**Informações sobre o curso: **A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

Universidade Johns Hopkins

**Informações sobre o curso: **A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

**Ministrado por:**John McGready, PhD, MS, Associate Scientist, Biostatistics

Compromisso | 8 weeks of study, 2-3 hours/week |

Idioma | English |

Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |

Classificação do usuário |

Programa

SEMANA 1

Introduction and Module 1

This module, consisting of one lecture set, is intended to whet your appetite for the course, and examine the role of biostatistics in public health and medical research. Topics covered include study design types, data types, and data summarization.

7 vídeos, 1 leitura

**Vídeo:**Welcome to the Course!**Reading:**Syllabus**Vídeo:**Introduction to Module 1**Vídeo:**Lecture 1A: The Role of Statistics in Public Health Research**Vídeo:**Lecture 1B: Samples Versus Population**Vídeo:**Lecture 1C: Considerations with Regard to Study Design**Vídeo:**Lecture 1D: Data Types and Summarization**Vídeo:**Lecture 1E: Self-Assessment/Active Learning Exercise

SEMANA 2

Module 2A: Summarization and Measurement

Module 2A consists of two lecture sets that cover measurement and summarization of continuous data outcomes for both single samples, and the comparison of two or more samples. Please see the posted learning objectives for these two lecture sets for more detail.

10 vídeos, 6 leituras, 5 questionários de prática

**Vídeo:**Introduction to Module 2**Reading:**Learning Objectives, Lecture Set 2**Vídeo:**Lecture 2A: Continuous Data: Useful Summary Statistics**Vídeo:**Lecture 2B: Continuous Data: Visual Displays**Vídeo:**Lecture 2C: Continuous Data: The Role of Sample Size on Sample Based Estimates**Vídeo:**Lecture 2D: Continuous Data: Comparing Distributions**Vídeo:**Lecture 2E: Self Assessment/Active Learning Exercise**Reading:**Learning Objectives, Lecture Set 3**Vídeo:**Lecture 3A: The Standard Normal Distribution Defined**Vídeo:**Lecture 3B: Applying the Principles of the Normal Distribution to Sample Data**Vídeo:**Lecture 3C: What Happens When We Apply the Properties of the Normal Distribution to Data Not Approximately Normal: A Warning**Vídeo:**Lecture 3D: Some Practice Exercises .**Reading:**Supporting Documents for Homework 1A**Practice Quiz:**Homework 1A**Practice Quiz:**Homework 1B**Practice Quiz:**Homework 1C**Reading:**Supporting Information for Homework 1D**Practice Quiz:**Homework 1D**Reading:**Supporting Information for Homework 1E**Practice Quiz:**Homework 1E**Reading:**Quiz 1 Solutions

SEMANA 3

Module 2B: Summarization and Measurement

Module 2B includes a single lecture set on summarizing binary outcomes. While at first, summarization of binary outcome may seem simpler than that of continuous outcomes, things get more complicated with group comparisons. Included in the module are examples of and comparisons between risk differences, relative risk and odds ratios. Please see the posted learning objectives for these this module for more details.

6 vídeos, 1 leitura

**Reading:**Learning Objectives, Lecture Set 4**Vídeo:**Lecture 4A: Binary Data: Definition and Summarization (Binomial Distribution, P-Hat, SD)**Vídeo:**Lecture 4B, part 1**Vídeo:**Lecture 4B, part 2**Vídeo:**Lecture 4C: Comparing Distributions of Binary Data: Odds Ratios**Vídeo:**Lecture 4D: A Brief Note About Ratios**Vídeo:**Lecture 4E: Self Assessment/Active Learning Exercise

SEMANA 4

Module 2C: Summarization and Measurement

This module consists of a single lecture set on time-to-event outcomes. Time-to-event data comes primarily from prospective cohort studies with subjects who haven to had the outcome of interest at their time of enrollment. These subjects are followed for a pre-established period of time until they either have there outcome, dropout during the active study period, or make it to the end of the study without having the outcome. The challenge with these data is that the time to the outcome is fully observed on some subjects, but not on those who do not have the outcome during their tenure in the study. Please see the posted learning objectives for each lecture set in this module for more details.

6 vídeos, 5 leituras, 6 questionários de prática

**Reading:**Learning Objectives, Lecture Set 5**Vídeo:**Lecture 5A: Time to Event Data: Definition (Censoring) and Numerical Summary Measures (Incidence Rates)**Vídeo:**Lecture 5B: Numerically Comparing Groups on Time to Event Outcomes**Vídeo:**Lecture 5C Part 1 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach**Vídeo:**Lecture 5C Part 2 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach**Vídeo:**Lecture 5D: Graphically Comparing Groups on Time to Event Outcomes**Vídeo:**Lecture 5E: Self Assessment/Active Learning Exercise**Reading:**Supporting Documents For Homework 2A**Practice Quiz:**Homework 2A**Practice Quiz:**Homework 2B**Reading:**Supporting Information for Homework 2C**Practice Quiz:**Homework 2C**Practice Quiz:**Homework 2D**Practice Quiz:**Homework 2E**Practice Quiz:**Homework 2F**Reading:**Formula Files for Quiz 2**Reading:**Quiz 2 Solutions

SEMANA 5

Module 3A: Sampling Variability and Confidence Intervals

Understanding sampling variability is the key to defining the uncertainty in any given sample/samples based estimate from a single study. In this module, sampling variability is explicitly defined and explored through simulations. The resulting patterns from these simulations will give rise to a mathematical results that is the underpinning of all statistical interval estimation and inference: the central limit theorem. This result will used to create 95% confidence intervals for population means, proportions and rates from the results of a single random sample.

11 vídeos, 2 leituras

**Vídeo:**Introduction to Module 3**Reading:**Learning Objectives, Lecture Set 6**Vídeo:**Lecture 6A: Sampling Distribution Definition**Vídeo:**Lecture 6B: Examples: Sampling Distribution for a Single Mean**Vídeo:**Lecture 6C: Examples: Sampling Distribution for a Single Proportion, Incidence Rate**Vídeo:**Lecture 6D: Estimating Sampling Distribution Characteristics from Single Samples of Data**Vídeo:**Lecture 6E: Self Assessment/Active Learning Exercise**Reading:**Learning Objectives, Lecture Set 7**Vídeo:**Lecture 7A: Confidence Intervals for Population Means**Vídeo:**Lecture 7B: Confidence Intervals for Sample Proportions and Rates**Vídeo:**Lecture 7C: On the Interpretation of CIs**Vídeo:**Lecture 7D: A Note about CIs for Smaller Samples/Exact CIs**Vídeo:**Lecture 7E: Self-Assessment/Active Learning Exercise

SEMANA 6

Module 3B: Sampling Variability and Confidence Intervals

The concepts from the previous module (3A) will be extended create 95% CIs for group comparison measures (mean differences, risk differences, etc..) based on the results from a single study.

7 vídeos, 3 leituras, 3 questionários de prática

**Reading:**Learning Objectives, Lecture Set 8**Vídeo:**Lecture 8A: An Overview of Confidence Intervals for Population Comparison Measures**Vídeo:**Lecture 8B: Confidence Intervals for Differences in Population Means**Vídeo:**Lecture 8C: Confidence Intervals for Binary Comparisons: Part 1, Difference in Proportions (Risk Difference) - Subtitles Pending**Vídeo:**Lecture 8D: Confidence Intervals for Binary Comparisons: Part 2: Ratio of Proportions (Relative Risk), Odds Ratio - Subtitles Pending**Vídeo:**Lecture 8E: Confidence Intervals for Incidence Rate Ratios**Vídeo:**Lecture 8F: Revisiting Ratios and the Log Scale (with Respect to Effect Sized and CIs)**Vídeo:**Lecture 8G: Self Assessment/Active Learning Exercise**Reading:**Supporting Information For Homework 3A**Practice Quiz:**Homework 3A**Practice Quiz:**Homework 3B**Practice Quiz:**Homework 3C**Reading:**Quiz 3 Solutions

SEMANA 7

Module 4A: Making Group Comparisons: The Hypothesis Testing Approach

Module 4A shows a complimentary approach to confidence intervals when comparing a summary measure between two populations via two samples; statistical hypothesis testing. This module will cover some of the most used statistical tests including the t-test for means, chi-squared test for proportions and log-rank test for time-to-event outcomes.

10 vídeos, 2 leituras

**Vídeo:**Introduction to Module 4**Reading:**Learning Objectives, Lecture Set 9**Vídeo:**Lecture 9A: Two-Group Hypothesis Testing: The General Concept**Vídeo:**Lecture 9B: Comparing Means between Two Populations: The Paired Approach**Vídeo:**Lecture 9C: Comparing Means between Two Populations: The Unpaired Approach**Vídeo:**Lecture 9D: Section D: Debriefing on the p-value, Part 1**Vídeo:**Lecture 9E: Self Assessment Exercise**Reading:**Learning Objectives, Lecture Set 10**Vídeo:**Lecture 10A: Comparing Proportions between Two Populations: The “Z-Test” Approach**Vídeo:**Lecture 10B: Comparing Proportions between Two Populations: Chi-Squared and Fisher’s Exact Tests**Vídeo:**Lecture 10C: Comparing Time-to-Event Between Two Populations: The Log-Rank Test**Vídeo:**Lecture 10D: Debriefing on the P-Value, Part II

SEMANA 8

Module 4B: Making Group Comparisons: The Hypothesis Testing Approach

Module 4B extends the hypothesis tests for two populations comparisons to "omnibus" tests for comparing means, proportions or incidence rates between more than two populations with one test

11 vídeos, 5 leituras, 5 questionários de prática

**Reading:**Learning Objectives, Lecture Set 11**Vídeo:**Lecture 11A: (Hypothesis Testing) Comparing Means Between More than Two Populations: Analysis of Variance (ANOVA)**Vídeo:**Lecture 11B: (Hypothesis Testing) Comparing Proportions between More than Two Populations: Chi-Square Tests**Vídeo:**Lecture 11C: Hypothesis Testing) Comparing Survival Curves between More than Two Populations: Log-Rank Tests**Reading:**Supporting Documents For Homework 4**Practice Quiz:**Homework 4A**Practice Quiz:**Homework 4B**Practice Quiz:**Homework 4C**Practice Quiz:**Homework 4D**Practice Quiz:**Homework 4E**Reading:**Quiz 4 Solutions**Reading:**Learning Objectives, Lecture Set 12**Vídeo:**Lecture 12A: Precision and Sample Size : An Overview**Vídeo:**Lecture 12B: Computing Sample Size to Achieve a Desired Level of Precision : Single Population Quantities**Vídeo:**Lecture 12C: Computing Sample Size to Achieve a Desired Level of Precision : Population Comparison Quantities**Reading:**Learning Objectives, Lecture Set 13**Vídeo:**Lecture 13A: Power and Its Influences**Vídeo:**Lecture 13B: Sample Size Computations For Studies Comparing Two (or More) Means**Vídeo:**Lecture 13C: Sample Size Computations For Studies Comparing Two (or More) Proportions or Incidence Rates**Vídeo:**Lecture 13D: Sample Size and Study Design Principles: A Brief Summary**Vídeo:**Lecture 13E: An Example of the Mathematics Behind Power Computations

Perguntas frequentes

Como funciona

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Desenvolvedores

Universidade Johns Hopkins

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.

Classificações e avaliações

Avaliado em 4.8 de 5 decorrente de 207 avaliações

Hi John,

I really enjoyed your enthusiastic attitude and this was a great introduction/refresher course for me into statistics. I really liked math in high school and it was a pity that I did not continue mathematics in university - but now that I can get university content through coursera, I am so grateful for this opportunity! I really want to go into public health and I will definitely take Part 2 of your course. This has been a really fun way into the course. I really enjoyed the homework/active learning/self-learning exercises as they allowed me to put the stuff we learnt into practice. It was also great that you provided all the solutions to the quizzes with explanations - that must have taken a lot of time and work!

I think you already know this but there were a lot of spelling errors on the side and they did detract from the flow occasionally, but you managed to correct it which made the course great anyway! I do wish there were more check exercises applied within the video.

Thanks!

Thanks. I learnt so much. The specificity of the lessons based on public health research helped understand how statistics works. I would suggest to review the materials before making them public as there were some distracting mistakes. Overall, John you are awesome ! Tons of thanks from Cote d'Ivoire !

JL

I took this course to gain foundational understanding of health study design techniques. This course delivered! It was very engaging, the case studies interesting and the quizzes help reinforce learnings. There are some annoying aspects others have mentioned with slide mistakes, etc - but I did not find these interfered with learning. It is basically an applied statistics class for those wanting/not wanting math.

Talvez você também goste de

O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.

HD

Overall well presented course. Repetition of key concepts by different examples really helps to reinforce learning.Good examples from actual reported studies . What would help is to explain the maths /computations for certain formulas that are used in general terms i.e translate it into common language for the non-statistician .

Another thing I noticed that on completing some of the quizzes , I noticed on a couple of occasions that the answers I had given were not the same represented after marking -ones that I had answered correctly somehow had been replaced with ones that were incorrect, which was odd.