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Courses>Programming Languages>Performing Experiments in Python
Data SciencePerforming Experiments in Python
Price:Paid
Length:4 Hours
Language:English
Content type:video
level:advanced
Updated:21 February 2024
Published:04 September 2022
Similar courses
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Courses>Programming Languages>Performing Experiments in Python
Performing Experiments in Python
4 Hours
5288 students
Syllabus
In this chapter, you will learn how to explore your data and ask meaningful questions. Then, you will discover how to answer these question by using your first statistical hypothesis tests: the t-test, the Chi-Square test, the Fisher exact test, and the Pearson correlation test.
In this chapter, you will learn how to examine and multiple factors at once, controlling for the effect of confounding variables and examining interactions between variables. You will learn how to use randomization and blocking to build robust tests and how to use the powerful ANOVA method.
In this chapter, you will focus on ways to avoid drawing false conclusions, whether false positives (type I errors) or false negatives (type II errors). Central to avoiding false negatives is understanding the interplay between sample size, power analysis, and effect size.
In this final chapter, you will examine the assumptions underlying statistical tests and learn about how that influences your experimental design. This will include learning whether a variable follows a normal distribution and when you should use non-parametric statistical tests like the Wilcoxon rank-sum test and the Spearman correlation test.
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Careertail
Courses>Programming Languages>Performing Experiments in Python
Data SciencePerforming Experiments in Python
Price:Paid
Length:4 Hours
Language:English
Content type:video
level:advanced
Updated:21 February 2024
Published:04 September 2022
Similar courses
Opportunities
Courses>Programming Languages>Performing Experiments in Python
Performing Experiments in Python
4 Hours
5288 students
Syllabus
In this chapter, you will learn how to explore your data and ask meaningful questions. Then, you will discover how to answer these question by using your first statistical hypothesis tests: the t-test, the Chi-Square test, the Fisher exact test, and the Pearson correlation test.
In this chapter, you will learn how to examine and multiple factors at once, controlling for the effect of confounding variables and examining interactions between variables. You will learn how to use randomization and blocking to build robust tests and how to use the powerful ANOVA method.
In this chapter, you will focus on ways to avoid drawing false conclusions, whether false positives (type I errors) or false negatives (type II errors). Central to avoiding false negatives is understanding the interplay between sample size, power analysis, and effect size.
In this final chapter, you will examine the assumptions underlying statistical tests and learn about how that influences your experimental design. This will include learning whether a variable follows a normal distribution and when you should use non-parametric statistical tests like the Wilcoxon rank-sum test and the Spearman correlation test.
Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot