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Descriptive Analytics Quiz

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Questions and Answers

What is the purpose of descriptive analytics?

To apply advanced statistical techniques to data sets

To analyze distributions and sampling methods

To build predictive models designed to identify and predict future trends

To identify possible trends in large data sets or databases (correct)

Which methodology is NOT typically associated with descriptive analytics?

Charts

Measures of central tendency (mean, median, mode)

Distributions (correct)

Probability

What distinguishes predictive analytics from descriptive analytics?

It identifies trends and relationships not readily observed in a descriptive analysis (correct)

It aims to get a rough picture of what the data looks like

It focuses on building descriptive statistics

It uses simple statistical techniques

Which measure is a part of descriptive analytics?

Standard deviation Signup and view all the answers

What is the purpose of predictive analytics?

To build predictive models designed to identify and predict future trends Signup and view all the answers

Match the following with their respective analytics type:

Measures of central tendency (mean, median, mode) = Descriptive Analytics Distributions and sampling methods = Predictive Analytics Charts, graphs, sorting methods = Descriptive Analytics Building predictive models to identify future trends = Predictive Analytics Signup and view all the answers

Study Notes

Descriptive Analytics

Purpose: to summarize and describe historical data to understand past business performance
Methodology: NOT typically associated with Machine Learning
Measured by: mean, median, mode, frequency, and percentiles

Predictive Analytics

Purpose: to forecast what may happen in the future based on patterns in historical data
Distinguishing feature: uses statistical models and machine learning to forecast, whereas descriptive analytics focuses on summarizing past data

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Description

Test your knowledge of Descriptive Analytics with this quiz! Explore the definition, purpose, and example methodologies of this fundamental statistical technique for analyzing large data sets and databases.