Master in Data Science (SMS, TU) Statistical Computing with R Question Paper 2079 Nepal

Describe the following concepts with focus on R software:

a) Loops b) Function c) Pipe

Explain following concepts with examples focusing on R software:

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

Explain the following concept with examples focusing on R software:

a) Measures of central tendency b) Measures of dispersion c) Measures of relative position

Explain the following concepts with examples focusing on R software:

a) Correlation b) Parametric tests c) Non-parametric tests

Compare following model with focus on R software:

a) Naïve Bayes and Support Vector Machine b) Decision Tree and Random Forest c) Feed-forward and feed-backward neural network

Do the following in R Studio with R script so that it can be knitted as PDF:

a) Prepare a column vector of miles per gallon (mpg) variable with random range between 10 to 50 of 500 values, do not forget to use your exam roll number as random seed to replicate the result a) Plot histogram of this "mpg" variable and interpret it carefully b) Refine the histogram by filling the bars with "blue" color and changing number of bins to 8 c) Add a vertical abline at the arithmetic mean of the mpg variable d) Plot Q-Q plot of mpg variable, add normal Q-Q line of red color on it and interpret it carefully e) Plot density plot of mpg variable without the border, fill it with yellow color and interpret it

OR

Use the "ggplot2" package and do as follow in R studio:

a) Define first layer of the ggplot object with diamond data, carat as x-axis and price as y-axis b) Add layer with geometric aesthetic as "point", statistics and position as "identity" c) Add layers with scale of y and x variables as continuous d) Add layer with coordinate system as Cartesian e) Add layer with appropriate title and interpret the resulting graph carefully

Do the following in R Studio with R script so that it can be knitted as PDF:

a) Prepare a data with 100 random observations and two variables: miles per gallon (mpg) with random range between 10 to 50 and transmission gears (gear) as random binary variable (3=3 gear, 4=four gear and 5=five gears), do not forget to use your class roll number as random seed to replicate the result b) Perform goodness-of-fit test on miles per gallon (mpg) variable to check if it follows normal distribution or not c) Perform goodness-of-fit test on miles per gallon (mpg) variable to check if the variances of mpg are equal or not on gears variable categories d) Perform the best 1-way analysis of variance test based on goodness-of-fit results with justification e) Can you use this test for this data? Interpret the result carefully, if applicable.

Do the followings in R Studio using R script so that it can be knitted as PDF:

b) Prepare a data with 200 random observations and four variables: miles per gallon (mpg) with random range between 10 to 50; transmission (am) as random binary variable (0=automatic, 1=Manual), weight (wt) with random range of 1 to 10 and horse power (hp) with random range of 125 and 400, do not forget to use your exam roll number as random seed to replicate the result c) Divide this data into train and test datasets with 70:30 random splits with your exam roll number as random seed for replication d) Fit a supervised linear regression model for the train data e) Explain the model fit and BLUE coefficients for the fitted model f) Predict the mpg variable in the test data, get fit indices and interpret them carefully

Do the following in R Studio with R script so that it can be knitted as PDF:

a) Prepare a data with four random variables and 300 observations: miles per gallon (mpg) with random range between 10 to 50; transmission (am) as random binary variable (0=automatic, 1=Manual), weight (wt) with random range of 1 to 10 and horse power (hp) with random range of 125 and 400, do not forget to use your exam roll number as random seed to replicate the result b) Divide this data into train and test datasets with 80:20 random splits with your exam roll number as random seed for replication c) Fit a supervised logistic regression model on train data with transmission (am) as dependent variable and miles per gallon (mpg), horse power (hp) and weight (wt) as independent variable d) Predict the transmission variable in the test data and interpret the predicted result carefully e) Get the confusion matrix, sensitivity, specificity of the predicted model and interpret them carefully

Do as follows using "mtcars" dataset in R studio with R script so that it can be knitted as PDF:

a) Check the head and the structure of the dataset b) Create a "cars scale" using the Principal Component Analysis (PCA) model based on nine numerical variables with centering and scaling of the variables c) Based on the PCA summary result, how many components must be extracted? Why? d) Get the bi-plot of the fitted model and interpret it carefully e) Improve the fitted model with VARIMAX process and interpret the results carefully

OR

Do as follows using "USArrests" dataset in R studio with R script so that it can be knitted as PDF:

a) Get dissimilarity distance as state.dissimilarity object b) Fit a classical multidimensional model using the state.dissimilarity object c) Get the summary of the model and interpret it carefully d) Get the plot of the model and interpret it carefully e) Compare this model with the first two components from principal component analysis in this data