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Hypothesis Testing in R

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

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4 Horas16 Videos53 Ejercicios

19.706 AprendicesDeclaración de cumplimiento

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Descripción del curso

Discover Hypothesis Testing in R

Hypothesis testing lets you ask questions about your datasets and answer them in a statistically rigorous way. In this course, you'll learn how and when to use common tests like t-tests, proportion tests, and chi-square tests.

You'll gain a deep understanding of how they work and the assumptions that underlie them. You'll also learn how different hypothesis tests are related using the ""There is only one test"" framework and use non-parametric tests that let you sidestep the requirements of traditional hypothesis tests.

Learn About T-Tests and Chi-Square Tests

You’ll start by learning why hypothesis testing in R is useful while examining some key concepts as you go. You’ll also learn how t-tests can help you test for differences in means between two groups and how chi-square tests can help you compare observed results with expected results.

Understand the Relationships Between R Hypothesis Tests

As you progress, you’ll discover the relationships between different tests, exploring elements of randomness, independence of observation, and sample sizes.

By the time you finish this course, you’ll have a deeper understanding of hypothesis testing in R and when it’s appropriate to use specific tests on your data.

Throughout the course, you'll explore a Stack Overflow user survey and a dataset of late shipments of medical supplies."

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1
Introduction to Hypothesis Testing
Gratuito
Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.
Reproducir Capítulo Ahora
Hypothesis tests and z-scores
50 xp
Uses of A/B testing
50 xp
Calculating the sample mean
100 xp
Calculating a z-score
100 xp
p-values
50 xp
Criminal trials and hypothesis tests
50 xp
Left tail, right tail, two tails
100 xp
Calculating p-values
100 xp
Statistical significance
50 xp
Decisions from p-values
50 xp
Calculating confidence intervals
100 xp
Type I and type II errors
100 xp
2
Two-Sample and ANOVA Tests
Learn how to test for differences in means between two groups using t-tests, and how to extend this to more than two groups using ANOVA and pairwise t-tests.
Reproducir Capítulo Ahora
Performing t-tests
50 xp
Hypothesis testing workflow
100 xp
Two sample mean test statistic
100 xp
Calculating p-values from t-statistics
50 xp
Why is t needed?
50 xp
The t-distribution
50 xp
From t to p
100 xp
Paired t-tests
50 xp
Is pairing needed?
100 xp
Visualizing the difference
100 xp
Using t.test()
100 xp
ANOVA tests
50 xp
Visualizing many categories
100 xp
Conducting an ANOVA test
100 xp
Pairwise t-tests
100 xp
3
Proportion Tests
Learn how to test for differences in proportions between two groups using proportion tests, extended it to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.
Reproducir Capítulo Ahora
One-sample proportion tests
50 xp
t for proportions?
50 xp
Test for single proportions
100 xp
Two-sample proportion tests
50 xp
Test of two proportions
100 xp
prop_test() for two samples
100 xp
Chi-square test of independence
50 xp
The chi-square distribution
50 xp
How many tails for chi-square tests?
50 xp
Performing a chi-square test
100 xp
Chi-square goodness of fit tests
50 xp
Visualizing goodness of fit
100 xp
Performing a goodness of fit test
100 xp
4
Non-Parametric Tests
Learn about the assumptions made by parametric hypothesis tests and see how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.
Reproducir Capítulo Ahora
Assumptions in hypothesis testing
50 xp
Common assumptions of hypothesis tests
50 xp
Testing sample size
100 xp
The "There is only one test" framework
50 xp
There is only one test
50 xp
Specifying and hypothesizing
100 xp
Continuing the infer pipeline
50 xp
Generating & calculating
100 xp
Observed statistic and p-value
100 xp
Non-parametric ANOVA and unpaired t-tests
50 xp
Simulation-based t-test
100 xp
Rank sum tests
100 xp
Congratulations!
50 xp

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Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

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Certificación disponible

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Estadístico con R

En otras pistas

Fundamentos de Estadística con R

Sets De Datos

Late Shipments Late shipments Bootstrap Distribution Democratic Presidential Candidates by County StackOverflow Survey

Colaboradores

Dr. Chester Ismay

Requisitos Previos

Data Evangelist at DataCamp

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