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Statistical Inference
  • Level Foundation
  • المدة 54 ساعات hours
  • الطبع بواسطة Johns Hopkins University
  • Offered by

عن

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.

الوحدات

Module 1: Introduction

    • 1 Videos
    • 7 Readings
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1 Videos

  • Introductory video

7 Readings

  • Welcome to Statistical Inference
  • Some introductory comments
  • Pre-Course Survey
  • Syllabus
  • Course Book: Statistical Inference for Data Science
  • Data Science Specialization Community Site
  • Homework Problems

Module 2: Probability

    • 3 Videos
    • 1 Readings
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3 Videos

  • 02 01 Introduction to probability
  • 02 02 Probability mass functions
  • 02 03 Probability density functions

1 Readings

  • Probability

Module 3: Conditional Probability

    • 3 Videos
    • 1 Readings
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3 Videos

  • 03 01 Conditional Probability
  • 03 02 Bayes' rule
  • 03 03 Independence

1 Readings

  • Conditional probability

Module 4: Expected Values

    • 3 Videos
    • 1 Readings
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3 Videos

  • 04 01 Expected values
  • 04 02 Expected values, simple examples
  • 04 03 Expected values for PDFs

1 Readings

  • Expected values

Practical R Exercises in swirl

    • 1 Readings
    • 5 Programming
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5 Programming

  • swirl Lesson 1: Introduction
  • swirl Lesson 2: Probability1
  • swirl Lesson 3: Probability2
  • swirl Lesson 4: ConditionalProbability
  • swirl Lesson 5: Expectations

1 Readings

  • Practical R Exercises in swirl 1

Week 1 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 1

Module 5: Variability

    • 4 Videos
    • 1 Readings
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4 Videos

  • 05 01 Introduction to variability
  • 05 02 Variance simulation examples
  • 05 03 Standard error of the mean
  • 05 04 Variance data example

1 Readings

  • Variability

Module 6: Distributions

    • 3 Videos
    • 1 Readings
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3 Videos

  • 06 01 Binomial distrubtion
  • 06 02 Normal distribution
  • 06 03 Poisson

1 Readings

  • Distributions

Module 7: Asymptotics

    • 3 Videos
    • 1 Readings
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3 Videos

  • 07 01 Asymptotics and LLN
  • 07 02 Asymptotics and the CLT
  • 07 03 Asymptotics and confidence intervals

1 Readings

  • Asymptotics

Practical R Exercises in swirl

    • 1 Readings
    • 3 Programming
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3 Programming

  • swirl Lesson 1: Variance
  • swirl Lesson 2: CommonDistros
  • swirl Lesson 3: Asymptotics

1 Readings

  • Practical R Exercises in swirl Part 2

Week 2 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 2

Module 8: Confidence intervals

    • 4 Videos
    • 1 Readings
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4 Videos

  • 08 01 T confidence intervals
  • 08 02 T confidence intervals example
  • 08 03 Independent group T intervals
  • 08 04 A note on unequal variance

1 Readings

  • Confidence intervals

Module 9: Hypothesis testing

    • 4 Videos
    • 1 Readings
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4 Videos

  • 09 01 Hypothesis testing
  • 09 02 Example of choosing a rejection region
  • 09 03 T tests
  • 09 04 Two group testing

1 Readings

  • Hypothesis testing

Module 10: P-values

    • 2 Videos
    • 1 Readings
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2 Videos

  • 10 01 Pvalues
  • 10 02 Pvalue further examples

1 Readings

  • P-values

Knitr

    • 1 Videos
    • 1 Readings
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1 Videos

  • Just enough knitr to do the project

1 Readings

  • Knitr

Practical R Exercises in swirl

    • 1 Readings
    • 3 Programming
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3 Programming

  • swirl Lesson 1: T Confidence Intervals
  • swirl Lesson 2: Hypothesis Testing
  • swirl Lesson 3: P Values

1 Readings

  • Practical R Exercises in swirl Part 3

Week 3 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 3

Module 11: Power

    • 4 Videos
    • 1 Readings
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4 Videos

  • 11 01 Power
  • 11 02 Calculating Power
  • 11 03 Notes on power
  • 11 04 T test power

1 Readings

  • Power

Module 12: Multiple Comparisons

    • 1 Videos
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1 Videos

  • 12 01 Multiple Comparisons

Module 13: Resampling

    • 4 Videos
    • 1 Readings
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4 Videos

  • 13 01 Bootstrapping
  • 13 02 Bootstrapping example
  • 13 03 Notes on the bootstrap
  • 13 04 Permutation tests

1 Readings

  • Resampling

Week 4 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 4

Course Project

    • 1 PeerReview
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1 Peer Review

  • Statistical Inference Course Project

Practical R Exercises in swirl

    • 1 Readings
    • 3 Programming
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3 Programming

  • swirl Lesson 1: Power
  • swirl Lesson 2: Multiple Testing
  • swirl Lesson 3: Resampling

1 Readings

  • Practical R Exercises in swirl Part 4

Share Your Feedback

    • 1 Readings
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1 Readings

  • Post-Course Survey

Auto Summary

Unlock the essentials of drawing conclusions from data with the "Statistical Inference" course on Coursera. Focused on Data Science & AI, this foundational course simplifies complex inferential methods and theories for practical application. Guided by expert instruction, learners will master various inference techniques and make informed data analysis decisions. Available with Starter, Professional, and Paid subscription options, this course is ideal for anyone aiming to strengthen their analytical skills in data science.

Brian Caffo, PhD
Instructors

Brian Caffo, PhD

Roger D. Peng, PhD
Instructors

Roger D. Peng, PhD

Jeff Leek, PhD
Instructors

Jeff Leek, PhD