CptS 475/575: Data Science

Assignment 5: Regression and Classification

General instruction: The first part of this assignment will assess your understanding of linear

and logistic regression. The second part of this assignment will require you to take a set of realworld complaints received about financial products and services and classify them based on

which category they belong to. The assignment is broken into four sections with points assigned

to each.

Your solution will be submitted as a PDF/HTML file, which must include your full, functional

code and relevant results as stated in each part. You are encouraged to use R Markdown to

prepare your file.

1) (15 points) This question involves the use of multiple linear regression on the Cars data set

available on Canvas in the Datasets module. Ensure that values are represented in the

appropriate types.

a. (5 points) Perform a multiple linear regression with MPG as the response and all other

variables except Car as the predictors. Show a printout of the result (including

coefficient, error and t values for each predictor). Comment on the output:

i) Which predictors appear to have a statistically significant relationship to the response,

and how do you determine this?

ii) What does the coefficient for the Displacement variable suggest, in simple terms?

b. (5 points) Produce diagnostic plots of the linear regression fit. Comment on any problems

you see with the fit. Do the residual plots suggest any unusually large outliers? Does the

leverage plot identify any observations with unusually high leverage?

c. (5 points) Fit linear regression models with interaction effects. Do any interactions appear

to be statistically significant?

2) (30 points) This problem involves the Boston data set, which can be attached from library

MASS in R and is also made available in the Datasets module on Canvas. We will now try to

predict per capita crime rate (crim) using the other variables in this data set. In other words,

per capita crime rate is the response, and the other variables are the predictors.

a. (6 points) For each predictor, fit a simple linear regression model to predict the response.

Include the code, but not the output for all models in your solution.

b. (6 points) In which of the models is there a statistically significant association between

the predictor and the response? Considering the meaning of each variable, discuss the

relationship between crim and nox, chas, rm, dis and medv in particular. How do these

relationships differ?

c. (6 points) Fit a multiple regression model to predict the response using all the predictors.

Describe your results. For which predictors can we reject the null hypothesis H0 : βj = 0?

d. (6 points) How do your results from (a) compare to your results from (c)? Create a plot

displaying the univariate regression coefficients from (a) on the x-axis, and the multiple

regression coefficients from (c) on the y-axis. That is, each predictor is displayed as a

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single point in the plot. Its coefficient in a simple linear regression model is shown on the

x-axis, and its coefficient estimate in the multiple linear regression model is shown on the

y-axis. What does this plot tell you about the various predictors?

e. (6 points) Is there evidence of non-linear association between any of the predictors and

the response? To answer this question, for each predictor X, fit a model of the form

Y = β0 + β1X + β2X2 + β3X3+ ε

Hint: use the poly() function in R. Again, include the code, but not the output for

each model in your solution, and instead describe any non-linear trends you

uncover.

3) (15 points) Suppose we collect data for a group of students in a statistics class with variables:

X1 = hours studied,

X2 = undergrad GPA,

X3 = PSQI score (a sleep quality index), and

Y = receive an A.

We fit a logistic regression and produce estimated coefficient, β0 = −7, β1 = 0.1, β2 = 1, β3 = -.04.

a. (5 points) Estimate the probability that a student who studies for 30 h, has a PSQI score

of 11 and has an undergrad GPA of 3.0 gets an A in the class. Show your work.

b. (5 points) How many hours would the student in part (a) need to study to have a 60 %

chance of getting an A in the class? Show your work.

c. (5 points) How many hours would a student with a 3.0 GPA and a PSQI score of 5 need

to study to have a 50 % chance of getting an A in the class? Show your work.

4) (40 points) For this question, you will use a naïve Bayes model to classify consumer

complaints by the category of financial product or service the complaints are related to. You

are provided a set of consumer complaints collected by the Consumer Financial Protection

Bureau. The original dataset can be found at https://catalog.data.gov/dataset/consumercomplaint-database but you will use a subset of this dataset made available on Canvas. The

dataset is available in the Datasets module on Canvas and is named

consumer_complaints.csv. The dataset consists of complaints belonging to 9 categories.

Prepare the dataset for classification as suggested below.

a. Tokenization (20 points)

In order to use Naïve Bayes effectively, you will need to split your text into tokens. It is

common practice when doing this to reduce your words to their stems so that

conjugations produce less noise in your data. For example, the words “speak”, “spoke”,

and “speaking” are all likely to denote a similar context, and so a stemmed tokenization

will merge all of them into a single stem. R has several libraries for tokenization,

stemming and text mining. Some of you may want to use as a starting point are

tokenizers, SnowballC, tm respectively, or alternatively quanteda, which will handle the

aforementioned along with building your model in the next step. You will need to

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produce a document-term matrix from your stemmed tokenized data. This will have a

very wide feature set (to be reduced in the following step) where each word stem is a

feature, and each complaint has a list of values representing the number of occurrences of

each stem in its body. Before representing the feature set in a non-compact storage format

(such as a plain matrix), you will want to remove any word which appears in too few

documents (typically fewer than 1% of documents, but you can be more or less stringent

as you see fit). You may also use a boolean for word presence/absence if you find it more

effective. To demonstrate your completion of this part, you can simply select and print

the text of a random article along with the non-zero entries of its feature vector.

b. Classification (20 points)

For the final portion of this assignment, you will build and test a Naïve Bayes classifier

with your data. Since we have multiple classes in the given dataset, a Multinominal Naïve

Bayes model would be more appropriate. First, you will need to use feature selection to

reduce your feature set. A popular library for this is caret. It has many functionalities for

reducing feature sets, including removing highly correlated features. You may wish to try

several different methods to see which produces the best results for the following steps.

Next, you will split your data into a training set and a test set. Your training set should

comprise approximately 80% of your articles, however, you may try several sizes to find

which produces the best results. Whatever way you split your training and test sets, you

should try to ensure that your nine complaint categories are equally represented in both

sets.

Next, you will build your Naïve Bayes classifier from your training data. The naivebayes

library in CRAN is most commonly used for this. Finally, you can use your model to

predict the categories of your test data.

Once you have produced a model that generates the best predictions you can get, print a

confusion matrix of the results to demonstrate your completion of this task. For each

class, give scores for precision (TruePositives / TruePositives+FalsePositives) and recall

(TruePositives / TruePositives+FalseNegatives). To do this, you may want to use the

confusionMatrix() function.