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In this course it will teach the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will also describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: Harvard University

Course Curriculum

Module: 1
Introduction to statistics and R for the Life Sciences 00:02:00
Getting Started with R 00:06:00
GitHub 00:03:00
RStudio 00:04:00
Using the Textbook 00:02:00
RStudio for Organization 00:07:00
Introduction to dplyr 00:08:00
Motivation 00:05:00
Introduction to Random Variables 00:04:00
Introduction to Null Distribution 00:10:00
Module: 2
Probability Distributions 00:03:00
The Normal Distribution 00:06:00
Populations, parameters, and sample estimates 00:05:00
Central Limit Theorem (CLT) 00:06:00
CLT in Practice 00:04:00
T-test 00:06:00
T-test in practice 00:03:00
Introduction to Inference 00:02:00
Confidence Intervals 00:08:00
Power Calculations 00:07:00
Module: 3
Monte Carlo Simulation 00:06:00
Association Tests 00:08:00
Histogram 00:04:00
qq-plot 00:04:00
Boxplot 00:04:00
Scatterplot 00:07:00
Symmetry of Log Ratios 00:02:00
Plots to Avoid 00:05:00
Avoid Pseudo 3D 00:01:00
Median, MAD, and Spearman Correlation 00:04:00
Mann-Whitney-Wilcoxon Test 00:03:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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