Inferential Statistics

Data Science    |    Beginner
  • 10 videos | 1h 1m 30s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 187 users 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

  • 2m 54s
  • 6m 48s
    In this video, you will learn how to draw the shape of a Gaussian distribution and enumerate its defining properties. FREE ACCESS
  • Locked
    3.  Inferential Statistics and Hypothesis Testing
    5m 44s
    In this video, find out how to list the steps involved in hypothesis testing and define the null and alternative hypotheses. FREE ACCESS
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    4.  Simplified Example of Hypothesis Testing
    4m 9s
    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. FREE ACCESS
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    5.  T-tests
    9m
    Find out how to list types and uses of t-tests in hypothesis testing. FREE ACCESS
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    6.  Skewness and Kurtosis
    4m 15s
    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. FREE ACCESS
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    7.  Correlation and Autocorrelation
    5m 17s
    In this video, you will calculate the autocorrelation of a time series. FREE ACCESS
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    8.  Introducing Linear Regression
    8m 45s
    To find out how to define linear regression, consult a statistics textbook or search for a definition online. FREE ACCESS
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    9.  Overfitting and Goodness-of-Fit
    8m 56s
    During this video, you will learn how to interpret the R-squared of a regression and identify when a model is overfitting. FREE ACCESS
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    10.  Exercise: Basic Inferential Statistics
    5m 41s
    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. FREE ACCESS

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