Learning Statistics: Concepts and Applications in R
Learning Statistics: Concepts and Applications in R
Seasons
24.
Statistics Your Way with Custom Functions
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 24 Now
23.
Prior Information and Bayesian Inference
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 23 Now
22.
Time Series Analysis
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 22 Now
21.
Spatial Statistics
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 21 Now
20.
Polynomial and Logistic Regression
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 20 Now
19.
Regression Trees and Classification Trees
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 19 Now
18.
Statistical Design of Experiments
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 18 Now
17.
Analysis of Covariance and Multiple ANOVA
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 17 Now
16.
Analysis of Variance: Comparing 3 Means
2017-08-18
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?
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 16 Now
15.
Multiple Linear Regression
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 15 Now
14.
Regression Predictions, Confidence Intervals
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 14 Now
13.
Linear Regression Models and Assumptions
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 13 Now
12.
Hypothesis Testing: 2 Samples, Paired Test
1970-01-01
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 12 Now
11.
Hypothesis Testing: 1 Sample
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 11 Now
10.
Interval Estimates and Confidence Intervals
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 10 Now
9.
Point Estimates and Standard Error
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 9 Now
8.
Sample Size and Sampling Distributions
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 8 Now
7.
Validating Statistical Assumptions
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 7 Now
6.
Covariance and Correlation
2017-08-18
When are two variables correlated? Learn how to measure covariance, which is the association between two random variables.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 6 Now
5.
Continuous and Normal Distributions
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 5 Now
4.
Discrete Distributions
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 4 Now
3.
Sampling and Probability
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 3 Now
2.
Exploratory Data Visualization in R
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 2 Now
1.
How to Summarize Data with Statistics
2017-08-18
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.
Watch Learning Statistics: Concepts and Applications in R Season 1 Episode 1 Now
Learning Statistics: Concepts and Applications in R is a series categorized as a new series. Spanning 1 seasons with a total of 24 episodes, the show debuted on 2017. The series has earned a no reviews from both critics and viewers. The IMDb score stands at undefined.
How to Watch Learning Statistics: Concepts and Applications in R
How can I watch Learning Statistics: Concepts and Applications in R online? Learning Statistics: Concepts and Applications in R is available on The Great Courses Signature Collection with seasons and full episodes. You can also watch Learning Statistics: Concepts and Applications in R on demand at Prime, Prime Video online.
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Channel
The Great Courses Signature Collection
Cast
Talithia Williams
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