Summarizing Data and Deducing Probabilities Flashcards Preview

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Flashcards in Summarizing Data and Deducing Probabilities Deck (10)
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1

Which of the following is a univariate descriptive statistic that measures dispersion?

Covariance matrix

Inter-quartile range

Correlation

Covariance

Inter-quartile range

2

Which of the following is a multivariate descriptive statistic?

Inter-quartile range

Covariance matrix

Correlation

Covariance

Covariance matrix

3

Which of these is the best description of Azure notebooks?

Data science virtual machine running on Azure in notebook format

Azure’s special serverless notebook IDE for Python

Hosted Jupyter notebooks on an Azure VM

Hosted Jupyter notebooks on an Azure VM

4

Which of the following are Python libraries that can help you work with data?

A. NumPy
B. Pandas
C. Statsmodel
D. Scipy

B and C only

A, B, C and D

A, B, and C only

A and B only

A, B, C and D

5

Which probabilities do you need to know to apply Bayes’ rule?

A. A priori probabilities

B. Independent probabilities

C. Conditional probabilities

D. Dissimilar probabilities

C and D only

A and C only

A and B only

A and C only

6

Why is the classification algorithm that uses Bayes rule called Naive?

It is overly simplistic in its predictions

It makes strong assumptions about the independence of features

It takes interactions between variables into account

It makes strong assumptions about the independence of features

7

Which of the following charts is most appropriate for visualizing univariate distribution of data?

Strip or Swarm plot

Histogram

Pie chart

Line chart

Histogram

8

Which of the following charts is most appropriate for finding a smooth probability distribution from a dataset?

Line chart

KDE plot

Histogram

Gantt chart

KDE plot

9

What is the standard deviation of a series of constant data?

1

0

-1

Infinite

0

10

How are variance and standard deviation linked to each other?

Given N data points, variance is computed using N as the denominator, while standard deviation is computed using N – 1 as the denominator

Variance is the square root of standard deviation

Given N data points, variance is computed using (N – 1) as the denominator, while standard deviation is computed using N as the denominator

Variance is the square of standard deviation

Variance is the square of standard deviation