a) Document type b) Columnar c) key-value pair d) Graph
a) Planning with data b) Acting on the plan c) Doing the analysis d) Checking the results
a) na() b) na.omit() c) na.omit d) na.Omit
a) Vector b) Matrix c) List d) Data frame
a) Arguments can be optional, in which case you do not have to specify a value for them. b) Arguments are always named when you define the function. c) The name of the argument must be specified during the function call. d) Arguments can have a default value, which is used if you did not specify a value for that argumentyourself. / |

**Question 6:- **A military campaign, four girls are selected for a special mission. The heights of these girls are 152 cms, 155 cms, 157 cms, and 163 cms. Which of the followings commands will give the correct variance of the heights of these girls?

var(height, na.rm = False) variance(height, na.rm = TRUE)

variance(height, unmow)

var(height, mow)

**Question 7:- **Consider the following command: > sd(unmow, na.rm = TRUE) Which of the followings statements is correct for this command?

Calculates the standard deviation but removes NA items with an additional instruction

Calculates the standard deviation of the complete unmow sample Calculates the standard deviation but not the remove NA items.

**Question 8:- **A company stores the details of all the products it manufactures in a data sample named product.

Which of the following commands should you use to see the total number of items in this data sample?

>count(product)

>length(Product)

>num(product)

>number(product)

**Question 9:- **A gym instructor calculates the physical stamina of 10 men on the basis of the push-ups they can complete in a minute. The score obtained is as follows: 4, 6, 7, 8, 9, 10, 12, 14, 15, 18 Which of the followings commands is used to find the median number of push-ups?

med (score, na.rm = FALSE) md(score, na.rm = FALSE)

median(score, na.rm = FALSE)

>score >Median

**Question 10:- **Which of the following data structures represents the storage areas for datasets?

Lists

Matrixes

Data frames

Vector

**Question 11:- **The following are the sales details of several products: P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 10 23 21 24 32 34 35 45 48 54 Which of the following R commands will you use to select the details of the 7th product?

>Sale [7]

>Sale[P7]

>Sale[P7:P1]

>Sale[1-7]

**Question 12:- **Consider the following matrix: > XY X Y A 16 18 B 24 32 C 17 16 D 15 12 E 11 13 The R command to select all the rows for which the value of the column X is greater than 12 is:

XY[XY[“X”] > 12, ]

XY[XY[, “X”] > 12, ]

XY[XY[X] > 12, ]

XY[XY[“X”,] > 12, ]

**Question 13:- **Which of the following commands is used to call and execute an R script in the console?

>source<- do(`myscript.R`) >source (‘myscript.R’) do(‘myscript.R’) do<- ‘myscript.R’

**Question 14:- **Which of the followings statements is correct in terms of R programming?

Every method is a function and every function is a method.

Every method is a function, but not every function is a method.

Every function is a method, but not every method is a function.

Methods and functions are different things.

**Question 15:- **Which of the following functions cannot be used to enter a nonparametric smoothed curve through

lowess loess

gap lm

**Question 16:- **Which of the following functions can be used to change the size of the window in R?

window() fetch()

size() reshape()

**Question 17:- **Which of the graphical techniques should be used to plot multiple comparisons in R?

Boxplots with notches

Barplots

Scatter plots

Pie Chart

**Question 18:- **The Monte Carlo simulation is used to determine the p-value for any data frame. Which of the following is the correct syntax to implement the Monte Carlo simulation in R?

> chisq.test(sample.df, simulate.p.value = TRUE, B = 500)

> chisq.test( simulate.p.value = TRUE, B = 500)

> chisq.test(sample.df, simulate.p.value = FALSE, B = 500)

**Question 19:- **Which of the following is the correct syntax to specify a formula in the formula syntax in R?

response ~ predictor response = predictor

response * predictor

r ~ p

b) Logistic regression models c) Linear regression models d) Aymptotic exponential models |

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