Informações sobre o curso
A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.
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cursos 100% online

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Clock

Approx. 27 hours to complete

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

English

Legendas: English

Habilidades que você terá

BiostatisticsStatisticsData AnalysisSampling (Statistics)Sampling Statistics
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Clock

Approx. 27 hours to complete

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

English

Legendas: English

Syllabus - What you will learn from this course

1

Section
Clock
2 hours to complete

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....
Reading
7 videos (Total 98 min), 1 reading
Video7 videos
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
Reading1 readings
Syllabus10m

2

Section
Clock
6 hours to complete

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....
Reading
10 videos (Total 223 min), 6 readings, 6 quizzes
Video10 videos
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
Reading6 readings
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
Quiz6 practice exercises
Homework 1A26m
Homework 1B10m
Homework 1C12m
Homework 1D16m
Homework 1E16m
Quiz 1 (Covers Material Through Lecture 3)24m

3

Section
Clock
2 hours to complete

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....
Reading
6 videos (Total 110 min), 1 reading
Video6 videos
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
Reading1 readings
Learning Objectives, Lecture Set 410m

4

Section
Clock
4 hours to complete

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....
Reading
6 videos (Total 112 min), 5 readings, 7 quizzes
Video6 videos
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
Reading5 readings
Learning Objectives, Lecture Set 510m
Supporting Documents For Homework 2A10m
Supporting Information for Homework 2C10m
Formula Files for Quiz 210m
Quiz 2 Solutions10m
Quiz7 practice exercises
Homework 2A18m
Homework 2B10m
Homework 2C8m
Homework 2D18m
Homework 2E10m
Homework 2F14m
Quiz 2 (Covers Material Through Lecture 5)26m

5

Section
Clock
4 hours to complete

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....
Reading
11 videos (Total 211 min), 2 readings
Video11 videos
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
Reading2 readings
Learning Objectives, Lecture Set 610m
Learning Objectives, Lecture Set 710m

6

Section
Clock
5 hours to complete

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....
Reading
7 videos (Total 159 min), 3 readings, 4 quizzes
Video7 videos
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
Reading3 readings
Learning Objectives, Lecture Set 810m
Supporting Information For Homework 3A10m
Quiz 3 Solutions10m
Quiz4 practice exercises
Homework 3A28m
Homework 3B36m
Homework 3C30m
Quiz 3 (Covers Material Through Lecture 8)26m

7

Section
Clock
3 hours to complete

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....
Reading
10 videos (Total 188 min), 2 readings
Video10 videos
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
Reading2 readings
Learning Objectives, Lecture Set 910m
Learning Objectives, Lecture Set 1010m

8

Section
Clock
5 hours to complete

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...
Reading
11 videos (Total 174 min), 5 readings, 6 quizzes
Video11 videos
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
Reading5 readings
Learning Objectives, Lecture Set 1110m
Supporting Documents For Homework 410m
Quiz 4 Solutions10m
Learning Objectives, Lecture Set 1210m
Learning Objectives, Lecture Set 1310m
Quiz6 practice exercises
Homework 4A14m
Homework 4B12m
Homework 4C14m
Homework 4D14m
Homework 4E8m
Quiz 4 (Covers Material Through Lecture 11)32m
4.8
Direction Signs

33%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

By ZCOct 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 CAug 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

Instructor

Avatar

John McGready, PhD, MS

Associate Scientist, 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....

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