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  3. Television

  4. Learning Statistics: Concepts and Applications in R

TELEVISION

Learning Statistics: Concepts and Applications in R

Series: Great Courses
4.5
(51)
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Episodes
24
Rating
TVPG
Year
2017
Language
English
Publisher
The Great Courses

About

The ability of statistics to extract insights from a random collection of facts is one of the most astonishing and useful feats of applied mathematics. This course surveys college-level statistics through dozens of exercises conducted through the statistical programming language R, a free, open-source computer language with millions of users worldwide.

Related Subjects

  • Educational
  • Science

Episodes

1. How to Summarize Data with Statistics

30m

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Confront how ALL data has uncertainty, and why statistics is a powerful tool for reaching insights and solving problems. Begin by describing and summarizing data with the help of concepts such as the mean, median, variance, and standard deviation. Learn common statistical notation and graphing techniques, and get a preview of the programming language R, which will be used throughout the course.

2. Exploratory Data Visualization in R

30m

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Dip into R, which is a popular open-source programming language for use in statistics and data science. Consider the advantages of R over spreadsheets. Walk through the installation of R, installation of a companion IDE (integrated development environment) RStudio, and how to download specialized data packages from within RStudio.

3. Sampling and Probability

30m

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Study sampling and probability. See how sampling aims for genuine randomness in the gathering of data, and probability provides the tools for calculating the likelihood of a given event based on that data. Solve a range of problems in probability, including a case of medical diagnosis that involves the application of Bayes' theorem.

4. Discrete Distributions

30m

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There's more than one way to be truly random! Delve deeper into probability by surveying several discrete probability distributions - those defined by discrete variables. Examples include Bernoulli, binomial, geometric, negative binomial, and Poisson distributions - each tailored to answer a specific question. Get your feet wet by analyzing several sets of data using these tools.

5. Continuous and Normal Distributions

30m

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Focus on the normal distribution, which is the most celebrated type of continuous probability distribution. Characterized by a bell-shaped curve that is symmetrical around the mean, the normal distribution shows up in a wide range of phenomena. Use R to find percentiles, probabilities, and other properties connected with this ubiquitous data pattern.

6. Covariance and Correlation

30m

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When are two variables correlated? Learn how to measure covariance, which is the association between two random variables. Then use covariance to obtain a dimensionless number called the correlation coefficient. Using an R data set, plot correlation values for several variables, including the physical measurements of a sample population.

7. Validating Statistical Assumptions

30m

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Graphical data analysis was once cumbersome and time-consuming, but that has changed with programming tools such as R. Analyze the classic Iris Flower Data Set - the standard for testing statistical classification techniques. See if you can detect a pattern in sepal and petal dimensions for different species of irises by using scatterplots, histograms, box plots, and other graphical tools.

8. Sample Size and Sampling Distributions

30m

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It's rarely possible to collect all the data from a population. Learn how to get a lot from a little by "bootstrapping," a technique that lets you improve an estimate by resampling the same data set over and over. It sounds like magic, but it works! Test tools such as the Q-Q plot and the Shapiro-Wilk test, and learn how to apply the central limit theorem.

9. Point Estimates and Standard Error

30m

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Take your understanding of descriptive techniques to the next level, as you begin your study of statistical inference, learning how to extract information from sample data. Focus on the point estimate - a single number that provides a sensible value for a given parameter. Consider how to obtain an unbiased estimator, and discover how to calculate the standard error for this estimate.

10. Interval Estimates and Confidence Intervals

30m

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Move beyond point estimates to consider the confidence interval, which provides a range of possible values. See how this tool gives an accurate estimate for a large population by sampling a relatively small subset of individuals. Then learn about the choice of confidence level, which is often specified as 95%. Investigate what happens when you adjust the confidence level up or down.

Extended Details

  • SeriesGreat Courses
  • Closed CaptionsEnglish

Artists

Talithia WilliamsWriter
Talithia WilliamsActor