Learn about sampling distribution; simulating data; confidence interval; confidence level; and score at percentile.
- [Instructor] In this chapter,…you will be a journalist who knows a little…about statistics.…Imagine a very important election is taking place…in your city with an incumbent mayor, Mr. Brown,…against the local celebrity chef, Mrs. Green.…You work for the local newspaper,…and you're asked to poll your co-citizens for their vote.…To make things easy for you,…we'll assume you can reach every voter by phone…and that every poll voter replies truthfully.…
Both are not trivial assumptions in reality,…but in this case, there are no selection effects.…Laboriously, you call 1,000 voters…and ask for their voting intention.…I'm giving you a file with your findings.…I load packages as usual…and read my file.…
As we have learned in chapter three,…we may count votes using the DataFrame method value_counts.…In fact, let's give the queue of normalize…to get the fractions, the proportions…for each candidate.…The data seem to say that Brown is going to remain mayor.…However, you realize that the limited sample means that…the proportion depends on the specific people…
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
Next steps1m 55s
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