Basic Statistics

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

• 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

The quiz is about MCQs Data and Variables with Answers. There are 20 multiple-choice questions covering the topics related to variables, data, types of data (such as discrete or continuous, quantitative or qualitative), and level of measurements. Let us start with the MCQs Data and Variables Statistics Quiz.

Please go to MCQs Data and Variables 22 to view the test

Online MCQs Data And Variables Quiz with Answers

• The age of the individual was recorded at the time of the survey. What type of variable would age be considered?
• The adult indicator variable is coded as a 1 if the individual is 18 or older and a 0 if not. What type of variable would the adult indicator variable be considered?
• In a survey, employees were asked to report their typical daily commute time, in minutes. What type of variable would their response be considered?
• In a survey, employees were asked to report their typical daily mode of transportation to and from work (i.e. Car, Bike, Bus, etc.). What type of variable would their response be considered?
• In a survey, the company wanted to know how employees perceived the work of upper management. Employees were asked to report the satisfaction of upper management using a 1 to 5 scale (with the following representations:

1. Extremely Unsatisfied,
2. Unsatisfied,
3. Neutral,
4. Satisfied,
5. Extremely Satisfied)

• What type of variable would their response be considered? https://gmstat.com
• In a survey, it was reported that Fridays were generally lighter regarding the number of meetings held. Employees were asked to report the number of scheduled meetings they attended the previous Friday. What type of variable would their response be considered?
• In a survey, management was playing around with the idea of having a food truck visit the office once a week and was trying to gauge how much employees would spend to help entice various food truck owners. Employees were asked to report the amount of money they believed they would spend on lunch (in $XX.XX) if a food truck came to the office once a week. What type of variable would their response be considered?
• Library cardholders were asked whether or not they had checked out a book from the library in the past month (yes or no). What type of variable would their response be considered?
• Library cardholders were asked to report the amount of late fees they have been charged in the past year (input in the form of $XX.XX). What type of variable would their response be considered?
• Library cardholders were asked to reflect on the most recent book they checked out and report the genre that it most closely represented (i.e. Science Fiction, Action, Romance, Mystery, etc.). What type of variable would their response be considered?
• The library recently added a new online checkout/renewal system. Library cardholders were asked how many times they had used the new online system. What type of variable would their response be considered?
• Library card holders were asked to report the satisfaction of their library experience during their last visit using a 1 to 5 scale (with the following representations:

1. Extremely Unsatisfied,
2. Unsatisfied,
3. Neutral,
4. Satisfied,
5. Extremely Satisfied).

• What type of variable would their response be considered?
• Focus groups, individual respondents, and panels of respondents are classified as
• Reports on quality control, production, and financial accounts issued by companies are considered as
• The type of rating scale that allows respondents to choose the most relevant option out of other stated options is classified as
• Data which is generated within the company such as routine business activities is classified as
• The scale which is used to determine ratio equality is considered as
• Measurement scale which allows researchers and statisticians to perform certain operations on data collected from respondents is classified as
• The type of questions included in the questionnaire to record responses in which respondents can answer in any way are classified as
• Measurement scale which allows ranking of numbers rather than arithmetic operations on data is classified as

MCQs General Knowledge

The post is about Classification in Data Mining. It is in the form of questions and answers for easy of understanding and learning the classification techniques and their applications in real-life.

What is Classification in Data Mining? Explain with Examples.

Classification in data mining is a supervised learning technique used to categorize data into predefined classes or labels based on input feature data. The classification technique is widely used in various applications, such as spam detection, image recognition, sentiment analysis, and medical diagnosis.

The following are some of the real life examples that make use of classification algorithms:

• A bank loan officer may need to analyze the data to know which customers are risky or which are safe.
• A marketing manager may need to analyze a customer with a given profile, who will buy a new product item.
• Banks and financial institutions use classification algorithms to identify potentially fraudulent transactions by classifying them as “Fraudulent” or “Legitimate” transactions based on transaction patterns.
• Mobile apps and digital assistants use classification algorithms to convert handwritten text into digital format by identifying and classifying individual characters or words.
• News channels and companies use classification algorithms to categorize their articles into different sections (such as Sports, Politics, Business, Technology, etc.) based on the content of the articles.
• Businesses analyze customer reviews, feedback, and social media posts to classify sentiments as “Positive,” “Negative,” or “Neutral,” helping them gauge public perception about their products or services.

What is the Goal of Classification?

Classification aims to develop a model that can accurately predict the class of unseen instances based on patterns learned from a training dataset.

Write about the Key Components of Classification.

Key components of classification in Data Mining are:

1. Training Data: A dataset where the class labels are known, which will be used to train the classification model.
2. Model: An algorithm (such as decision trees, neural networks, support vector machines, etc.) that learns to distinguish between different classes based on the training data.
3. Features: The input variables or attributes that are used to make predictions about the class labels.
4. Prediction: Once a model is trained, the model can classify new, unseen instances by assigning them to one of the predefined classes.
5. Evaluation: The performance of the classification model can be assessed using metrics like accuracy, precision, F1 score, recall, and confusion matrix.

Why Classification is Needed?

In today’s world of Big Data, a large dataset is becoming a norm. For example, image a dataset/database with many terabytes such as Facebook alone crunches 4 Petabyte of data every single day. On the other hand primary challenge of big data is how to make sense of it. Moreover, the sheer volume is not the only problem. also, big data needs to be diverse, unstructured, and fast changing.

Similalry, consider the audio and video data, social media posts, 3D data or geospatial data. These kind of data are not easy to categorize or organized.

Name Methods of Classification Methods

The following are some population methods of classification methods.

• Statistical procedure based approach
• Machine Learning based approach
• Neural network
• Classification algorithms
• ID3 algorithm
• 4.5 Algorithm
• Nearest neighbour algorithm
• Naive bayes algorithm
• SVM algorithm
• ANN algorithm
• Deision Trees
• Support vector machine
• Sense Clusters (an adaption of the K-means clustering algorithm)

Explain ID3 Algorithm

The ID3 (Iterative Dichotomiser 3) algorithm is a decision tree learning algorithm, primarily used for classification tasks in data mining and machine learning.

What are the Key Features of ID3 Classification?

• Categorical Attributes: ID3 algorithm is designed to work primarily with categorical attributes. It does not handle continuous attributes directly, but they can be converted into categorical ones through binning.
• Information Gain: The algorithm uses information gain as a criterion to select the attribute that best separates the data into different classes. Information gain measures the reduction in entropy (uncertainty) after a dataset is split based on a specific attribute.
• Recursive Tree Building: ID3 classification algorithm builds the decision tree recursively, splitting the data into subsets based on attribute values.

MCQs Data Mining

Data Analysis in R Programming Language