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📚 Principles of Data Science
Chapters ▾
1 What Are Data and Data Science?2 Collecting and Preparing Data3 Descriptive Statistics: Statistical Measurements and Probability Distributions4 Inferential Statistics and Regression Analysis5 Time Series and Forecasting6 Decision-Making Using Machine Learning Basics7 Deep Learning and AI Basics8 Ethics Throughout the Data Science Cycle9 Visualizing Data10 Reporting Results

Chapter 4: Inferential Statistics and Regression Analysis

An analyst examines statistics on a computer and paper printout.
Figure 4.1 Inferential statistics is used extensively in data science to draw conclusions about a larger population—and drive decision-making.Inferential statistics is used extensively in data science to draw conclusions about a larger population—and drive decision-making. (credit: modification of work “Compiling Health Statistics” by Ernie Branson/Wikimedia Commons, Public Domain)

Inferential statistics plays a key role in data science applications, as its techniques allow researchers to infer or generalize observations from samples to the larger population from which they were selected. If the researcher had access to a full set of population data, then these methods would not be needed. But in most real-world scenarios, population data cannot be obtained or is impractical to obtain, making inferential analysis essential. This chapter will explore the techniques of inferential statistics and their applications in data science.

Confidence intervals and hypothesis testing allow a data scientist to formulate conclusions regarding population parameters based on sample data.

One technique, correlation analysis, allows the determination of a statistical relationship between two numeric quantities, often referred to as variables. A variable is a characteristic or attribute that can be measured or observed. A correlation between two variables is said to exist where there is an association between them. Finance professionals often use correlation analysis to predict future trends and mitigate risk in a stock portfolio. For example, if two investments are strongly correlated, an investor might not want to have both investments in a certain portfolio since the two investments would tend to move in the same direction as market prices rose or fell. To diversify a portfolio, an investor might seek investments that are not strongly correlated with one another.