4.8

235 ratings

•

66 reviews

Johns Hopkins University

Informações sobre o curso

A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

Comece imediatamente e aprenda em seu próprio cronograma.

Sugerido: 8 weeks of study, 2-3 hours/week

Legendas: English

BiostatisticsStatisticsData AnalysisSampling (Statistics)Sampling Statistics

Comece imediatamente e aprenda em seu próprio cronograma.

Sugerido: 8 weeks of study, 2-3 hours/week

Legendas: English

Section

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 videos (Total 98 min), 1 reading

Introduction to Module 1 2m

Lecture 1A: The Role of Statistics in Public Health Research10m

Lecture 1B: Samples Versus Population 20m

Lecture 1C: Considerations with Regard to Study Design 37m

Lecture 1D: Data Types and Summarization 9m

Lecture 1E: Self-Assessment/Active Learning Exercise 15m

Syllabus10m

Section

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 videos (Total 223 min), 6 readings, 6 quizzes

Lecture 2A: Continuous Data: Useful Summary Statistics24m

Lecture 2B: Continuous Data: Visual Displays 23m

Lecture 2C: Continuous Data: The Role of Sample Size on Sample Based Estimates 27m

Lecture 2D: Continuous Data: Comparing Distributions 20m

Lecture 2E: Self Assessment/Active Learning Exercise 19m

Lecture 3A: The Standard Normal Distribution Defined22m

Lecture 3B: Applying the Principles of the Normal Distribution to Sample Data 34m

Lecture 3C: What Happens When We Apply the Properties of the Normal Distribution to Data Not Approximately Normal: A Warning 17m

Lecture 3D: Some Practice Exercises . 29m

Learning Objectives, Lecture Set 210m

Learning Objectives, Lecture Set 310m

Supporting Documents for Homework 1A10m

Supporting Information for Homework 1D10m

Supporting Information for Homework 1E10m

Quiz 1 Solutions10m

Homework 1A26m

Homework 1B10m

Homework 1C12m

Homework 1D16m

Homework 1E16m

Quiz 1 (Covers Material Through Lecture 3)24m

Section

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 videos (Total 110 min), 1 reading

Lecture 4B, part 117m

Lecture 4B, part 213m

Lecture 4C: Comparing Distributions of Binary Data: Odds Ratios 27m

Lecture 4D: A Brief Note About Ratios 15m

Lecture 4E: Self Assessment/Active Learning Exercise 21m

Learning Objectives, Lecture Set 410m

Section

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 videos (Total 112 min), 5 readings, 7 quizzes

Lecture 5B: Numerically Comparing Groups on Time to Event Outcomes 19m

Lecture 5C Part 1 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach22m

Lecture 5C Part 2 Time to Event Data: Graphical Summarization: Kapalan-Meier Approach13m

Lecture 5D: Graphically Comparing Groups on Time to Event Outcomes 13m

Lecture 5E: Self Assessment/Active Learning Exercise 17m

Learning Objectives, Lecture Set 510m

Supporting Documents For Homework 2A10m

Supporting Information for Homework 2C10m

Formula Files for Quiz 210m

Quiz 2 Solutions10m

Homework 2A18m

Homework 2B10m

Homework 2C8m

Homework 2D18m

Homework 2E10m

Homework 2F14m

Quiz 2 (Covers Material Through Lecture 5)26m

Section

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 videos (Total 211 min), 2 readings

Lecture 6A: Sampling Distribution Definition15m

Lecture 6B: Examples: Sampling Distribution for a Single Mean 23m

Lecture 6C: Examples: Sampling Distribution for a Single Proportion, Incidence Rate 19m

Lecture 6D: Estimating Sampling Distribution Characteristics from Single Samples of Data24m

Lecture 6E: Self Assessment/Active Learning Exercise 18m

Lecture 7A: Confidence Intervals for Population Means24m

Lecture 7B: Confidence Intervals for Sample Proportions and Rates 16m

Lecture 7C: On the Interpretation of CIs 16m

Lecture 7D: A Note about CIs for Smaller Samples/Exact CIs 25m

Lecture 7E: Self-Assessment/Active Learning Exercise 22m

Learning Objectives, Lecture Set 610m

Learning Objectives, Lecture Set 710m

Section

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 videos (Total 159 min), 3 readings, 4 quizzes

Lecture 8B: Confidence Intervals for Differences in Population Means 32m

Lecture 8C: Confidence Intervals for Binary Comparisons: Part 1, Difference in Proportions (Risk Difference) - Subtitles Pending19m

Lecture 8D: Confidence Intervals for Binary Comparisons: Part 2: Ratio of Proportions (Relative Risk), Odds Ratio - Subtitles Pending27m

Lecture 8E: Confidence Intervals for Incidence Rate Ratios 15m

Lecture 8F: Revisiting Ratios and the Log Scale (with Respect to Effect Sized and CIs) 6m

Lecture 8G: Self Assessment/Active Learning Exercise 27m

Learning Objectives, Lecture Set 810m

Supporting Information For Homework 3A10m

Quiz 3 Solutions10m

Homework 3A28m

Homework 3B36m

Homework 3C30m

Quiz 3 (Covers Material Through Lecture 8)26m

Section

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 videos (Total 188 min), 2 readings

Lecture 9A: Two-Group Hypothesis Testing: The General Concept19m

Lecture 9B: Comparing Means between Two Populations: The Paired Approach 22m

Lecture 9C: Comparing Means between Two Populations: The Unpaired Approach 26m

Lecture 9D: Section D: Debriefing on the p-value, Part 1 23m

Lecture 9E: Self Assessment Exercise 16m

Lecture 10A: Comparing Proportions between Two Populations: The “Z-Test” Approach17m

Lecture 10B: Comparing Proportions between Two Populations: Chi-Squared and Fisher’s Exact Tests 26m

Lecture 10C: Comparing Time-to-Event Between Two Populations: The Log-Rank Test 20m

Lecture 10D: Debriefing on the P-Value, Part II 10m

Learning Objectives, Lecture Set 910m

Learning Objectives, Lecture Set 1010m

Section

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 videos (Total 174 min), 5 readings, 6 quizzes

Lecture 11B: (Hypothesis Testing) Comparing Proportions between More than Two Populations: Chi-Square Tests 17m

Lecture 11C: Hypothesis Testing) Comparing Survival Curves between More than Two Populations: Log-Rank Tests 14m

Lecture 12A: Precision and Sample Size : An Overview14m

Lecture 12B: Computing Sample Size to Achieve a Desired Level of Precision : Single Population Quantities15m

Lecture 12C: Computing Sample Size to Achieve a Desired Level of Precision : Population Comparison Quantities9m

Lecture 13A: Power and Its Influences20m

Lecture 13B: Sample Size Computations For Studies Comparing Two (or More) Means23m

Lecture 13C: Sample Size Computations For Studies Comparing Two (or More) Proportions or Incidence Rates16m

Lecture 13D: Sample Size and Study Design Principles: A Brief Summary5m

Lecture 13E: An Example of the Mathematics Behind Power Computations12m

Learning Objectives, Lecture Set 1110m

Supporting Documents For Homework 410m

Quiz 4 Solutions10m

Learning Objectives, Lecture Set 1210m

Learning Objectives, Lecture Set 1310m

Homework 4A14m

Homework 4B12m

Homework 4C14m

Homework 4D14m

Homework 4E8m

Quiz 4 (Covers Material Through Lecture 11)32m

4.8

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By ZC•Oct 10th 2017

It is an awesome course which does not require much math background and provides clear logic behind the interpretation of statistic results from the public health area. Strongly recommend!

By C•Aug 10th 2016

i enjoyed the course, however i am having an immense problem trying to get the certificate despite having paid for it and completing all my assignments. i need help in this. thanks

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....

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