Course: Statistical & Hypothesis Tests

$179.00
$216.59 incl. vat

duration: 11 hours |

Language: English (US) |

access duration: 90 days |

Details

Testing hypotheses is the basis of inferential statistics, which allows you to draw reliable conclusions about the population as a whole. In this course, you will learn about the distinction between descriptive and inferential statistics. You will learn about the components of a typical hypothesis test, such as the null and alternative hypothesis, the test statistic, and the p-value. In addition, you explore type-I and type-II errors, t-tests, and ANOVA. You will learn how to identify use cases for hypothesis tests and conceptually construct appropriate null hypotheses and alternative hypotheses for such tests.

First, you explore the most commonly used hypothesis tests, namely the one-sample t-test and the nonparametric Wilcoxon signed-rank test. You will learn to use these tests to evaluate both one-sample and two-sample hypotheses about the population mean or median.

Then you learn to use two-sample tests and paired t-tests in situations where two independent samples are drawn from different populations or where paired samples are available.

Finally, you will delve into analysis of variance, or ANOVA. This technique is used if more than two samples were drawn. In this course, you will examine the various use cases for Mann-Whitney U-tests, the use of the nonparametric paired Wilcoxon signed-rank test, and perform pairwise T-tests and ANOVA.

Result

After taking this course, you will have expanded your knowledge of inferential statistics. You will be able to identify which hypothesis test is appropriate for different user situations and you will be able to perform the different hypothesis tests.

Prerequisites

You have a basic understanding of statistics. You are at least familiar with descriptive statistics.

Target audience

Data analist

Content

Statistical & Hypothesis Tests

11 hours

Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing

  • Hypothesis testing is the bedrock of inferential statistics,

  • allowing us to draw inferences reliably about the population as a
  • whole. Use this course to learn more about the distinction between
  • descriptive and inferential statistics and how the latter seek to
  • generalize from the sample to the population as a whole. Examine
  • the components of a typical hypothesis test, such as the null and
  • alternative hypothesis, the test statistic, and the p-value. You'll
  • also explore type-I and type-II errors and the use cases and
  • conceptual underpinnings of t-tests and ANOVA. By the time you
  • finish this course, you will be able to identify use-cases for
  • hypothesis testing and conceptually construct the appropriate null
  • and alternative hypotheses for such tests.

Statistical & Hypothesis Tests: Using the One-sample T-test

  • One-sample T-tests are probably the single most commonly used

  • type of hypothesis test. Through this course, learn to manually
  • implement the one-sample T-test to know exactly how the p-value and
  • test statistic are calculated. You'll examine various library
  • implementations of the one-sample T-test and apply the test on data
  • drawn from several different distributions. This course will also
  • help you explore the non-parametric Wilcoxon signed-rank test,
  • which is conceptually very similar to the one-sample T-test and
  • helps estimate the median rather than the mean of that population
  • without making assumptions about the population distribution. Upon
  • completion of this course, you will be able to use the one-sample
  • T-test as well as its non-parametric equivalent to evaluate both
  • one-sided and two-sided hypotheses about the population mean or
  • median.

Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests

  • In situations where two independent samples are drawn from

  • different populations or where paired samples are available, such
  • as in a before-after scenario, two-sample and paired T-tests are
  • needed, respectively. Use this course to explore how two-sample
  • T-tests can be used to test the null hypothesis that two
  • independent samples have drawn from populations with equal means.
  • You'll examine type I and type II errors and the use of paired
  • samples T-tests. By the time you finish this course, you will be
  • able to test whether two samples - either drawn independently or
  • explicitly linked - are drawn from populations with equal
  • means.

Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis

  • Two-sample T-tests are great for comparing population means

  • given two samples. However, if the number of samples increases
  • beyond two, we need a much more versatile and powerful technique -
  • analysis of variance (ANOVA). Use this course to learn more about
  • non-parametric tests and the ANOVA analysis. In this course, you'll
  • explore the different use cases for Mann-Whitney U-tests, the use
  • of the non-parametric paired Wilcoxon signed-rank test, and perform
  • pairwise T-tests and ANOVA. You'll also get a chance to try your
  • hand at the non-parametric variant of ANOVA - Kruskal Wallis test
  • and post hoc tests, such as Tukey’s honestly significant difference
  • test (HSD). After completing this course, you will be able to
  • account for the effect of one or two independent categorical
  • variables, each having an arbitrary number of levels, on a
  • dependent variable using ANOVA.

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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