Chapter 1.3: Populations, Samples, and Traditional Statistics Flashcards
What is a population in the context of data analysis?
A population is the complete set of all elements or individuals about which conclusions are sought in a study.
Examples of populations include all graduates of a specific MBA program, all current MasterCard cardholders, and all Buick LaCrosses produced in a year.
What is a variable in relation to populations?
Variables describe characteristics of the elements within a population, and they are often the focus of study.
When do we have a population of measurements or observations?
We have a population of measurements or observations when we assign a value of a variable to each and every element in the population.
What is a census of a population?
A census of a population occurs when we examine all of the population measurements or observations.
Why might conducting a census of a population be impractical or costly?
Conducting a census of a population may be impractical or costly when the population is very large.
What is a sample in the context of data analysis?
A sample is a subset of the elements or individuals from a population that is selected for analysis when studying a population.
For instance, when a large state university has 8,742 graduating students, it might be impractical to collect data on the starting salaries of all graduates, so a sample of graduates is selected and their starting salaries are recorded
What is a sample of measurements?
A sample of measurements occurs when we measure a characteristic of the elements within a sample.
What is descriptive statistics?
Descriptive statistics is the science of describing the essential characteristics of a set of measurements, often focusing on measures like central tendency and variability.
What does descriptive statistics help us accomplish?
Descriptive statistics helps us summarize and understand data by providing insights into what is typical and how measurements vary within a dataset.
Descriptive statistics are often used when studying a small population or when conducting a census of a population.
What is statistical inference?
Statistical inference is the science of using a sample of measurements to make broader conclusions or generalizations about a larger population of measurements.
Statistical inference is used when studying large populations where it is impractical to conduct a census, requiring us to draw conclusions based on a sample.
What is traditional statistics primarily used for?
Traditional statistics is primarily used to describe populations and samples and make statistical inferences about populations using samples.
When might traditional statistics not be sufficient for analysis?
Traditional statistics may not be sufficient for analyzing big data, which refers to massive, rapidly collected data that often requires quick preliminary analysis for effective decision-making.
What are the two related extensions of traditional statistics developed to help analyze big data?
The two related extensions are business analytics and data mining.
Can you provide an example of how business analytics can be used?
Disney uses business analytics to analyze data on visitor riding patterns, helping patrons select rides or attractions by providing real-time waiting time information.
What is data mining, and why is it relevant in the context of big data?
Data mining is the process of discovering meaningful patterns or insights from large datasets.
It is relevant in the context of big data because it helps uncover valuable information hidden within massive datasets.
