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

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

• To make predictions about future events

• To describe the distribution of a single variable

• To draw causal relationships between variables

• To make generalizations about a population based on a sample

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

• Sample mean

• Sample variance

• Confidence Interval

• Standard Deviation

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

• 0.10

• 0.01

• 0.05

• 0.001

What is the difference between a population and a sample?

• Population is a collection of individuals or objects, while a sample is a subset of the population.

• Population is a subset of a sample, while a sample is a collection of individuals or objects.

• Population is a collection of data, while a sample is a subset of the population data.

• Population is a type of distribution, while a sample is a type of statistic.

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

• Population

• Parameter

• Sample

• Statistics

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?

• By calculating the median height of the sample.

• By calculating the mean height of the sample.

• By creating a histogram of the sample heights.

• By calculating the mode height of the sample.

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?

• Mean

• Median

• Mode

• Standard deviation

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?

• Mean

• Median

• Mode

• Range

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.

• Descriptive Statistics

• Inferential Statistics

• Measures of Dispersion

• Probability

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

• Measure of dispersion

• Measure of variation

• Measure of position

• Measure of center