HW5.pdf_pdf_源码

HW5

Question 8.1

Describe a situation or problem from your job, everyday life, current events, etc., for which a linear regression

model would be appropriate. List some (up to 5) predictors that you might use.

I believe that linear regression could be used to predict home buying trends. Predictors that can be used

are: average sales in the last 3 years, the average age of the buyer, first time home owners, home size, and

by average income. We could use these predictors to project what home buying trends will be like in the

next 12 months.

Question 8.2

some overfitting. We’ll see ways of dealing with this sort of problem later in the course.

Answer:

set.seed(1)

data <- read.delim("~/Desktop/data 2.2 2/uscrime.txt", header=TRUE)

head(data)

## M So Ed Po1 Po2 LF M.F Pop NW U1 U2 Wealth Ineq Prob

## 1 15.1 1 9.1 5.8 5.6 0.510 95.0 33 30.1 0.108 4.1 3940 26.1 0.084602

## 2 14.3 0 11.3 10.3 9.5 0.583 101.2 13 10.2 0.096 3.6 5570 19.4 0.029599

## 3 14.2 1 8.9 4.5 4.4 0.533 96.9 18 21.9 0.094 3.3 3180 25.0 0.083401

## 4 13.6 0 12.1 14.9 14.1 0.577 99.4 157 8.0 0.102 3.9 6730 16.7 0.015801

## 4 29.9012 1969

## 5 21.2998 1234

## 6 20.9995 682

Fist model fits the entire data set

1

require(graphics)

model1=lm(Crime~., data=data)

model1

##

## Call:

## -6.638e+02 1.741e+01 -7.330e-01 4.204e+00 -5.827e+03 1.678e+02

## Wealth Ineq Prob Time

## 9.617e-02 7.067e+01 -4.855e+03 -3.479e+00

summary(model1)

##

## Call:

## lm(formula = Crime ~ ., data = data)

##

## Residuals:

## Min 1Q Median 3Q Max

## So -3.803e+00 1.488e+02 -0.026 0.979765

## Ed 1.883e+02 6.209e+01 3.033 0.004861 **

## Po1 1.928e+02 1.061e+02 1.817 0.078892 .

## Po2 -1.094e+02 1.175e+02 -0.931 0.358830

## LF -6.638e+02 1.470e+03 -0.452 0.654654

## M.F 1.741e+01 2.035e+01 0.855 0.398995

## Pop -7.330e-01 1.290e+00 -0.568 0.573845

## NW 4.204e+00 6.481e+00 0.649 0.521279

## U1 -5.827e+03 4.210e+03 -1.384 0.176238

## U2 1.678e+02 8.234e+01 2.038 0.050161 .

##

## Residual standard error: 209.1 on 31 degrees of freedom

## Multiple R-squared: 0.8031, Adjusted R-squared: 0.7078

## F-statistic: 8.429 on 15 and 31 DF, p-value: 3.539e-07

Next are the prediction with the values that were given to in in the homework instructions.

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