# Inferential Statistics

Data Science    |    Beginner
• 10 videos | 1h 1m 30s
• Includes Assessment
Rating 4.4 of 187 users (187)
In this Skillsoft Aspire course on data science, learners can explore hypothesis testing, which finds wide applications in data science. This beginner-level, 10-video course builds upon previous coursework by introducing simple inferential statistics, called the backbone of data science, because they seek to posit and prove or disprove relationships within data. You will start by learning steps in simple hypothesis testing: the null and alternative hypotheses, s-statistic, and p-value, as ach term is introduced and explained. Next, listen to an informative discussion of a specific family of hypothesis tests, the t-test. Then learn to describe their applications, and become familiar with how to use cases including linear regression. Learn about Gaussian distribution and the related concepts of correlation, which measures relationships between any two variables, and autocorrelation, a special form used in the concept of time-series analysis. In the closing exercise, review your knowledge by differentiating between the null and the alternative hypotheses in a hypothesis testing procedure, then enumerating four distinct uses for different types of t-tests.

## WHAT YOU WILL LEARN

• Draw the shape of a gaussian distribution and enumerate its defining properties
Enumerate the steps involved in hypothesis testing and define the null and alternative hypotheses
Describe the role of test statistic and p-value in accepting or rejecting a null hypothesis
Enumerate types and uses of t-tests in hypothesis testing
Outline the significance of skewness and kurtosis and define the skewness and kurtosis of a normally distributed random variable
• Calculate the autocorrelation of a time series
Define linear regression
Interpret the r-squared of a regression and identify overfitting
Differentiate between null and alternative hypotheses, enumerate four use cases for t-tests, and calculate the correlation of time series with itself

## IN THIS COURSE

• In this video, you will learn how to draw the shape of a Gaussian distribution and enumerate its defining properties.
• 3.  Inferential Statistics and Hypothesis Testing
In this video, find out how to list the steps involved in hypothesis testing and define the null and alternative hypotheses.
• 4.  Simplified Example of Hypothesis Testing
After completing this video, you will be able to describe the role of the test statistic and p-value in accepting or rejecting a null hypothesis.
• 5.  T-tests
Find out how to list types and uses of t-tests in hypothesis testing.
• 6.  Skewness and Kurtosis
In this video, you will learn how to outline the significance of skewness and kurtosis, and how to define the skewness and kurtosis of a normally distributed random variable.
• 7.  Correlation and Autocorrelation
In this video, you will calculate the autocorrelation of a time series.
• 8.  Introducing Linear Regression
To find out how to define linear regression, consult a statistics textbook or search for a definition online.
• 9.  Overfitting and Goodness-of-Fit
During this video, you will learn how to interpret the R-squared of a regression and identify when a model is overfitting.
• 10.  Exercise: Basic Inferential Statistics
Find out how to differentiate between null and alternative hypotheses, enumerate four use cases for t-tests, and calculate the correlation of time series data with itself.

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