Data Visualization With Ggplot2 MCQ

Test your knowledge with important Data Visualization With Ggplot2 MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more Data Visualization With Ggplot2 MCQs at Bissoy

• . . . . . . . . is a JavaScript charting library and feature-rich API set that lets you build interactive Flash or HTML5 charts.
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  InstantAtlas

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  Alterian

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  ZingChart

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  paleoTS
• . . . . . . . . is simplest class of analytics.
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  Descriptive

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  Predictive

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  Prescriptive

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  Summarization
• . . . . . . . . is proprietary tool for predictive analytics.
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  R

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  SAS

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  SSAS

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  EDR
• . . . . . . . . involves predicting a response with meaningful magnitude, such as quantity sold, stock price, or return on investment.
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  Regression

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  Summarization

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  Clustering

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  Classification
• . . . . . . . . is rapidly being adopted for computing descriptive and query types of analytics on Big data.
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  EDR

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  Hadoop

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  Azure

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  InfoSight
• . . . . . . . . is an integrated hosted analytics platform for marketing insights, predictive models, and marketing optimization"
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  LityxEQ

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  WatSon

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  LityxIQ

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  InfoSight
• The IBM . . . . . . . . analytics appliances combine high-capacity storage for Big Data with a massively-parallel processing platform for high-performance computing.
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  Watson

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  Netezza

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  InfoSight

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  LityxEQ
• When hypothesis tests and confidence limits are to be used, the residuals are assumed to follow the . . . . . . . .distribution.
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  Formal

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  Mutual

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  Normal

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  Abnormal
• In order to calculate confidence intervals and hypothesis tests, it is assumed that the errors are independent and normally distributed with mean zero and . . . . . . . .
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  Mean

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  Variance

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  SD

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  KNN
• . . . . . . . . refers to a group of techniques for fitting and studying the straight-line relationship between two variables.
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  Linear regression

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  Logistic regression

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  Gradient Descent

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  Greedy algorithms
• . . . . . . . . regression method is also known as the ordinary least squares estimation.
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  Simple

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  Direct

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  Indirect

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  Mutual
• The parameter β0 is termed as intercept term and the parameter β1 is termed as slope parameter. These parameters are usually called as . . . . . . . .
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  Regressionists

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  Coefficients

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  Regressive

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  Regression coefficients
• When there are more than one independent variables in the model, then the linear model is termed as . . . . . . . .
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  Unimodal

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  Multiple model

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  Multiple Linear model

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  Multiple Logistic model
• Although it may seem overly simplistic, . . . . . . . . is extremely useful both conceptually and practically.
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  Linear regression

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  Logistic regression

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  Gradient Descent

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  Greedy algorithms
• . . . . . . . . is a simple approach to supervised learning. It assumes that the dependence of Y on X1, X2, . . . Xp is linear.
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  Linear regression

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  Logistic regression

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  Gradient Descent

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  Greedy algorithms
• Predicting y for a value of x that's outside the range of values we actually saw for x in the original data is called . . . . . . . .
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  Regression

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  Extrapolation

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  Intra polation

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  Polation
• The square of the correlation coefficient r 2 will always be positive and is called the . . . . . . . .
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  Regression

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  Coefficient of determination

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  KNN

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  Algorithm
• . . . . . . . . is an incredibly powerful tool for analyzing data.
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  Linear regression

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  Logistic regression

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  Gradient Descent

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  Greedy algorithms
• In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to . . . . . . . .
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  (X-intercept, Slope)

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  (Slope, X-Intercept)

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  (Y-Intercept, Slope)

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  (slope, Y-Intercept)
• Function used for linear regression in R is . . . . . . . .
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  lm(formula, data)

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  lr(formula, data)

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  lrm(formula, data)

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  regression.linear(formula, data)
• Which of the following metrics can be used for evaluating regression models?<br>i) R Squared<br>ii) Adjusted R Squared<br>iii) F Statistics<br>iv) RMSE / MSE / MAE
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  ii and iv

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  i and ii

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  ii, iii and iv

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  i, ii, iii and iv
• In practice, Line of best fit or regression line is found when . . . . . . . .
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  Sum of residuals (∑(Y – h(X))) is minimum

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  Sum of the absolute value of residuals (∑|Y-h(X)|) is maximum

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  Sum of the square of residuals ( ∑ (Y-h(X))2) is minimum

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  Sum of the square of residuals ( ∑ (Y-h(X))2) is maximum
• In lm(response ~ terms), terms specification of the form "first*second" is same as . . . . . . . .
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  first+second

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  first:second

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  first+second+first:second

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  first:second+second:first
• Common Metrics which are used to select linear model are . . . . . . . .
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  R-Squared   Lower the better<br>F-Statistic   Higher the better

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  R-Squared   Lower the better<br>F-Statistic   Lower the better

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  R-Squared   Higher the better<br>F-Statistic   Higher the better

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  R-Squared   Higher the better<br>F-Statistic   Lower the better
• The purpose of correct.cor is to correct . . . . . . . . in values.
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  difference

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  reliability

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  error

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  similar
• The purpose of fisher.test() is . . . . . . . . test for contingency table.
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  Chisq

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  Fisher

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  Prop

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  Stem
• . . . . . . . . function carries out a chi-square test.
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  chisq.test()

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  t.test()

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  prop.test()

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  fisher.test()
• . . . . . . . . are the most commonly documented object.
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  Interface

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  Class

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  Functions

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  Libraries
• . . . . . . . . is not useful when developing a package since you have to install the package first.
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  Function

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  Library

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  Interface

  ঘ

  Class
• .Rbuildignore prevents files in the . . . . . . . . package from appearing in the bundled package.
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  single

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  source

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  bundle

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  double