How well do you know Exploratory Data Analysis (EDA)? This interactive Exploratory Data Analysis Quiz tests your understanding of key EDA concepts, including data distributions, outlier detection, visualization techniques (histograms, box plots, scatter plots), and statistical summaries. Whether you’re a student, data scientist, statistician, or researcher, this exploratory data analysis quiz helps sharpen your skills in uncovering insights from raw data. Let us start with the Online Exploratory Data Analysis Quiz now.
Online Exploratory Data Analysis Quiz with Answers
Online Exploratory Data Analysis Quiz with Answers
- Which of the following forms of exploratory data analysis generates short summaries about the sample and measures of the data?
- Which of the following forms of exploratory data analysis is a statistical comparison of groups of data?
- Which of the following would NOT be a good use of analytic graphing?
- Plots let you summarize the data (usually graphically) and highlight any broad features
- Which of the following do plots NOT do?
- What do you think is a disadvantage of the Base Plotting System?
- Which of the following is a principle of analytic graphics?
- What is the role of exploratory graphs in data analysis?
- What is the purpose of hierarchical clustering?
- When you’re doing hierarchical clustering, there are strict rules that you MUST follow.
- Average linkage uses the maximum distance between points of two clusters as the distance between those clusters.
- The number of clusters you derive from your data depends on the distance at which you choose to cut it.
- Once you decide basics, such as defining a distance metric and linkage method, hierarchical clustering is deterministic.
- K-means clustering requires you to specify a number of clusters before you begin.
- K-means clustering requires you to specify a number of iterations before you begin.
- Which of the following would be an example of variables correlated to one another?
- Every data set has a single fixed number of clusters.
- K-means clustering will always stop in 3 iterations
- When starting k-means with random centroids, you’ll always end up with the same final clustering.
- Which of the following cliches LEAST captures the essence of dimension reduction?
MCQs General Knowledge, R Programming Language
