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…
Author
Released
7/17/2018- 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
Duration
Views
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Introduction
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Welcome1m 9s
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Using the exercise files1m 2s
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1. Installation and Setup
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2. Importing and Cleaning Data
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The structure of data1m 52s
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Create tidy data tables5m 20s
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Introducing pandas7m 28s
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Data cleaning12m 6s
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3. Visualizing and Describing Data
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The power of visualization7m 12s
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Describe distributions5m 3s
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Plot distributions7m 34s
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More quantitative variables7m 58s
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Plot categorical variables4m 30s
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Personal email analytics10m 10s
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4. Introduction to Statistical Inference
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Statistical inference1m 27s
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Confidence intervals9m 30s
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Bootstrapping7m 10s
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Hypothesis testing7m 34s
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5. Introduction to Statistical Modeling
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Statistical modeling1m 35s
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Fitting models to data7m 36s
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Goodness of fit6m 13s
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Cross validation6m 22s
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Logistic regression5m 30s
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Bayesian inference9m 14s
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Conclusion
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Next steps1m 55s
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Video: Confidence intervals