A data analyst is comparing the heights of two groups of people. Group A has a standard deviation of 2 inches, and Group B has a standard deviation of 5 inches. Which group has greater variability in height?

Both groups have the same variability in height.

Cannot be determined from the information given.

What does the interquartile range (IQR) measure?

The spread of the middle 50% of values in a dataset.

The average distance between all pairs of values in a dataset.

The spread of the entire dataset.

The difference between the largest and smallest values in a dataset.

Which of the following is true about the expected value of a probability distribution?

The expected value is a measure of the central tendency of the distribution.

The expected value is the same as the most common value in the distribution.

The expected value is always one of the possible outcomes in the distribution.

The expected value is not affected by the probabilities assigned to each outcome.

What is the formula for calculating the probability of an event?

Probability = (number of favorable outcomes) / (total number of outcomes)

Probability = (total number of outcomes) / (number of favorable outcomes)

Probability = (number of favorable outcomes) + (total number of outcomes)

Probability = (number of favorable outcomes) * (total number of outcomes)

What is the standard normal distribution?

A distribution with a mean of zero and a standard deviation of one.

A distribution with a mean of one and a standard deviation of zero.

A distribution with a mean of one and a standard deviation of one.

A distribution with a mean of zero and a standard deviation of zero.

What is the empirical rule for the standard normal distribution?

About 68% of the data falls within one standard deviation of the mean, about 95% of the data falls within two standard deviations of the mean, and about 99.7% of the data falls within three standard deviations of the mean.

About 50% of the data falls within one standard deviation of the mean, about 75% of the data falls within two standard deviations of the mean, and about 90% of the data falls within three standard deviations of the mean.

About 90% of the data falls within one standard deviation of the mean, about 95% of the data falls within two standard deviations of the mean, and about 99% of the data falls within three standard deviations of the mean.

About 95% of the data falls within one standard deviation of the mean, about 99% of the data falls within two standard deviations of the mean, and about 99.7% of the data falls within three standard deviations of the mean.

In machine learning, what is the main difference between supervised and unsupervised learning?

In supervised learning, the input data is labeled, while in unsupervised learning, it is not.

In unsupervised learning, the input data is labeled, while in supervised learning, it is not.

Supervised learning is faster than unsupervised learning.

Which of the following is an example of a deep learning architecture used in image recognition?

Convolutional Neural Networks (CNN)

What is the purpose of data preprocessing in the machine learning process?

To convert raw data into a format that can be used by machine learning algorithms.

To select the best features for the model

To train the machine learning model

Why do we train the machine learning model?

To optimize the model's parameters to minimize prediction error

To evaluate the model's performance on new data

To select the best features for the model

What is the purpose of the test dataset in the machine learning process?

To evaluate the model's performance on new data

To select the best features for the model

To optimize the model's parameters to minimize prediction error

What is the goal of a Decision Tree Classifier?

To predict the class label of a new instance based on its features

To cluster data points based on their similarity

To split data into subsets based on the values of a feature

To identify associations and patterns in the data

What is the advantage of using a Decision Tree Classifier over other classification algorithms?

It can handle both categorical and continuous data

It is computationally efficient

It can be easily visualized and interpreted

What is the role of the leaf nodes in a Decision Tree Classifier?

To assign a class label to each data point

To split the data into subsets based on the values of a feature

To determine the order in which the features are evaluated

What is a Decision Tree Classifier?

A supervised learning algorithm used for classification and regression

A type of unsupervised learning algorithm

A type of reinforcement learning algorithm

BSCS 4-2 - Elective 3 (Final Exam) - 2-16-2023

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BSCS 4-2 - Elective 3 (Final Exam) - 2-16-2023

40 questions

What is the purpose of statistical inference in the context of data science?

Which of the following is an example of a point estimate?

What is a p-value cutoff commonly used to determine statistical significance?

What is the difference between a population and a sample?

This refer as the characteristic of a sample that enables us to make an educated guess about the population parameter.

A data analyst is working with a dataset that contains the heights of a random sample of 500 adult males. What is the best way to summarize the data in this scenario?

A marketing team is analyzing the sales of a new product over the past six months. Which of the following measures of dispersion would be most appropriate to use in this scenario?

A customer service team is analyzing the number of complaints they receive per day. Which of the following measures of central tendency would be most appropriate to use in this scenario?

This involves summarizing and organizing the data so that they can be easily understood. We use this as to seek and describe data in a meaningful way such that, for example, patterns might emerge from the data and will give us conclusions beyond that data we have analyzed.

This is a value that attempts to describe a set of data by identifying the central position of the data set.

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