Data Analysis Quiz

Data Analysis Quiz

University

10 Qs

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Data Analysis Quiz

Data Analysis Quiz

Assessment

Quiz

Computers

University

Practice Problem

Hard

Created by

JBREC CSE

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which is the correct statement?

A scatter plot displays 2-D data points using cartesian coordinates

A scatter plot matrix can handle higher dimensionality effectively

A scatter plot matrix become less effective as the dimensionality increases

A third dimension can be added using different colors or shapes to represent same data points

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

__________ is the process of extracting smaller segments out of one image with the intent of identifying different parts/objects within an image.

Grouping

Regression

Prediction

Segmentation

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

The process of removing sub nodes of a decision node is called as __________.

Splitting

Merging

Pruning

Classification

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

____________________ component describes random, irregular influences.

Seasonal

Trend

Noise

None

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A ______________ is a table with two rows and two columns that reports the number of false positives, false negatives, true positives, and true negatives.

Table of Confusion

Table Matrix

Confusion Matrix

Negative Matrix

6.

OPEN ENDED QUESTION

3 mins • 1 pt

____________ occurs when a model begins to memorize training data rather than learning to generalize from trend.

Evaluate responses using AI:

OFF

Answer explanation

Overfitting occurs when a model begins to memorize training data rather than learning to generalize from trends. This leads to poor performance on unseen data, as the model fails to adapt to new patterns.

7.

OPEN ENDED QUESTION

3 mins • 1 pt

STL in time series method stands for _____________________

Evaluate responses using AI:

OFF

Answer explanation

STL in time series stands for Seasonal-Trend decomposition using Loess. It is a method used to decompose a time series into seasonal, trend, and residual components, helping to analyze and forecast data effectively.

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