Practice MCQ with Answer of Business Research Methods MCQ Set 5th. Also practice other multiple choice question answer set for Business Research Methods. Must give online exam quiz of Conflict Resolution and Management and assess your knowledge.
Business Research Methods MCQ and Quiz Page
Q161: What does the range measure in a dataset? Answer: d) The difference between the highest and lowest values
a) The average deviation of values from the mean
b) The spread of values in the dataset
c) The most frequently occurring value in the dataset
d) The difference between the highest and lowest values
Answer
Q162: Which parametric test is used to compare means between two independent groups? Answer: b) t-test
a) z-test
b) t-test
c) F-test
d) Correlation
Answer
Q163: What is the key requirement for conducting a z-test? Answer: a) The data must be normally distributed
a) The data must be normally distributed
b) The sample size should be small
c) The population standard deviation is unknown
d) The variables being compared are categorical
Answer
Q164: Which parametric test is used to compare means between two related groups or repeated measures? Answer: b) t-test
a) z-test
b) t-test
c) F-test
d) Correlation
Answer
Q165: What is the main purpose of an F-test in hypothesis testing? Answer: c) To compare variances between multiple groups
a) To compare means between two independent groups
b) To compare means between two related groups or repeated measures
c) To compare variances between multiple groups
d) To assess the strength and direction of a relationship between variables
Answer
Q166: Which parametric test is used to examine the strength and direction of a linear relationship between two continuous variables? Answer: d) Correlation
a) z-test
b) t-test
c) F-test
d) Correlation
Answer
Q167: What is the key difference between a one-sample t-test and a two-sample t-test? Answer: c) One compares the mean of a single group to a known value, while the other compares the means of two independent groups
a) One tests the difference in means, while the other tests the difference in proportions
b) One tests the difference between two independent groups, while the other tests the difference between two related groups
c) One compares the mean of a single group to a known value, while the other compares the means of two independent groups
d) One examines the relationship between two continuous variables, while the other examines the relationship between two categorical variables
Answer
Q168: Which non-parametric test is used to examine the association between two categorical variables? Answer: d) Chi-square test
a) z-test
b) t-test
c) F-test
d) Chi-square test
Answer
Q169: What is the primary advantage of using non-parametric tests over parametric tests? Answer: a) Non-parametric tests can handle smaller sample sizes
a) Non-parametric tests can handle smaller sample sizes
b) Non-parametric tests are more powerful and accurate
c) Non-parametric tests require normally distributed data
d) Non-parametric tests can be used for both continuous and categorical variables
Answer
Q170: When should a non-parametric test, like the Chi-square test, be used? Answer: d) When examining the association between two categorical variables
a) When comparing means between two independent groups
b) When examining the strength and direction of a linear relationship between continuous variables
c) When comparing means between two related groups or repeated measures
d) When examining the association between two categorical variables
Answer
Q171: Which parametric test should be used when comparing means between multiple groups (more than two)? Answer: c) F-test
a) z-test
b) t-test
c) F-test
d) Correlation
Answer
Q172: What is a major challenge in storing and processing big data? Answer: b) Slow processing speed
a) Limited storage capacity
b) Slow processing speed
c) Lack of data sources
d) Data privacy concerns
Answer
Q173: Which challenge refers to the ability to extract meaningful insights from large volumes of data? Answer: c) Data analysis
a) Data quality
b) Data integration
c) Data analysis
d) Data storage
Answer
Q174: What is the term used to describe the problem of integrating data from diverse sources and formats? Answer: b) Data integration
a) Data quality
b) Data integration
c) Data analysis
d) Data storage
Answer
Q175: Which challenge involves ensuring that the collected data is accurate, complete, and reliable? Answer: a) Data quality
a) Data quality
b) Data integration
c) Data analysis
d) Data storage
Answer
Q176: What is a significant challenge in preserving the privacy and security of big data? Answer: d) Data privacy concerns
a) Data quality
b) Data integration
c) Data analysis
d) Data privacy concerns
Answer
Q177: What is the primary goal of machine learning? Answer: b) To enable computers to learn from data and improve performance on a given task
a) To write programs to perform specific tasks
b) To enable computers to learn from data and improve performance on a given task
c) To design algorithms for efficient data storage
d) To develop powerful computer hardware
Answer
Q178: Which type of machine learning algorithm aims to classify input data into specific categories or classes? Answer: c) Classification
a) Regression
b) Clustering
c) Classification
d) Reinforcement learning
Answer
Q179: What is the key characteristic of unsupervised learning in machine learning? Answer: b) The learning algorithm discovers patterns or structures in the data without labeled examples
a) The learning algorithm is provided with labeled training data
b) The learning algorithm discovers patterns or structures in the data without labeled examples
c) The learning algorithm is provided with feedback or rewards to learn optimal actions
d) The learning algorithm predicts continuous numeric values
Answer
Q180: Which machine learning technique involves training a model using historical data to make predictions or estimates? Answer: a) Supervised learning
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
d) Clustering
Answer
Q181: What is the process of evaluating a trained machine learning model on unseen data called? Answer: b) Testing
a) Training
b) Testing
c) Validation
d) Inference
Answer
Q182: Which regression technique aims to minimize the sum of squared residuals between the observed and predicted values? Answer: a) Ordinary Least Squares (OLS) regression
a) Ordinary Least Squares (OLS) regression
b) Ridge regression
c) Lasso regression
d) K Nearest Neighbors (KNN) regression
Answer
Q183: What is the primary advantage of OLS regression? Answer: c) It provides interpretable coefficient estimates
a) It is computationally efficient for large datasets
b) It automatically handles multicollinearity in predictor variables
c) It provides interpretable coefficient estimates
d) It is robust to outliers in the data
Answer
Q184: Which regression technique introduces a penalty term to the OLS objective function to shrink the coefficient estimates? Answer: b) Ridge regression
a) Ordinary Least Squares (OLS) regression
b) Ridge regression
c) Lasso regression
d) K Nearest Neighbors (KNN) regression
Answer
Q185: What is the primary purpose of Ridge regression? Answer: c) To handle multicollinearity among predictor variables
a) To eliminate irrelevant predictor variables from the model
b) To perform variable selection by setting some coefficient estimates to zero
c) To handle multicollinearity among predictor variables
d) To capture non-linear relationships between variables
Answer
Q186: Which regression technique performs both variable selection and regularization by setting some coefficient estimates to zero? Answer: c) Lasso regression
a) Ordinary Least Squares (OLS) regression
b) Ridge regression
c) Lasso regression
d) K Nearest Neighbors (KNN) regression
Answer
Q187: What is the key difference between Ridge regression and Lasso regression? Answer: a) Ridge regression only sets some coefficient estimates to zero, while Lasso regression can set more coefficients to zero
a) Ridge regression only sets some coefficient estimates to zero, while Lasso regression can set more coefficients to zero
b) Ridge regression uses L1 regularization, while Lasso regression uses L2 regularization
c) Ridge regression is more effective for handling multicollinearity than Lasso regression
d) Ridge regression provides interpretable coefficient estimates, while Lasso regression does not
Answer
Q188: Which regression technique does not assume a linear relationship between predictor variables and the target variable? Answer: d) K Nearest Neighbors (KNN) regression
a) Ordinary Least Squares (OLS) regression
b) Ridge regression
c) Lasso regression
d) K Nearest Neighbors (KNN) regression
Answer
Q189: What is the main characteristic of K Nearest Neighbors (KNN) regression? Answer: a) It uses the average of the nearest K data points as the predicted value
a) It uses the average of the nearest K data points as the predicted value
b) It fits a line or plane to the data using OLS regression
c) It performs feature selection based on the distance metric
d) It assumes a linear relationship between predictor variables and the target variable
Answer
Q190: Which regression technique is non-parametric and does not make assumptions about the underlying data distribution? Answer: d) K Nearest Neighbors (KNN) regression
a) Ordinary Least Squares (OLS) regression
b) Ridge regression
c) Lasso regression
d) K Nearest Neighbors (KNN) regression
Answer
Q191: Which regression technique is suitable for handling a large number of predictor variables? Answer: c) Lasso regression
a) Ordinary Least Squares (OLS) regression
b) Ridge regression
c) Lasso regression
d) K Nearest Neighbors (KNN) regression
Answer
Q192: Which method is commonly used for predicting categorical outcomes and estimating probabilities? Answer: a) Logistic regression
a) Logistic regression
b) Classification tree
c) Clustering
d) Unsupervised learning
Answer
Q193: What is the primary goal of logistic regression? Answer: b) To classify data into specific categories or classes
a) To minimize the sum of squared residuals between observed and predicted values
b) To classify data into specific categories or classes
c) To identify groups or patterns in the data
d) To estimate the mean value of a continuous outcome variable
Answer
Q194: Which technique is used for segmenting data into homogeneous groups based on similarities in variables? Answer: c) Clustering
a) Logistic regression
b) Classification tree
c) Clustering
d) Unsupervised learning
Answer
Q195: What is the primary purpose of unsupervised learning? Answer: c) To discover patterns or structures in the data without labeled examples
a) To predict outcomes based on labeled training data
b) To classify data into specific categories or classes
c) To discover patterns or structures in the data without labeled examples
d) To estimate the mean value of a continuous outcome variable
Answer
Q196: Which method involves intentionally manipulating variables in a controlled experiment to generate data for analysis? Answer: c) Designed experiments
a) Logistic regression
b) Classification tree
c) Designed experiments
d) Active learning
Answer
Q197: What is the main advantage of using designed experiments to create data for analytics? Answer: d) It allows researchers to control and manipulate variables of interest
a) It allows for the study of natural phenomena without intervention
b) It provides real-time feedback for model training
c) It enables the collection of large amounts of data quickly
d) It allows researchers to control and manipulate variables of interest
Answer
Q198: Which method involves actively selecting and labeling data points to train a machine learning model? Answer: d) Active learning
a) Logistic regression
b) Classification tree
c) Designed experiments
d) Active learning
Answer
Q199: What is the primary purpose of active learning? Answer: c) To maximize the information gained from each labeled data point
a) To minimize the sum of squared residuals between observed and predicted values
b) To classify data into specific categories or classes
c) To maximize the information gained from each labeled data point
d) To estimate the mean value of a continuous outcome variable
Answer
Q200: Which method involves an agent learning to interact with an environment to maximize rewards? Answer: c) Reinforcement learning
a) Logistic regression
b) Classification tree
c) Reinforcement learning
d) Clustering
Answer
List of Business Research Methods mcq practice set
- Business Research Methods mcq practice set 1
- Business Research Methods mcq practice set 2
- Business Research Methods mcq practice set 3
- Business Research Methods mcq practice set 4
- Business Research Methods mcq practice set 5
- Business Research Methods mcq practice set 6
- Business Research Methods mcq practice set 7
- Business Research Methods mcq practice set 8
