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Rated 4 out of 5 by Robert Woodman from An Outstanding Course on Statistics with R I bought "Learning Statistics: Concepts and Applications in R" with some formal training in statistics but none in the R programming language. Dr. Talithia Williams is the statistics professor I WISH I had when I took statistics in college many years ago. She is engaging, deeply knowledgeable of the subject matter, and aware of the pitfalls where people can go wrong with statistics or R. As I proceeded through the course, I rewrote all of her code examples for myself (using RStudio) and then played with them to see what changes to the code would do. This practice of rewriting the code helped me learn R, and I would recommend it to anyone trying to understand the R programming language while taking this course. This course is so good that I wanted to give it five stars, but I couldn't. My reason for not giving it five stars is that it was developed several years ago using R 3.3.3 "Another Canoe." (For those who don't know R, all R versions to date have a funny name. As of this writing, the current version of R is 4.4.2, "Pile of Leaves.") Some of Dr. Williams' coding examples use deprecated functions and, in one case, a deprecated package. The code could also have used some proofreading to make the examples consistent (for example, using <- versus = in assigning variable values) and to avoid errors. There were NOT many errors in her code examples, but the few that were there slowed me down in completing the course because I took the time to identify and understand the problem and rewrite the code to make it work. With the deprecated functions, I consulted online resources to find better ways to get the same result. I hope "The Great Courses" company will reach out to Dr. Williams and encourage her to update this course and perhaps add a second course on using R for Bayesian statistics. I would have liked 6 - 12 lessons on Bayesian statistics with R, but it could be a separate course. The above caveats do not detract from my enthusiasm for "Learning Statistics: Concepts and Applications in R." I recommend this course to anyone wanting to learn statistics and the R programming language. Dr. Williams is an outstanding instructor in statistics, and this course is worth the investment of time and money to take.

Date published: 2025-01-23

Rated 3 out of 5 by Joe0311 from Not Statistics 101 This is not a beginner’s statistics class. I would not recommend this course unless you have a good understanding of statistics. The professor, who is very knowledgeable in the subject matters, moves very quickly through much of the material. The course does expose you to R with examples used throughout the course.

Date published: 2024-03-21

Rated 3 out of 5 by dweitz from Have never seen R I am currently tutoring math at a local community college. I have an MA in Physics and many years ago taught in the math/sciences dept at a local college. One of the classes I help people in is Statistics. It is a basic course, not advanced. That said, I have never seen any mention of the language R in any of the statistics textbooks that I have used or looked at. In addition, I've never heard of R being used anywhere in an academic setting. Definitely not where I'm working now, and there is a computer science program. I am curious about this class as I am familiar with a number of the topics included. Of course, I would recommend this course just on the basis of curiosity.

Date published: 2024-02-18

Rated 5 out of 5 by garygech from Great Course! I enjoy watching Dr. Williams. She has a natural warmth in her presentation. I like her progressions of methods, and then her examples. R is not an easy application to understand. If you have any concern about how difficult R can be, try learning it from the command prompt! The statistics for me is the easy part. Her explanation of the mathematics is very engaging, and highly logical. You cannot go wrong with this course, but be advised, it is heavy lifting because it does involve actual computing.

Date published: 2023-11-19

Rated 1 out of 5 by jhncondix from Not for beginners, not for R Learners Not enough about R to really introduce you to it. At the same time not really an introduction to statistics. More like a review for someone who already knows the subject. She does not show what the math is actually doing in relation to the data, how it is organizing it in a meaningful way. She very quickly just springs complex formulas on you in a way that left me befuddled. She already thinks in terms of the mathematics and has forgotten the original thinking that the math is doing. For a beginner I would strongly recommend Wondrium's other title, "Meaning from Data: Statistics Made Clear," which starts where the learner is and moves slowly to the point where what the math is doing is comprehensible.

Date published: 2023-03-09

Rated 5 out of 5 by Latoma from A solid and well delivered course Professor Williams is doing a great job to introduce the main statistical concepts. From t-tests to Bayesian statistics, she provides a good foundation of the theory, uses a wide range of examples (to appeal to a wide range of people - female and male, older and young, nerdy and athletic) and translates it into an application in R. My last stats course was almost thirty years ago and so I enjoyed the refresher. I used different programs then and it is evident that R is the way to analyze data today.

Date published: 2021-11-05

Rated 5 out of 5 by prit from Excellent presentation of the subject I have basic background on Statistics from my undergraduate as well as graduate school curriculum and also using the corresponding tools during my professional engineering career. I find that the Course Professor has presented all the key concepts in a clear and concise manner over 24 well-created modules. As result, this course has reinforced my understanding of the subject very well and I can carry on with my work in this area more effectively.

Date published: 2021-10-10

Rated 1 out of 5 by DJ Gib from Downloading R was an impossible problem . . . I think Ms. Williams would have been an excellent teacher. However, I could not get by the first lecture because I could not download the required programming language. Too bad because it took me a long time to get to this course.

Date published: 2021-06-06

Learning Statistics: Concepts and Applications in R

Trailer

01: How to Summarize Data with Statistics

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....

30 min

02: Exploratory Data Visualization in R

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. Then, try out simple operations, learning how to import data, save ...

26 min

03: Sampling and Probability

Study sampling and probability, which are key aspects of how statistics handles the uncertainty inherent in all data. 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 applicatio...

25 min

04: Discrete Distributions

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....

30 min

05: Continuous and Normal Distributions

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....

30 min

06: Covariance and Correlation

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....

26 min

07: Validating Statistical Assumptions

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....

27 min

08: Sample Size and Sampling Distributions

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....

31 min

09: Point Estimates and Standard Error

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. In this lecture, 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...

23 min

10: Interval Estimates and Confidence Intervals

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....

29 min

11: Hypothesis Testing: 1 Sample

Having learned to estimate a given population parameter from sample data, now go the other direction, starting 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 ...

28 min

12: Hypothesis Testing: 2 Samples, Paired Test

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....

27 min

13: Linear Regression Models and Assumptions

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....

27 min

14: Regression Predictions, Confidence Intervals

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....

27 min

15: Multiple Linear Regression

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. Learn to avoid overfitting, which happens when too many explanatory va...

34 min

16: Analysis of Variance: Comparing 3 Means

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....

30 min

17: Analysis of Covariance and Multiple ANOVA

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....

32 min

18: Statistical Design of Experiments

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....

29 min

19: Regression Trees and Classification Trees

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....

28 min

20: Polynomial and Logistic Regression

What can be done with data when transformations and tree algorithms don't work? One approach is polynomial regression, 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 whic...

34 min

21: Spatial Statistics

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....

35 min

22: Time Series Analysis

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....

34 min

23: Prior Information and Bayesian Inference

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.

35 min

24: Statistics Your Way with Custom Functions

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....

34 min

Overview Course No. 1480

Statistical literacy is essential in today's world of big data, where we are constantly inundated with statistical arguments by those trying to influence us. On the flip side, we can use statistics in our own lives to analyze data such as financial returns, medical treatments, and housing prices, and discover the truth about anything that interests us. Crucial to this power is the statistical programming language R, a free, open-source computer language with millions of users worldwide. Designed for people comfortable with algebra, this course surveys the concepts and methods of college-level statistics through dozens of exercises conducted in RStudio, a free programming editor for the free programming language R.

Award-winning Professor Talithia Williams of Harvey Mudd College begins with descriptive statistics: you learn to draw conclusions from sample data using histograms, scatterplots, and other visual aids, and employ ideas such as the normal distribution, central limit theorem, and correlation. Next you explore the remarkable power of statistics to make inferences beyond the population being sampled: you study linear regression, multiple regression, ANOVA, and other classic techniques. In the final part of the course, you zero in on advanced topics such as experimental design, spatial statistics, time series analysis, and Bayesian inference. You conclude with a lecture that teaches you how to customize R for your own unique needs.

About

Talithia Williams

To truly appreciate statistical information, we have to understand the language of statistics, the assumptions of statistics, and how we reason in the face of uncertainty.

ALMA MATER

Rice University

INSTITUTION

Harvey Mudd College

Talithia Williams is an Associate Professor of Mathematics and the Associate Dean for Research and Experiential Learning at Harvey Mudd College. She earned her bachelor's degree in Mathematics from Spelman College, a master's degree in mathematics from Howard University and her Ph.D. in Statistics from Rice University. Her professional experiences include research appointments at NASA's Jet Propulsion Laboratory, NASA's Johnson Space Center, and the National Security Agency.

Renowned for her popular TED Talk, "Own Your Body's Data," Dr. Williams takes sophisticated numerical concepts and makes them understandable to a wide audience. She won the Mathematical Association of America's Henry L. Alder Award for Distinguished Teaching by a Beginning College or University Mathematics Faculty Member, which honors faculty members whose teaching is effective and extraordinary, and extends its influence beyond the classroom.

In her research, Dr. Williams develops statistical models that emphasize the spatial and temporal structure of data. She has partnered with the World Health Organization in developing a model to predict the annual number of cataract surgeries needed to eliminate blindness in Africa.

Dr. Williams is cohost of the PBS series NOVA Wonders, and she has delivered speeches around the country on the value of statistics in quantifying personal health information.