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Sampling in R

Intermediate
4.8+
227 reviews
Updated 05/2025
Master sampling to get more accurate statistics with less data.
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RProbability & Statistics4 hours15 videos51 Exercises4,000 XP20,183Statement of Accomplishment

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Course Description

Sampling is a cornerstone of inference statistics and hypothesis testing. It's tremendously important in survey analysis and experimental design. This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions. Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition.

Prerequisites

Introduction to Statistics in R
1

Introduction to Sampling

Start Chapter
2

Sampling Methods

Start Chapter
3

Sampling Distributions

Start Chapter
4

Bootstrap Distributions

Start Chapter
Sampling in R
Course
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Don’t just take our word for it

*4.8
from 227 reviews
85%
14%
1%
0%
0%
  • Parag
    about 17 hours

  • Nabin
    about 19 hours

  • Rohini
    3 days

  • FATIMA
    about 12 hours

  • Florian
    2 days

  • Vania
    2 days

    I found Chapter 2 to be really difficult to understand. A couple of times the correct answer had functions not yet explained in the video or in the text next to the excersices, so I couldn't arrive to the response on my own withoth relying on AI help. The last part of Chapter 4 was also hard to understand, I really got confused with all the concepts of the sampling distributions and its relationship with bootstraping. I wish they had included more and better examples of in what situations these are useful.

Parag

Nabin

Rohini

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