You will get idea about the present fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. The goal of this course is to equip you with biostatistics and quantitative scientists with core applied statistical concepts.
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Biostatistics Methods - Part 1
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You will get idea about the present fundamental concepts in applied probability, exploratory data
analysis, and statistical inference, focusing on β¦
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You will get idea about the present fundamental concepts in applied probability, exploratory data
analysis, and statistical inference, focusing on probability and analysis of one and two samples. The
goal of this course is to equip you with biostatistics and quantitative scientists with core applied
statistical concepts.
You will learn graphical and exploratory data analysis, hypothesis testing, con dence intervals,
discrete and continuous probability models, variance, central limit theorem, inference, maximum
likelihood estimation etc. and of course basic concepts of ANOVA.
Assessment
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.
Certification
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assignment of the course as part of the assessment. Upon successful completion of a course, you can
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Module: 01
Lecture Number 1 Set Theory Basics and Probability 00:05:00
Lecture Number 2 Introduction to Probability 00:10:00
Lecture Number 3 Expected Values 1 00:10:00
Lecture Number 4 Random Vectors, Independence 1 00:10:00
Module: 02
Lecture Number 5 Conditional Probabilities, Bayeβs Rule 1 00:10:00
Lecture Number 6 Likelihood 00:08:00
Lecture Number 7 Distributions 00:10:00
Lecture Number 8 Asymptotics 00:10:00
Module: 03
Lecture Number 9 Con dence Intervals 1 00:15:00
Lecture Number 10 Con dence Intervals 1 00:05:00
Lecture Number 11 Presentation of Data 1 00:15:00
Lecture Number 12 Bootstrapping 1 00:08:00
Lecture Number 14 Logs and Geometric Means 1 00:10:00
Assessment
COURSE CURRICULUM
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