WGU D491 INTRO TO ANALYTICS
(EXTRA CREDIT) EXAM QUESTIONS
AND ANSWERS
What is Naive Bayes analysis primarily used for in machine learning?
A) Dimensionality reduction B) Anomaly detection C) Classification D) Regression -
ANSWER-C) Classification
Explanation: Naive Bayes analysis is primarily used for classification tasks in
machine learning. It predicts the class label of a given instance based on its features,
using Bayes' theorem and the assumption of conditional independence between
features.
What is the primary difference between k-means clustering and hierarchical
clustering?
A) K-means clustering assigns each data point to the nearest centroid, while
hierarchical clustering creates a hierarchy of clusters. B) K-means clustering is a
supervised learning algorithm, while hierarchical clustering is unsupervised. C) K-
means clustering requires the number of clusters to be specified in advance, while
hierarchical clustering does not. D) K-means clustering is based on distance
measures, while hierarchical clustering is based on similarity measures. - ANSWER-
A) K-means clustering assigns each data point to the nearest centroid, while
hierarchical clustering creates a hierarchy of clusters.
What does a p-value represent in statistical hypothesis testing?
A) The probability of observing the null hypothesis given the data. B) The probability
of observing the alternative hypothesis given the data. C) The probability of obtaining
the observed data, or more extreme data, if the null hypothesis is true. D) The
probability of obtaining the null hypothesis, regardless of the data. - ANSWER-C)
The probability of obtaining the observed data, or more extreme data, if the null
hypothesis is true.
What is the key difference between linear regression and logistic regression? A)
Linear regression models continuous outcomes, while logistic regression models
binary outcomes. B) Linear regression uses a linear equation to fit the data, while
logistic regression uses a logistic function. C) Linear regression assumes a linear
relationship between variables, while logistic regression assumes a nonlinear
relationship. D) Linear regression estimates parameters that minimize the sum of
squared errors, while logistic regression maximizes the likelihood of observing binary
outcomes. - ANSWER-A) Linear regression models continuous outcomes, while
logistic regression models binary outcomes.
What is the primary objective of Principal Component Analysis (PCA) in data
analysis?
A) To identify clusters of similar data points in high-dimensional datasets. B) To
reduce the dimensionality of data while preserving most of the variability. C) To
estimate the parameters of a linear regression model. D) To determine the optimal
(EXTRA CREDIT) EXAM QUESTIONS
AND ANSWERS
What is Naive Bayes analysis primarily used for in machine learning?
A) Dimensionality reduction B) Anomaly detection C) Classification D) Regression -
ANSWER-C) Classification
Explanation: Naive Bayes analysis is primarily used for classification tasks in
machine learning. It predicts the class label of a given instance based on its features,
using Bayes' theorem and the assumption of conditional independence between
features.
What is the primary difference between k-means clustering and hierarchical
clustering?
A) K-means clustering assigns each data point to the nearest centroid, while
hierarchical clustering creates a hierarchy of clusters. B) K-means clustering is a
supervised learning algorithm, while hierarchical clustering is unsupervised. C) K-
means clustering requires the number of clusters to be specified in advance, while
hierarchical clustering does not. D) K-means clustering is based on distance
measures, while hierarchical clustering is based on similarity measures. - ANSWER-
A) K-means clustering assigns each data point to the nearest centroid, while
hierarchical clustering creates a hierarchy of clusters.
What does a p-value represent in statistical hypothesis testing?
A) The probability of observing the null hypothesis given the data. B) The probability
of observing the alternative hypothesis given the data. C) The probability of obtaining
the observed data, or more extreme data, if the null hypothesis is true. D) The
probability of obtaining the null hypothesis, regardless of the data. - ANSWER-C)
The probability of obtaining the observed data, or more extreme data, if the null
hypothesis is true.
What is the key difference between linear regression and logistic regression? A)
Linear regression models continuous outcomes, while logistic regression models
binary outcomes. B) Linear regression uses a linear equation to fit the data, while
logistic regression uses a logistic function. C) Linear regression assumes a linear
relationship between variables, while logistic regression assumes a nonlinear
relationship. D) Linear regression estimates parameters that minimize the sum of
squared errors, while logistic regression maximizes the likelihood of observing binary
outcomes. - ANSWER-A) Linear regression models continuous outcomes, while
logistic regression models binary outcomes.
What is the primary objective of Principal Component Analysis (PCA) in data
analysis?
A) To identify clusters of similar data points in high-dimensional datasets. B) To
reduce the dimensionality of data while preserving most of the variability. C) To
estimate the parameters of a linear regression model. D) To determine the optimal
