QCM : Fundamentals of Data Analytics — 12 questions

Questions et réponses du QCM

1. What is predictive analytics?

The process of analyzing past data to summarize what has happened
Collecting and storing data systematically to answer specific questions
Using data assets to guide or influence future outcomes through data-driven solutions
Building models to analyze past data in order to predict or classify future events

Building models to analyze past data in order to predict or classify future events

Explication

Predictive analytics involves building models that analyze past data to forecast or classify future events, enabling organizations to anticipate trends and behaviors.

2. What is the primary purpose of the data collection and storage process in data analytics?

To share data openly with all stakeholders
To organize and store data systematically to facilitate analysis and decision-making
To collect as much data as possible without regard to organization
To delete irrelevant data to reduce storage costs

To organize and store data systematically to facilitate analysis and decision-making

Explication

The primary purpose of data collection and storage is to organize and store data systematically, which enables effective analysis and informed decision-making. This process ensures data integrity, accessibility, and readiness for analysis, leading to reliable insights and recommendations.

3. What is the primary role of descriptive statistics?

To collect and organize data
To guide decision-making processes
To predict future events based on data
To summarize and understand past data

To summarize and understand past data

Explication

Descriptive statistics are used to summarize and understand past data by calculating measures like mean, median, mode, and dispersion. They provide insights into what has already occurred, which is essential for analysis and interpretation.

4. When was predictive analytics most widely established as a distinct field within data analysis?

In the early 1900s during the advent of classical statistics
In the 1950s with the development of early computer programming
In the 1980s with the rise of data mining techniques
In the 2000s with the growth of big data and machine learning

In the 2000s with the growth of big data and machine learning

Explication

Predictive analytics became most widely established as a distinct field in the 2000s, coinciding with the growth of big data, advanced computing power, and machine learning techniques, which enabled more sophisticated modeling and forecasting methods. Earlier periods saw foundational work in statistics and data analysis, but the formal recognition and widespread application of predictive analytics as a separate discipline emerged in the 2000s.

5. How does prescriptive analytics differ from predictive analytics?

Prescriptive analytics forecasts future events, while predictive analytics recommends actions to influence outcomes.
Prescriptive analytics and predictive analytics are identical in function, both predicting future outcomes.
Prescriptive analytics only summarizes past data, whereas predictive analytics makes future predictions.
Prescriptive analytics recommends actions to influence outcomes, while predictive analytics forecasts future events.

Prescriptive analytics recommends actions to influence outcomes, while predictive analytics forecasts future events.

Explication

Prescriptive analytics differs from predictive analytics in that it not only forecasts future outcomes but also recommends actions to influence or control those outcomes. Predictive analytics focuses solely on forecasting based on historical data.

6. Who is credited with the development of variance and standard deviation as measures of dispersion?

Ronald A. Fisher
Jerzy Neyman
William Sealy Gosset
Karl Pearson

Ronald A. Fisher

Explication

Ronald A. Fisher is credited with the development of key statistical concepts such as variance and standard deviation, which are measures of dispersion. The other options are prominent statisticians but are associated with different contributions in the field.

7. What is a potential consequence of high variability in data as measured by spread?

It reduces the need for sampling
It leads to wider confidence intervals
It increases the accuracy of predictions
It decreases the uncertainty in estimates

It leads to wider confidence intervals

Explication

High variability, indicated by measures like variance and standard deviation, results in wider confidence intervals and less precise estimates, which is a key consequence affecting the reliability of data analysis and decision-making.

8. A researcher wants to study the average income of different regions within a country. To ensure that all regions are proportionally represented in the sample, which sampling method should they use?

Simple random sampling
Stratified sampling
Cluster sampling
Systematic sampling

Stratified sampling

Explication

Stratified sampling involves dividing the population into distinct groups or strata based on specific characteristics (such as regions) and then randomly sampling from each group. This ensures proportional representation of all regions, making it the most suitable method for the scenario described.

9. What is the key feature that differentiates nominal data from ordinal data?

Nominal data are categorical without any order, whereas ordinal data are categorical with a specific order.
Nominal data have an inherent order, while ordinal data do not.
Nominal data are numerical, while ordinal data are qualitative.
Nominal data can take any value within a range, while ordinal data are only whole numbers.

Nominal data are categorical without any order, whereas ordinal data are categorical with a specific order.

Explication

Nominal data are categories without any intrinsic order, such as eye color or gender, while ordinal data are categories with a meaningful order, such as rankings or education levels. The key feature distinguishing them is the presence or absence of an inherent order.

10. What does a data measurement scale represent in data analysis?

A method for collecting data from samples
A technique for testing hypotheses
A way to visualize data distributions
A system for categorizing and quantifying data

A system for categorizing and quantifying data

Explication

A data measurement scale is a system used to categorize or quantify data, such as interval or ratio scales, which helps in analyzing and interpreting data appropriately.

11. Who is associated with the development of variance and standard deviation as measures of dispersion?

Francis Galton
Jerzy Neyman
Karl Pearson
Ronald A. Fisher

Ronald A. Fisher

Explication

Ronald A. Fisher is credited with foundational work in statistics, including the development of variance and standard deviation as measures of dispersion. The other options are notable statisticians but are not specifically linked to these measures in the context provided.

12. What is the primary role of outliers and influence in data analysis regarding measures of central tendency?

Outliers have little to no effect on any measure of central tendency.
Outliers are always errors and should be removed from the data.
Outliers tend to influence the mean more than the median or mode.
Outliers significantly skew the median, making it unreliable.

Outliers tend to influence the mean more than the median or mode.

Explication

Outliers tend to influence the mean more than the median or mode because they can significantly skew the average, pulling it toward the outlier. The median and mode are less affected, making the mean more sensitive to extreme values. Therefore, the primary role of outliers and influence is their differential impact on measures of central tendency, especially the mean.

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Types of Analytics — roles?

Descriptive, predictive, prescriptive; analyze past, forecast, guide actions.

Data collection — purpose?

Gather and organize data for analysis and insights.

Population parameters — what?

True values describing entire population.

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