Introduction to Data Science - Data Analysis Task - Statistics Assignment Help

Assignment Task

Assesment on Data Science Computer Based Assignment

Include R code, output and a logical clear explanation where necessary. 

You can write the answers in any word processing system (eg. Word or R-markdown). 

Once completed submit the pdf file online via the link in vUWS. (Include a cover sheet).

Question 1 

Consider “Boston.csv” dataset to answer the following questions. 

crim= per capita crime rate by town 

indus= proportion of non-retail business acres per town. 

rm= average number of rooms per dwelling 

dis= weighted distances to five Boston employment centres 

medv= Median value of owner-occupied homes (in $1000's) 

i. Construct the matrix plot and correlation matrix. Comment on the relationship 

among variables. 

ii. Derive a multiple linear regression model to describe “median value of owner- 

occupied homes” in terms of other numeric variables and give the resulting model. 

iii. Add the interaction term crim*indus to the model in part ii and derive the resulting model. 

iv. Add the polynomial term rm*rm of order 2 to the model in part iii and derive the 

resulting model. 

v. Test the significance of each slope parameter of the model and discuss the results. 

vi. Give the resultant best model and describe its accuracy. 

vii. List the model assumptions and test for three of these assumptions. 

Question 02

Consider “Wine_Quality.csv” dataset to answer the following questions. 

i. Divide the dataset into two parts; training set with 3000 observations and testing set with the 

rest of the observations. [Use set.seed as 10 to generate same randomness.] 

ii. Build a decision tree model for the training dataset to predict the Quality of Wine. 

iii. Use cross-validation and choose the best size for the tree in part ii. 

iv. Build the best tree model and identify the variables that contribute in creating a Quality Wine. 

v. Predict the outputs for the testing dataset using the model in part iv and calculate the Mean 

Squared Error(MSE). 

vi. Consider the wine quality as high if WineQuality > 6 and low otherwise. Create a new variable 

to categorise it as “High” or “Low” and name it “Wine_Cat”. 

Repeat the steps i to v. In step v calculate the misclassification rate instead of MSE. 

Question 03

Consider “CPU_Performance.csv” dataset to answer the following questions. 

i. Build a linear support vector classifier to classify the CPU Performance. 

ii. Select the best parameter values for the model in (i) using cross-validation. 

iii. Discuss the performance of the model in (ii) by considering misclassification matrix and 

misclassification rate. 

iv. Build a polynomial support vector machine to classify the CPU Performance. 

v. Select the best parameter values for the model in (iv) using cross-validation. 

vi. Discuss the performance of the model in (v) by considering misclassification matrix and 

misclassification rate. 

vii. Build a radial support vector machine to classify the CPU Performance. 

viii. Select the best parameter values for the model in (vii) using cross-validation. 

ix. Discuss the performance of the model in (viii) by considering misclassification matrix and 

misclassification rate. 

x. Identify the best model out of the three different models obtained in previous parts. Justify 

your answer. 

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