R Programming Quiz: Trivia Exam!

Csv()

Read.csv()

Read_csv()

B & c

GetOption("defaultPackages")

Install.packages(packagenames)

Library(packagename)

Require(packagename)

Character

Number

Factor

List

1st-moment business decision

Data type

Storage mode

Data frame

Data frame

Factor

Integer

Categorical

Probability distribution

Normal distribution

Z value

Frequency distribution

Electronic design automation

Economic development administration

Exploratory data analysis

Electronic data access

Pie

Pieplot

Pipe

Package

1, 2

1, 3, 4

3, 4, 5

1, 2, 3

1, 2

1, 3, 4

3, 4

1, 2, 3

Glm()

Lm()

Linear()

Slm()

1TB

2TB

5TB

8TB

Lm()

Knn()

Glm()

Mean()

Cor()

Plot()

Scatter()

Pairs()

Sample variance

Population variance

Sample standard deviation

Population standard deviation

2.015

2.575

1.960

1.645

Regression

Classification

Clustering

Summarization

Convert a list into vector

Convert a vector into list

Convert a categorical data into numeric

List of the variables defined

0

1

2

3

K-means clustering

K-medians clustering

K-modes clustering

K-medoids clustering

1 and 2

1 and 3

2 and 3

1, 2 and 3

Scree plot

Knn

Skewness

Dendrogram

1, 2 and 3

1 and 3

1 and 2

All the above

Continuous variables

Measuring variables

Flowchart variables

Discrete variables

Classification and prediction

Dependency analysis

Apriori algorithms

Data description

Estimate of cluster centroids

Tree showing relations between each other

Assignment of each point to clusters

All of the Mentioned

Partitional

Hierarchical

Knn

PCA

Office of Legislative Services

Ottawa Linux Symposium

Organizational Leadership Supervision

Ordinary Least Squares

Tells about the height of the regression line

Tells about the slope of the line

Should be excluded if one variable has negative values

Is statistically significant if it is larger than 1.96

Minimizing the sum of squared errors

Making the sum of squared errors equal to one

Minimizing the absolute difference of the residuals

Forcing the smallest distance between the actual and fitted values

1% change in X is associated with a B1 % change in Y

Change in X by one unit is associated with a B1 change in Y

Change in X by one unit is associated with a 100 B1 % change in Y

1% change in X is associated with a change in Y of 0.01 B1

Y = B0 + B1X + B2Y^2 + e

Y = B0 + B1Log(X) + e

Y = B0 + B1X + B2X^2 + e

Y^2 = B0 + B1X + e

A specific value of the y-variable given a specific value of the x-variable

A specific value of the x-variable given a specific value of the y-variable

The strength of the relationship between the x and y variables

None of these

Is also equal to 1

Lie between -1 or +1

Be either of -1 to +1

Must be -1

Will have a positive slope

Will have a negative slope

Will have a positive y intercept

Could have either a positive or a negative slope

The relationship between two categorical variables

The relationship between two quantitative variables

He relationship between a quantitative input variable and a categorical output variable

The relationship between a categorical input and a quantitative output variable

It will always decrease correlation coefficient

It will always increase correlation coefficient

It might either decrease or increase a correlation coefficient, depending on its relation with other points

It will have no effect on the correlation coefficient

Regression analysis

Discriminant analysis

Analysis of variance

Cluster analysis

Euclidean distance

Ward.d2 distance

Chebychev’s distance

Manhattan distance

Clustering is used for analyzing data when the dependent variable is categorical and the independent variables are discrete

Clustering is also called classification analysis

Groups or clusters are suggested by the data

Objects in each cluster tend to be similar to each other and dissimilar to objects in the other clusters