预测建模作业代写 Predictive Modeling代写 统计学习代写

Predictive Modeling

Homework 1 Stat Learning and DEA

预测建模作业代写 1.Automobile Injury Insurance Claims. (Frees 1.5 page 17) We consider automobile injury claims data using data from the Insurance Research Council

1.Automobile Injury Insurance Claims. 预测建模作业代写

(Frees 1.5 page 17) We consider automobile injury claims data using data from the Insurance Research Council (IRC), a division of the American Institute for Chartered Property Casualty Underwriters and the Insurance Institute of America. The data, collected in 2002, contain information on demographic information about the claimant, attorney involvement, and economic loss (LOSS, in thousands), among other variables. We consider here a sample of n = 1, 340 losses from a single state. The full 2002 study contain more than 70,000 closed claims based on data from 32 insurers. The IRC conducted similar studies in 1977, 1987, 1992, and 1997.

(a) Compute descriptive statistics for the total economic loss (LOSS). What is the typical loss?

(b) Compute a histogram and (normal) qq plot for LOSS. Comment on the shape of the distribute.

(c) Partition the dataset into two subsamples, one corresponding to those claims that involved an ATTORNEY (=1) and the other to those in which an ATTORNEY was not involved (=2).

1. For each subsample, compute the typical loss. Does there appear to be a difference in the typical losses by attorney involvement?

2. To compare the distributions, compute a box plot by level of attorney involvement.

3. For each subsample, compute a histogram and qq plot. Compare the two distributions.

2.James page 56 exercise 10. 预测建模作业代写

This exercise involves the Boston housing data set.

(a) To begin, load in the Boston data set. The Boston data set is part of the MASS library in R. How many rows are in this data set? How many columns? What do the rows and columns represent?

(b) Make some pairwise scatterplots of the predictors in this data set. Describe your findings.

(c) Are any of the predictors associated with per capita crime rate? If so, explain the relationship.

(d) Do any of the suburbs of Boston appear to have particularly high crime rates? Tax rates? Pupil-teacher ratios? Comment on the range of each predictor.

(e) How many of the suburbs in this data set bound the Charles river?

(f) What is the median pupil-reacher ratio among the towns in this data set?

(g) Which suburb of Boston has lowest median value of owner-occupied homes? What are the values of the other predictors for that suburb, and how do those values compare to the overall ranges for those predictors? Comment on your findings.

(h) In this data set, how many of the suburbs average more than seven rooms per dwelling? More than eight rooms per dwelling? Comment on the suburbs that average more than either rooms per dwelling.

3. James 2.7 Page 53 预测建模作业代写

The table below provides a training data set containing data set containing six observations, three predictors, and one qualitative response variable.

Table 1: K-nearest neighbors application

Suppose we wish to use this data set to make a prediction for Y when X₁ = X₂ = X₃ = 0 using K-nearest neighbors.

(a) Compaute the Euclidean distance between each observation and the test point, X₁ = X₂ = X₃ = 0.

(b) What is our prediction with K = 1? Why?

(c) What is our prediction with K = 3? Why?

(d) If the Bayes decision boundary in this problem is highly non-linear, then would we expect the best value for K = 1 to be large or small? Why?

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