# Statistical Analysis and Modeling in R: Working with Probability Distributions

R Programming 4.0+    |    Intermediate
• 12 Videos | 1h 43m 24s
• Includes Assessment
Interpreting data is a core pre-processing step in data analysis and modeling. Use this course to practice using various dynamic statistical tools to explore and understand your data. During this course, you'll explore population distributions to model random variables, work with discrete and continuous probability distributions, and use discrete probability distribution types, such as the uniform, binomial, and Poisson distributions. You'll also examine continuous distributions, such as the normal and the exponential distributions. You'll round the course off by learning how to read and interpret QQ plots, which can be used to compare the distributions of two samples of data. When you're finished, you'll be able to use probability distributions to model events and understand your data.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course recall the sets of statistical tools used to understand data compare and contrast population metrics with sample metrics recall the characteristics of discrete and continuous probability distributions sample and analyze data that follows uniform distribution sample and analyze data which follows binomial distribution
• calculate probabilities of events in the binomial distribution sample and analyze data which follows uniform distribution examine and interpret normal distributions and exponential distributions interpret QQ plots for normally and non-normally distributed data use QQ plots to compare samples from different distributions summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Statistical Tools for Understanding Data
• 3.
Population and Sample Metric Comparisons
• 4.
Characteristics of Probability Distribution Types
• 5.
Sampling and Analyzing Uniform Distribution Data
• 6.
Sampling and Analyzing Binomial Distribution Data
• 7.
Computing Probabilities in Binomial Distributions
• 8.
Sampling and Analyzing Poisson Distribution Data
• 9.
Examining Normal and Exponential Distributions
• 10.
Interpreting QQ Plots Using R
• 11.
Using QQ Plots in R to Compare Datasets
• 12.
Course Summary

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