Watch Learning Statistics: Concepts and Applications in R

Name: Learning Statistics: Concepts and Applications in R
Released: 2017-08-18
Cast: Talithia Williams

2017
1 Season

Learning Statistics: Concepts and Applications in R is a fascinating and informative course offered by The Great Courses Signature Collection. Taught by the engaging and knowledgeable Talithia Williams, this course explores the statistical concepts and techniques that underlie much of modern data analysis, showing how these tools can be applied in real-world contexts using the R programming language.

Throughout the course, Dr. Williams takes learners on a journey through the basics of statistics, building their understanding of key concepts such as probability, random variables, and linear regression. She then shows how these concepts can be applied in a variety of settings, including hypothesis testing, statistical inference, and data visualization.

One of the great strengths of this course is its emphasis on practical applications. Dr. Williams provides numerous examples of how statistical methods can be used to solve real-world problems, such as predicting cancer risk or analyzing customer behavior. She also takes students through the process of collecting and analyzing their own data, equipping them with the skills and confidence needed to apply statistical techniques in their own lives and careers.

The course is structured around a series of engaging and interactive lectures, each designed to build on the knowledge gained in the previous lesson. Dr. Williams presents complex material in an easily digestible format, using clear and concise language and rich visual aids to illustrate key concepts. She also provides hands-on exercises and quizzes that allow learners to test their understanding and reinforce their learning.

One of the unique features of this course is its focus on the R programming language. R is a powerful and flexible tool for statistical analysis, but many students are intimidated by its complexity. Dr. Williams demystifies R, showing how it can be used to perform a wide range of statistical tasks, from data manipulation to visualization.

Throughout the course, Dr. Williams stresses the importance of critical thinking and an evidence-based approach. She encourages students to question assumptions and to be skeptical of claims that cannot be supported by data. She also emphasizes the importance of using statistics ethically, highlighting the potential pitfalls of misleading or biased analyses.

Overall, Learning Statistics: Concepts and Applications in R is an excellent course for anyone interested in statistics, data analysis, or data science. It provides a solid foundation in key statistical concepts, along with practical skills and tools that can be applied in a wide variety of settings. With Talithia Williams as your guide, you'll discover the beauty and power of statistics, and gain the confidence to use these tools to tackle complex problems and make informed decisions.

Learning Statistics: Concepts and Applications in R is a series that is currently running and has 1 seasons (24 episodes). The series first aired on August 18, 2017.

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Seasons

24. Statistics Your Way with Custom Functions

August 18, 2017

Close the course by learning how to write custom functions for your R programs, streamlining operations, enhancing graphics, and putting R to work in a host of other ways. Professor Williams also supplies tips on downloading and exporting data, and making use of the rich resources for R - a truly powerful tool for understanding and interpreting data in whatever way you see fit.

23. Prior Information and Bayesian Inference

August 18, 2017

Turn to an entirely different approach for doing statistical inference: Bayesian statistics, which assumes a known prior probability and updates the probability based on the accumulation of additional data. Unlike the frequentist approach, the Bayesian method does not depend on an infinite number of hypothetical repetitions. Explore the flexibility of Bayesian analysis.

22. Time Series Analysis

August 18, 2017

Time series analysis provides a way to model response data that is correlated with itself, from one point in time to the next, such as daily stock prices or weather history. After disentangling seasonal changes from longer-term patterns, consider methods that can model a dependency on time, collectively known as ARIMA (autoregressive integrated moving average) models.

21. Spatial Statistics

August 18, 2017

Spatial analysis is a set of statistical tools used to find additional order and patterns in spatial phenomena. Drawing on libraries for spatial analysis in R, use a type of graph called a semivariogram to plot the spatial autocorrelation of the measured sample points. Try your hand at data sets involving the geographic incidence of various medical conditions.

20. Polynomial and Logistic Regression

August 18, 2017

Polynomial regression is a form of regression analysis in which the relationship between the independent and dependent variables is modelled as the power of a polynomial. Step functions fit smaller, local models instead of one global model. Or, if we have binary data, there is logistic regression, in which the response variable has categorical values such as true/false or 0/1.

19. Regression Trees and Classification Trees

August 18, 2017

Delve into decision trees, which are graphs that use a branching method to determine all possible outcomes of a decision. Trees for continuous outcomes are called regression trees, while those for categorical outcomes are called classification trees. Learn how and when to use each, producing inferences that are easily understood by non-statisticians.

18. Statistical Design of Experiments

August 18, 2017

While a creative statistical analysis can sometime salvage a poorly designed experiment, gain an understanding of how experiments can be designed in from the outset to collect far more reliable statistical data. Consider the role of randomization, replication, blocking, and other criteria, along with the use of ANOVA to analyze the results. Work several examples in R.

17. Analysis of Covariance and Multiple ANOVA

August 18, 2017

You can combine features of regression and ANOVA to perform what is called analysis of covariance, or ANCOVA. And that's not all: Just as you can extend simple linear regression to multiple linear regression, you can also extend ANOVA to multiple ANOVA, known as MANOVA, or multivariate analysis of variance. Learn when to apply each of these techniques.

16. Analysis of Variance: Comparing 3 Means

August 18, 2017

Delve into ANOVA, short for analysis of variance, which is used for comparing three or more group means for statistical significance. ANOVA answers three questions: Do categories have an effect? How is the effect different across categories? Is this significant? Learn to apply the F-test and Tukey's honest significant difference (HSD) test.

15. Multiple Linear Regression

August 18, 2017

Multiple linear regression lets you deal with data that has multiple predictors. Begin with an R data set on diabetes in Pima Indian women that has an array of potential predictors. Evaluate these predictors for significance. Then turn to data where you fit a multiple regression model by adding explanatory variables one by one.

14. Regression Predictions, Confidence Intervals

August 18, 2017

What do you do if your data doesn't follow linear model assumptions? Learn how to transform the data to eliminate increasing or decreasing variance (called heteroscedasticity), thereby satisfying the assumptions of normality, independence, and linearity. One of your test cases uses the R data set for miles per gallon versus weight in 1973-74 model automobiles.

13. Linear Regression Models and Assumptions

August 18, 2017

Step into fully modeling the relationship between data with the most common technique for this purpose: linear regression. Using R and data on the growth of wheat under differing amounts of rainfall, test different models against criteria for determining their validity. Cover common pitfalls when fitting a linear model to data.

12. Hypothesis Testing: 2 Samples, Paired Test

January 1, 1970

Extend the method of hypothesis testing to see whether data from two different samples could have come from the same population - for example, chickens on different feed types or an ice skater's speed in two contrasting maneuvers. Using R, learn how to choose the right tool to differentiate between independent and dependent samples. One such tool is the matched pairs t-test.

11. Hypothesis Testing: 1 Sample

August 18, 2017

Start with a hypothesized parameter for a population and determining whether we think a given sample could have come from that population. Practice this important technique, called hypothesis testing, with a single parameter, such as whether a lifestyle change reduces cholesterol. Discover the power of the p-value in gauging the significance of your result.

10. Interval Estimates and Confidence Intervals

August 18, 2017

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.

9. Point Estimates and Standard Error

August 18, 2017

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.

8. Sample Size and Sampling Distributions

August 18, 2017

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.

7. Validating Statistical Assumptions

August 18, 2017

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.

6. Covariance and Correlation

August 18, 2017

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.

5. Continuous and Normal Distributions

August 18, 2017

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.

4. Discrete Distributions

August 18, 2017

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.

3. Sampling and Probability

August 18, 2017

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.

2. Exploratory Data Visualization in R

August 18, 2017

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.

1. How to Summarize Data with Statistics

August 18, 2017

All data has uncertainty but statistics can still be a powerful tool for reaching insights and solving problems. Describe and summarize 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. #Science & Mathematics

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Where to Watch Learning Statistics: Concepts and Applications in R

Learning Statistics: Concepts and Applications in R is available for streaming on the The Great Courses Signature Collection website, both individual episodes and full seasons. You can also watch Learning Statistics: Concepts and Applications in R on demand at Amazon and Hoopla.