Master in Data Science (SMS, TU) Statistical Computing with R Question Paper 2081 Nepal

Describe data visualization with focus on:

a) Concept of grammar of graphics with Wilkinson's approach

b) Layers in grammar of graphics with ggplot package's approach

c) Statistical transformations in grammar of graphics

Describe followings for checking fit of the multiple linear regression model:

a) Outliers

b) Cook's distance

c) Leverage

Describe supervised learning classification regression model with focus on:

a) Model fit indices

b) Confusion matrix with an example

c) Prediction accuracy with ROC curve

Describe following with example on it use:

a) Poisson regression

b) Zero-inflated Poisson regression

c) Negative binomial regression

Describe supervised linear regression model with focus on:

a) Cross-validation

b) K-fold cross-validation

c) Repeated k-fold cross-validation

Do the following in R Studio using ggplot2 package with R script to knit PDF output:

a) Create a dataset with following variables: age (10-99 years), sex (male/female), educational levels (No education/Primary/Secondary/Beyond secondary), socio-economic status (Low, Middle, High) and body mass index (14 – 38) with random 200 cases of each variable. Your roll number must be used to set the random seed.

b) Create scatter plot of age and body mass index variables using ggplot2 package and interpret the result carefully.

c) Create classes of body mass index variable as: <18, 18-24, 25-30, 30+ and show it as pie chart using ggplot2 package and interpret it carefully

d) Create histogram of age variable with bin size of 15 using the ggplot2 package and interpret it carefully

Do the following in R Studio using "airquality" data set of R with R script to knit PDF output:

a) Perform goodness-of-fit test on Temp variable to check if it follows normal distribution or not

b) Perform goodness-of-fit test on Temp variable by Month variable to check if the variances of mpg are equal or not on am variable categories

c) Discuss which independent sample test must be used to compare "Temp" variable by "Month" variable categories based on the results obtained above

d) Perform the best independent sample statistical test for this data now and interpret the results carefully

Do the following in R Studio using "Arrests" dataset of car package with R script to knit PDF output:

a) Divide the Arrests data into train and test datasets with 80:20 random splits

b) Fit a supervised logistic regression and naïve Bayes classification models on train data with "released" as dependent variable and colour, age, sex, employed and citizen as independent variable

c) Predict the released variable in the test datasets of these models and interpret the result carefully

d) Compare and decide which classification model is better for this data

Do as follows using in-built "iris" dataset with R script to knit PDF output:

a) Create a "flower scale" of first four variables of iris dataset using the Principal Component Analysis

b) Compute the eigenvalues and interpret the PCA result carefully using Kaiser's criteria

c) Show the Scree plot and decide on the number of components to retain with careful interpretation

d) Revise the flower scale with 3 components using VARIMAX rotation and interpret the result carefully

OR

Do as follows using given dataset of 10 US cities in R studio with R script:

a) Get dissimilarity distance as city dissimilarity object

b) Fit a classical multidimensional model using the city dissimilarity object

c) Get the summary of the model and interpret it carefully

d) Get the bi-plot of the model and interpret it carefully

Use the first four variables of the "iris" data and do as follows with R Script to knit PDF output:

a) Fit a hierarchical clustering model using single linkage and get the dendogram for this model

b) Fit a hierarchical clustering model using complete linkage and get the dendogram for this model

c) Fit a hierarchical clustering model using average linkage and get the dendogram for this model

d) Find the best hierarchical clustering model for this data and locate the number of clusters for it

OR

Use the first four variables of "iris" data file into R Studio and do as follows with R script to knit PDF output:

a) Fit a k-means clustering model in the data with k=2 and k=3

b) Plot the clusters formed with k=3 in the single graph and interpret them carefully

c) Add cluster centers for the plot of clusters formed with k=3 and interpret it carefully

d) Compare the k=3 clusters with Species variable using confusion matrix and interpret the result carefully