Data Analytics | Hypothesis testing | Free Online Course | Alison

Data Analytics: Hypothesis testing

This free online explains the concepts of hypothesis testing as it is used for assumptions in populations parameters.

Data Science
Free Course
Hypothesis testing is used in data analytics to test assumptions on population parameters. It is a technique used to access the possibleness of a hypothesis by using sample data. This free online course describes how Hypothesis testing can be used to determine whether a statement about the value of a population parameter should or should not be rejected. By the end of this course, you will better informed about the null and alternate hypothesis.
  • Duration

    3-4 Hours
  • Assessment

  • Certification

  • Responsive

  • Publisher






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Data analytics and hypothesis testing is an aspect of inferential statistics that enables us to make decisions about populations based on available samples. This free online course on Alison takes you through the concept of population mean and population proportion. The course defines the steps used in computing the population value and how the critical value approach is implemented in two-tailed hypothesis testing.

The course explains the different types of errors encountered in hypothesis testing. You will learn about the several ways of obtaining the value of null hypothesis as well as the relationship between hypothesis testing and decision making. You will learn how the hypothesis tests are used for two population means. You will also get to know more about how to make inferences on the differences between two population means.

Regression analysis is a statistical technique that is very useful in engineering and science problems. This free online course explains how empirical models are used for process optimization, such as finding the level of temperature that maximizes yield and process control purposes. You will study factorial experiments and various environmental studies in businesses. Start this course on Alison today and improve your skills on data analytics.

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Learning Outcomes

Upon successful completion of this course, you will be able to: 

  • Describe population mean and population proportion 
  • Describe how to use one tailed tests on a population mean 
  • Describe the steps used in computing the population value 
  • Explain the critical value approach to two-tailed hypothesis testing 
  • Describe the ways of obtaining the value of null hypothesis 
  • Describe the one way ANOVA 
  • Explain the sample size calculation theorem 
  • Describe how to conduct hypothesis testing with categorical data 
  • Describe the production process input and output 
  • Explain the application of quality engineering techniques and the systemic reduction of process variability
  • Describe engineering experiments involving several factors using the factorial design approach 
  • Describe how ANOVA is used to analyze data from experiments 



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