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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.
您将学到的内容有
- Describe variability, distributions, limits, and confidence intervals
- Make informed data analysis decisions
- Understand the process of drawing conclusions about populations or scientific truths from data
- Use p-values, confidence intervals, and permutation tests
您将获得的技能
StatisticsStatistical InferenceStatistical Hypothesis Testing
教学大纲 - 您将从这门课程中学到什么
Week
1Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 个视频（共 64 分钟）, 11 个阅读材料, 6 个测验
10 个视频
02 01 Introduction to probability6分钟
02 02 Probability mass functions7分钟
02 03 Probability density functions13分钟
03 01 Conditional Probability3分钟
03 02 Bayes' rule7分钟
03 03 Independence3分钟
04 01 Expected values5分钟
04 02 Expected values, simple examples2分钟
04 03 Expected values for PDFs7分钟
11 个阅读材料
Welcome to Statistical Inference10分钟
Some introductory comments10分钟
Pre-Course Survey10分钟
Syllabus10分钟
Course Book: Statistical Inference for Data Science10分钟
Data Science Specialization Community Site10分钟
Homework Problems10分钟
Probability10分钟
Conditional probability10分钟
Expected values10分钟
Practical R Exercises in swirl 110分钟
1 个练习
Quiz 112分钟
Week
2Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
10 个视频（共 76 分钟）, 4 个阅读材料, 4 个测验
10 个视频
05 02 Variance simulation examples2分钟
05 03 Standard error of the mean7分钟
05 04 Variance data example3分钟
06 01 Binomial distrubtion3分钟
06 02 Normal distribution15分钟
06 03 Poisson6分钟
07 01 Asymptotics and LLN4分钟
07 02 Asymptotics and the CLT8分钟
07 03 Asymptotics and confidence intervals20分钟
4 个阅读材料
Variability10分钟
Distributions10分钟
Asymptotics10分钟
Practical R Exercises in swirl Part 210分钟
1 个练习
Quiz 216分钟
Week
3Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 个视频（共 83 分钟）, 5 个阅读材料, 4 个测验
11 个视频
08 02 T confidence intervals example4分钟
08 03 Independent group T intervals14分钟
08 04 A note on unequal variance3分钟
09 01 Hypothesis testing4分钟
09 02 Example of choosing a rejection region5分钟
09 03 T tests7分钟
09 04 Two group testing17分钟
10 01 Pvalues7分钟
10 02 Pvalue further examples5分钟
Just enough knitr to do the project3分钟
5 个阅读材料
Confidence intervals10分钟
Hypothesis testing10分钟
P-values10分钟
Knitr10分钟
Practical R Exercises in swirl Part 310分钟
1 个练习
Quiz 314分钟
Week
4Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
9 个视频（共 86 分钟）, 4 个阅读材料, 5 个测验
9 个视频
11 01 Power4分钟
11 02 Calculating Power12分钟
11 03 Notes on power4分钟
11 04 T test power8分钟
12 01 Multiple Comparisons25分钟
13 01 Bootstrapping7分钟
13 02 Bootstrapping example3分钟
13 03 Notes on the bootstrap10分钟
13 04 Permutation tests9分钟
4 个阅读材料
Power10分钟
Resampling10分钟
Practical R Exercises in swirl Part 410分钟
Post-Course Survey10分钟
1 个练习
Quiz 418分钟
4.2
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热门审阅
创建者 AP•Mar 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.
创建者 LH•Jan 31st 2016
I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference
讲师
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
关于 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.
关于 Data Science 专项课程
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
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