Test your knowledge of statistical methods and experimental designs with this 20-question MCQ quiz! This Basic Design Experiment MCQS Quiz is perfect for students, researchers, and statisticians preparing for exams or job tests. This quiz covers key topics like the Neuman-Keuls Test, one-factor-at-a-time designs, repeated measures design, crossover designs, and more. Assess your understanding of Type I error risks, precision in experiments, and optimal design choices for different research scenarios. Whether you are brushing up on statistical concepts or preparing for competitive tests, this Basic Design Experiment MCQs quiz will help reinforce your expertise. Take the challenge now and see how well you score!
Online Basic Design Experiments MCQs with Answers
Online Basic Design Experiment MCQs with Answers
The Neuman-Keuls Test starts with the difference between pairs of means, starting from the difference of:
The Neuman-Keuls Test uses:
The risk of type I error may be considerably inflated using:
This test requires a greater observed difference to detect significantly different pairs of means:
Cramer and Swanson (1973) have conducted ————– studies of a number of multiple comparison methods.
One-factor-at-a-time designs can be used when factors are:
One-factor-at-a-time designs include:
In one-factor-at-a-time designs, we use:
If a large fraction of experimental units does not respond, the suitable design is:
Precision of a —————- is low if experimental units are not uniform:
The design that allocated the maximum degree of freedom to error is:
In small experiments where there is a small number of degrees of freedom, the suitable design is:
In computer-based experiments, the variation may be easily controlled through sophisticated software. Hence —————— may be successfully applied:
Appropriate use of ————— is under conditions where the experimental material is homogeneous.
In a repeated measures design, each group member in an experiment is tested for multiple conditions over time or under different conditions
A crossover design is where subjects are assigned all treatments, and the results are measured over time, is called:
Whether new drugs are effective at different cholesterol levels and at different time intervals, we use:
The repeated measures design model is similar to:
In a Repeated measures design subjects are ——————.
Discover the key differences between supervised and unsupervised learning in this quick Q&A guide. Learn about supervised and unsupervised learning functions, standard approaches, and common algorithms (like kNN vs. k-means). Also, learn about how supervised and unsupervised learning apply to classification tasks. Perfect for beginners in machine learning!”
Table of Contents
Supervised and Unsupervised Learning Questions and Answers
What is the function of Unsupervised Learning?
Unsupervised Learning is a type of machine learning where the model finds hidden patterns or structures in unlabeled data without any guidance (no predefined outputs). It’s used for clustering, dimensionality reduction, and anomaly detection. The function of unsupervised learning is:
Find clusters of the data
Find low-dimensional representations of the data
Find interesting directions in data
Interesting coordinates and correlations
Find novel observations/ database cleaning
What is the function of Supervised Learning?
Supervised Learning is a type of machine learning where the model learns from labeled data (input-output pairs) to make predictions or classifications. It’s used for tasks like regression (predicting values) and classification (categorizing data). The function of supervised learning are:
Classifications
Speech recognition
Regression
Predict time series
Annotate strings
For the following Scenario about the train dataset, which is based on classification.
You are given a train data set having 1000 columns and 1 million rows. The dataset is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that the model computation time can be reduced. Your machine has memory constraints. What would you do? (You are free to make practical assumptions.)
Processing high-dimensional data on a limited memory machine is a strenuous task; your interviewer would be fully aware of that. The following are the methods you can use to tackle such a situation:
Due to the memory constraints on the machine (CPU has lower RAM), one should close all other applications on the machine, including the web browser, so that most of the memory can be put to use.
One can randomly sample the dataset. This means one can create a smaller data set, for example, having 1000 variables and 300000 rows, and do the computations.
For dimensionality reduction (to reduce dimensionality), one can separate the numerical and categorical variables and remove the correlated variables. For numerical variables, one should use correlation. For categorical variables, one should use the chi-square test.
One can also use PCA and pick the components that can explain the maximum variance in the dataset.
Using online learning algorithms like Vowpal Wabbit (available in Python) is a possible option.
Building a linear model using Stochastic Gradient Descent is also helpful.
One can also apply the business understanding to estimate which predictors can impact the response variable. But this is an intuitive approach; failing to identify useful predictors might result in a significant loss of information.
What is the standard approach to supervised learning?
The standard approach to supervised learning involves:
Labeled Dataset: Input features paired with correct outputs.
Training: The Model learns patterns by minimizing prediction errors.
Validation: Tuning hyperparameters to avoid overfitting.
Testing: Evaluating performance on unseen data.
What are the common supervised learning algorithms?
The most common supervised learning algorithms:
Linear Regression: Predicts continuous values (e.g., house prices).
Decision Trees: Splits data into branches for classification/regression.
Random Forest: An Ensemble of decision trees for better accuracy.
Support Vector Machines (SVM): Find optimal boundary for classification.
k-Nearest Neighbors (k-NN): Classifies based on the closest data points.
Naive Bayes: Probabilistic classifier based on Bayes’ theorem.
Neural Networks: Deep learning models for complex patterns.
How is kNN different from kmeans clustering?
Firstly, do not get misled by ‘k’ in their names. One should know that the fundamental difference between both these algorithms is,
kmeans clustering is unsupervised (it is a clustering algorithm) The kmeans clustering algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close to each other. The algorithm tries to maintain enough separability between these clusters. Due to their unsupervised nature, the clusters have no labels.
kNN is supervised in nature (it is a classification (or regression) algorithm) The kNN algorithm tries to classify an unlabeled observation based on its k (can be any number ) surrounding neighbors. It is also known as a lazy learner because it involves minimal training of the model. Hence, it doesn’t use training data to generalize to unseen datasets
The quiz is about Machine Learning MCQs Questions with Answers. Test your Machine Learning knowledge with this 20-question MCQ quiz! Perfect for students, data analysts, data scientists, and statisticians, this Machine Learning MCQs Questions Quiz covers key concepts like Naive Bayes, K-means, decision trees, random forests, and ensemble learning. Sharpen your skills and assess your understanding of supervised and unsupervised learning techniques in an academic and professional context. Let us start with the Machine Learning MCQs Questions with Answers now.
Online Machine Learning MCQs Questions with Answers
Naive Bayes is a supervised classification technique that is based on Bayes’ Theorem, with an assumption of ————- among predictors.
K-means is an unsupervised partitioning algorithm used to organize ————— data into clusters.
In K-means, what term describes the point at which each cluster is defined?
Similar to a flow chart, a ——————- is a classification model that represents various solutions available to solve a given problem based on the possible outcomes of each solution.
In tree-based learning, how is a split determined?
In a decision tree, which node is the location where the first decision is made?
In a random forest, what type of data is used to train the ensemble of decision-tree-based learners?
What are some of the benefits of ensemble learning?
When using a gradient boosting machine (GBM) modeling technique, which term describes a model’s ability to predict new values that fall outside of the range of values in the training data?
The supervised learning technique boosting builds an ensemble of weak learners ————-, then aggregates their predictions.
A data analytics team uses tree-based learning for a research and development project. Currently, they are interested in the parts of the decision tree that represent an item’s target value. What are they examining?
What are some disadvantages of decision trees?
Which section of a decision tree is where the final prediction is made?
What is the only section of a decision tree that contains no predecessors?
What are some benefits of decision trees?
What is the only section of a decision tree that contains no predecessors?
When might you use a separate validation dataset?
What process uses different “folds” (portions) of the data to train and evaluate a model across several iterations?
Which of the following statements correctly describes ensemble learning?
A random forest is an ensemble of decision-tree ————— that are trained on bootstrapped data.
