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Courses>Programming Languages>Inference for Numerical Data in R

Data ScienceInference for Numerical Data in R

Price:Paid

Length:4 Hours

Language:English

Content type:video

level:advanced

Updated:19 February 2024

Published:13 September 2022

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Opportunities

Courses>Programming Languages>Inference for Numerical Data in R

Inference for Numerical Data in RPaid

Datacamp4 Hours

10176 students

Syllabus

1. Bootstrapping for estimating a parameter

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

2. Bootstrapping for estimating a parameter

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

3. Introducing the t-distribution

In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution.

4. Inference for difference in two parameters

In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions.

5. Comparing many means

In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.

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Courses>Programming Languages>Inference for Numerical Data in R

Data ScienceInference for Numerical Data in R

Price:Paid

Length:4 Hours

Language:English

Content type:video

level:advanced

Updated:19 February 2024

Published:13 September 2022

Similar courses

Opportunities

Courses>Programming Languages>Inference for Numerical Data in R

Inference for Numerical Data in RPaid

Datacamp4 Hours

10176 students

SyllabusIn this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

1. Bootstrapping for estimating a parameter

In this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution.

2. Bootstrapping for estimating a parameter

3. Introducing the t-distribution

In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution.

4. Inference for difference in two parameters

In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions.

5. Comparing many means

In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.

Similar courses

Opportunities

Make the most out of your online education

Copyright © 2021 Careertail.

All rights reserved

All rights reserved

Quick Links

Get StartedLog InAbout UsCourses

Company

BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions

Stay up to date

Trustpilot