Explore topic-wise InterviewSolutions in .

Correct option is (c) anova()

EXPLANATION: ECDF() builds EMPIRICAL cumulative distribution function.

Right choice is (b) mosaicplot()

To EXPLAIN: histogram() is LATTICE command for PRODUCING a histogram.

The correct CHOICE is (b) search()

The explanation is: This search will allow you to search the contents of the R functions, PACKAGE vignettes, and TASK VIEWS.

The correct ANSWER is (C) paleoTS

Best EXPLANATION: This package contains parfossil parallelized functions for palaeoecological and palaeogeographical analysis.

The CORRECT answer is (a) which.min(x)

The best explanation: which() FUNCTION gives the TRUE indices of a LOGICAL OBJECT, allowing for array indices.

The correct choice is (C) Version

Explanation: The version number of your package increases with SUBSEQUENT releases of a package, but it’s more than just an INCREMENTING the counter of the way the number CHANGES with each release can convey INFORMATION.

Right answer is (b) adegenet

The explanation is: This package CONTAINS Classes and functions for genetic DATA ANALYSIS within the MULTIVARIATE framework.

Right CHOICE is (C) abind

The BEST explanation: This is a generalization of cbind and rbind.

Right ANSWER is (C) forecast

Easy EXPLANATION: Most important FEATURE is the resulting forecast PLOT.

Right choice is (a) count.pairwise

For explanation I would say: Pairwise COMPARISON generally is any PROCESS of comparing entities in PAIRS to JUDGE which of each ENTITY is preferred.

Right CHOICE is (c) Debian CONTROL format

Explanation: DESCRIPTION uses a very simple file format called DCF, the Debian control format. When you first start WRITING packages, you’ll mostly use these metadata to record what packages are needed to run your PACKAGE.

Right option is (b) satin

To ELABORATE: SAVES is USED for FASTER loading of VARIABLES.

Correct option is (B) Coefficient of DETERMINATION

The explanation is: The square of the correlation coefficient r square will always be positive and is CALLED the coefficient of determination. This also is equal to the proportion of the TOTAL variability that’s explained by a linear model.

The correct answer is (b) Direct

The best explanation: Direct regression method also KNOWN as the ORDINARY least SQUARES estimation. Assuming that a set of n paired observations are available which satisfy the linear regression MODEL.

Correct choice is (d) Classification

To ELABORATE: Classification techniques are WIDELY USED in data mining to classify data.

The correct choice is (B) 2

The EXPLANATION: In SIMPLE linear regression, there is one independent VARIABLE so 2 coefficients (Y=a+bx+error).

The correct ANSWER is (a) Path

Explanation: If you have an existing PACKAGE that doesn’t have an .RPROJ file, you can USE devtools for the use_rstudio(“path/to/package”) to ADD it.

Correct OPTION is (a) VAR()

For explanation: sd() is used to calculate STANDARD DEVIATION.

The correct OPTION is (B) prop.table()

The EXPLANATION: prop.table() COMPUTES proportions from a CONTINGENCY table.

The correct choice is (b) max(X, na.rm=TRUE)

The best EXPLANATION: all(x%in%y)RETURNS true if x is a proper subset of y.

Correct option is (b) Hadoop

Easy explanation: However, it has a reputation for not being a suitable environment for high PERFORMANCE complex iterative ALGORITHMS such as logistic regression, generalized LINEAR models, and DECISION trees.

Right ANSWER is (a) cut

Easy EXPLANATION: stem() is USED to MAKE a stemplot.

Correct answer is (c) bundle

Easy explanation: Files LISTED in the Rbuildignore were not INCLUDED in the bundle. .Rbuildignore prevents files from the SRC PACKAGE and appearing in the bundled package. It allows you to have some extra DIRECTORIES in your source package that will not be included in the package bundle.

Right choice is (a) G.S.

Easy EXPLANATION: The function returns a GriegSmith object which is a matrix with BLOCK sizes, SUM of squares for each block size as WELL as mean sums of squares. G1DBN is a package performing Dynamic Bayesian Network Inference.

Correct choice is (b) rowsum(x, group, reorder = TRUE, …)

Explanation: rowsum compute column SUMS across ROWS of a numeric matrix-like object for each LEVEL of a grouping VARIABLE.

Right answer is (d) x%in%y

To ELABORATE: y%in%x TESTS each element of y for MEMBERSHIP in x. x%in%y tests each element of x for membership in y.

Correct ANSWER is (c) new <- OLD[,-c(i,j)]

The explanation: new <- subset(old,LOGICAL) SELECT those CASES that meet the logical condition.

Right choice is (B) RM(list=ls())

To EXPLAIN: attach(mat) make the names of the variables in the matrix or DATA frame available in the WORKSPACE.

The CORRECT answer is (a) new <- OLD[-n,]

Explanation: new <- old[-n,] drops the nth row.

The CORRECT option is (C) x1 <- c(rnorm(n))

For explanation I WOULD say: rnorm generates random deviates.