Statistical Inference

4.1
2,534 ratings
539 reviews

Course 6 of 10 in the Data Science Specialization

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
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Clock

Aprox. 16 horas restantes

Sugerido: 5 hours/week
Comment Dots

English

Legendas: English

O que você vai aprender

  • Check
    Describe variability, distributions, limits, and confidence intervals
  • Check
    Make informed data analysis decisions
  • Check
    Understand the process of drawing conclusions about populations or scientific truths from data
  • Check
    Use p-values, confidence intervals, and permutation tests

Habilidades que você terá

StatisticsR ProgrammingData AnalysisType I And Type Ii Errors
Globe

curso 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Clock

Aprox. 16 horas restantes

Sugerido: 5 hours/week
Comment Dots

English

Legendas: English

Syllabus - What you will learn from this course

1

Section
Clock
18 hours to complete

Week 1: Probability & Expected Values

This week, we'll focus on the fundamentals including probability, random variables, expectations and more. ...
Reading
10 videos (Total 64 min), 11 readings, 6 quizzes
Video10 videos
02 01 Introduction to probability6m
02 02 Probability mass functions7m
02 03 Probability density functions13m
03 01 Conditional Probability3m
03 02 Bayes' rule7m
03 03 Independence3m
04 01 Expected values5m
04 02 Expected values, simple examples2m
04 03 Expected values for PDFs7m
Reading11 readings
Welcome to Statistical Inference10m
Some introductory comments10m
Pre-Course Survey10m
Syllabus10m
Course Book: Statistical Inference for Data Science10m
Data Science Specialization Community Site10m
Homework Problems10m
Probability10m
Conditional probability10m
Expected values10m
Practical R Exercises in swirl 110m
Quiz1 practice exercises
Quiz 112m

2

Section
Clock
11 hours to complete

Week 2: Variability, Distribution, & Asymptotics

We're going to tackle variability, distributions, limits, and confidence intervals....
Reading
10 videos (Total 76 min), 4 readings, 4 quizzes
Video10 videos
05 02 Variance simulation examples2m
05 03 Standard error of the mean7m
05 04 Variance data example3m
06 01 Binomial distrubtion3m
06 02 Normal distribution15m
06 03 Poisson6m
07 01 Asymptotics and LLN4m
07 02 Asymptotics and the CLT8m
07 03 Asymptotics and confidence intervals20m
Reading4 readings
Variability10m
Distributions10m
Asymptotics10m
Practical R Exercises in swirl Part 210m
Quiz1 practice exercises
Quiz 216m

3

Section
Clock
11 hours to complete

Week: Intervals, Testing, & Pvalues

We will be taking a look at intervals, testing, and pvalues in this lesson....
Reading
11 videos (Total 83 min), 5 readings, 4 quizzes
Video11 videos
08 02 T confidence intervals example4m
08 03 Independent group T intervals14m
08 04 A note on unequal variance3m
09 01 Hypothesis testing4m
09 02 Example of choosing a rejection region5m
09 03 T tests7m
09 04 Two group testing17m
10 01 Pvalues7m
10 02 Pvalue further examples5m
Just enough knitr to do the project3m
Reading5 readings
Confidence intervals10m
Hypothesis testing10m
P-values10m
Knitr10m
Practical R Exercises in swirl Part 310m
Quiz1 practice exercises
Quiz 314m

4

Section
Clock
13 hours to complete

Week 4: Power, Bootstrapping, & Permutation Tests

We will begin looking into power, bootstrapping, and permutation tests....
Reading
9 videos (Total 86 min), 4 readings, 5 quizzes
Video9 videos
11 02 Calculating Power12m
11 03 Notes on power4m
11 04 T test power8m
12 01 Multiple Comparisons25m
13 01 Bootstrapping7m
13 02 Bootstrapping example3m
13 03 Notes on the bootstrap10m
13 04 Permutation tests9m
Reading4 readings
Power10m
Resampling10m
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
Quiz1 practice exercises
Quiz 418m
4.1
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started a new career after completing these courses
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83%

got a tangible career benefit from this course

Top Reviews

By KSMay 16th 2018

Everything absolutely amazing. Sometimes there were some troubles with audio files, but I think it doesn't affect on the course. Because I have no trouble with understanding. Thank you to al masters!

By APMar 22nd 2017

The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.

Instructors

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Brian Caffo, PhD

Professor, Biostatistics
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Roger D. Peng, PhD

Associate Professor, Biostatistics
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Jeff Leek, PhD

Associate Professor, Biostatistics

About Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

Frequently Asked Questions

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