.poverty2 <- anova(lm.poverty2)
print(xtable(anova.poverty2, digits = 2), type='html')

\[ R^2 = \frac{explained \quad variability \quad in \quad y}{total \quad variability \quad in \quad y} = \frac{132.57 + 8.21}{480.25} = 0.29 \]

poverty vs % female head of household

poverty vs % female head of household and % female household

Note the difference in the estimate for female_house.

• Two predictor variables are said to be collinear when they are correlated, and this collinearity complicates model estimation.
  Remember: Predictors are also called explanatory or independent variables. Ideally, they would be independent of each other.

• We don’t like adding predictors that are associated with each other to the model, because often times the addition of such variable brings nothing to the table. Instead, we prefer the simplest best model, i.e. parsimonious model.

• While it’s impossible to avoid collinearity from arising in observational data, experiments are usually designed to prevent correlation among predictors

• When any variable is added to the model \(R^2\) increases.
• But if the added variable doesn’t really provide any new information, or is completely unrelated, adjusted \(R^2\) does not increase.

\[ { R }_{ adj }^{ 2 }={ 1-\left( \frac { { SS }_{ error } }{ { SS }_{ total } } \times \frac { n-1 }{ n-p-1 } \right) } \]

where n is the number of cases and p is the number of predictors (explanatory variables) in the model.

• Because p is never negative, \({ R }_{ adj }^{ 2 }\) will always be smaller than \(R^2\).
• \({ R }_{ adj }^{ 2 }\) applies a penalty for the number of predictors included in the model.
• Therefore, we choose models with higher \({ R }_{ adj }^{ 2 }\) over others.