Master in Data Science (SMS, TU) Statistical Computing with R Question Paper 2080 Nepal

Explain these terms with examples for R:

a) Getting multi-way table with array b) Creating class intervals of continuous variable c) Missingness vs nothingness

Explain following concepts with focus on R software:

a) Raw data b) Data wrangling c) Tidy data

Explain the followings with examples for R:

a) Reference range based on mean b) Reference range based on median c) Outliers and extreme values

Explain the following concepts with focus on R software:

a) Test of normality b) Parametric tests c) Residual analysis

Describe decision tree classification model with focus on:

a) Bagging b) Improved bagging c) Boosting

Do the following in R Studio with R script:

a) Create a dataset with following variables: age (18-99 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 150 random cases of each variable. Your exam roll number must be used to set the random seed. b) Show a sub-divided bar diagram of body mass index variable by sex and socio-economic variables separately with interpretations. c) Show multiple bar diagram of age variable with sex and educational level variables and interpret it carefully. d) Show boxplots of age and body mass index variable separately and interpret the results carefully. e) Create histogram of age and body mass index variable separately and interpret the results carefully.

Do the following in R studio and with R script to knit HTML output:

a) Define an object "rating" with 9, 2, 5, 8, 6, 1, 3, 2, 8, 4, 6, 8, 7, 1, 2, 6, 10, 5, 6, 9, 6, 2, 4, 7 values. b) Replicate the given table obtained from SPSS software for the rating object in R.

rating

Do the following in R Studio with R script:

a) Create a dataset with following variables: age (18-99 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 250 cases of each variable. Your exam roll number must be used to set the random seed. b) Create scatterplot of age and body mass index variable and interpret it carefully. c) Which correlation coefficient must be used based on the interpretation of the scatterplot? Why? d) Compute the best correlation coefficient identified from the scatterplot and interpret it carefully. e) Test whether this correlation coefficient is statistically valid or not and justify its value.

OR

Do the following in R Studio with R script:

a) Create a dataset with following variables: age (18-99 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 250 cases of each variable. Your exam roll number must be used to set the random seed. b) Check if body mass index variable follows normal distribution using suggestive plot and confirmative tests and interpret the results carefully. c) Check if body mass index variables have equal variance for sex variable using suggestive plot and confirmatory test and interpret the results carefully. d) Which independent sample t-test must be used to compare body mass index by sex? Why? e) Perform the independent sample t-test identified above and interpret it carefully.

Do the following in R Studio using "mtcars" dataset with R script:

a) Divide the mtcars data into train and test datasets with 70:30 random splits. b) Fit a supervised logistic regression model and naïve bayes classification models on train data with transmission (am) as dependent variable and miles per gallon (mpg), displacement (disp), horse power (hp) and weight (wt) as independent variable. c) Predict the transmission (am) variable in the test data for both the models and interpret the result carefully. d) Get the confusion matrix, sensitivity, specificity of both the models using predicted transmission variable on test data and interpret them carefully. e) Which supervised classification model is the best for doing prediction? Why?

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

a) Get this data in R and compute 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. e) Compare this model with the first two components from principal component analysis model in this data.

OR

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

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 above and interpret it carefully. d) Compare the k=3 cluster variable with Species variable of iris data using confusion matrix and interpret the result carefully.