Biostatistics is an essential skill for every public health researcher because it provides a set of precise methods for extracting meaningful conclusions from data. In this second course of the Biostatistics in Public Health Specialization, you'll learn to evaluate sample variability and apply statistical hypothesis testing methods. Along the way, you'll perform calculations and interpret real-world data from the published scientific literature. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Biostatistics in Public Health
Hypothesenüberprüfung im öffentlichen Gesundheitswesen
von
Über diesen Kurs
92,252 kürzliche Aufrufe
Was Sie lernen werden
Use statistical methods to analyze sampling distribution
Estimate and interpret 95% confidence intervals for single samples
Estimate and interpret 95% confidence intervals for two populations
Estimate and interpret p values for hypothesis testing
Kompetenzen, die Sie erwerben
Confidence IntervalStatistical Hypothesis Testingp valuessampling
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 4 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Anfänger“
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
Ca. 18 Stunden zum Abschließen
Empfohlen: 4 weeks of study, 3-4 hours/week
Englisch
Untertitel: Englisch
Lehrplan - Was Sie in diesem Kurs lernen werden
3 Stunden zum Abschließen
Sampling Distributions and Standard Errors
Within module one, you will learn about sample statistics, sampling distribution, and the central limit theorem. You will have the opportunity to test your knowledge with a practice quiz and, then, apply what you learned to the graded quiz.
3 Stunden zum Abschließen
7 Videos (Gesamt 90 min), 1 Lektüre, 2 Quiz
7 Videos
Sampling Distribution Definition9m
Examples: Sampling Distribution for a Single Mean16m
Examples: Sampling Distribution for a Single Proportion, Incidence Rate16m
Estimating Sampling Distribution Characteristics from Single Samples of Data22m
Additional Examples7m
Examples: Sampling Distribution for a Single Proportion, Incidence Rate (coursera test)16m
1 Lektüre
Formative Quiz 1 Solutions10m
2 praktische Übungen
Practice Quiz1h
Sampling Distributions and Standard Errors45m
3 Stunden zum Abschließen
Confidence Intervals for Single Population Parameters
Module two builds upon previous materials to discuss confidence intervals, the need for ample sizes of data, and ways to get around the need for ample sizes of data. The practice quiz helps you prepare for the graded quiz.
3 Stunden zum Abschließen
6 Videos (Gesamt 70 min), 1 Lektüre, 2 Quiz
6 Videos
Confidence Intervals for Single Population Parameters15m
Confidence Intervals for Population Proportions and Incidence Rates15m
So What Is a Confidence Interval, and How Should It Be Interpreted?13m
A Note on Confidence Intervals for Population Quantities Based on Small Samples15m
Additional Examples8m
1 Lektüre
Formative Quiz 2 Solutions10m
2 praktische Übungen
Practice Quiz: Confidence Intervals for Single Population Parameters1h
Quiz: Confidence Intervals for Single Population Parameters1h
4 Stunden zum Abschließen
Confidence Intervals for Population Comparison Measures
Within module three, confidence intervals are discussed at length and ratios are discussed again. Aside from the lectures, you will also be completing a practice quiz and graded quiz.
4 Stunden zum Abschließen
8 Videos (Gesamt 121 min), 1 Lektüre, 2 Quiz
8 Videos
Confidence Intervals for Population Comparison Measures15m
Confidence Intervals for Comparing Means of Continuous Outcomes Between Two Populations28m
Confidence Intervals for Binary Comparisons: Part 1, Difference in Proportions12m
Confidence Intervals for Binary Comparisons: Part 2, Ratio of Proportions and Odds Ratios24m
Confidence Intervals for Incidence Rate Ratios10m
A Brief Note About Ratios (Again)11m
Additional Examples17m
1 Lektüre
Solutions, Formative Assessment10m
2 praktische Übungen
Practice Quiz: Confidence Intervals for Population Comparison Measures1h
Confidence Intervals for Population Comparison Measures1h
3 Stunden zum Abschließen
Two-Group Hypothesis Testing: The General Concept and Comparing Means
Within module four, you will look at statistical hypothesis tests, confidence intervals, and p-value. There is a practice quiz to prepare you for the graded quiz.
3 Stunden zum Abschließen
6 Videos (Gesamt 95 min), 1 Lektüre, 2 Quiz
6 Videos
Hypothesis Testing: The General Concept, and for Comparing Means Between Two Populations17m
(Hypothesis Testing) Comparing Means Between Two Populations: The Paired Approach21m
(Hypothesis Testing) Comparing Means Between Two Populations: The Unpaired Approach20m
Debriefing on the p-Value, Part 120m
Additional Examples12m
1 Lektüre
Solutions, Formative Asessment10m
2 praktische Übungen
Practice Quiz: Two-Group Hypothesis Testing: The General Concept and Comparing Means
Two-Group Hypothesis Testing: The General Concept and Comparing Means, and Earlier Course Material1h
4 Stunden zum Abschließen
Hypothesis Testing (Comparing Proportions and Incidence Rates Between Two Populations) & Extended Hypothesis Testing
4 Stunden zum Abschließen
11 Videos (Gesamt 138 min)
11 Videos
(Hypothesis Testing) Comparing Proportions Between Two Populations: The “z-Test” Approach12m
(Hypothesis Testing) Comparing Proportions Between Two Populations: Chi-Square and Fisher’s Exact Test Approaches23m
Hypothesis Tests for Comparing Incidence Rates Between Two Populations20m
Debriefing on the p-Value and Hypothesis Testing, Part 28m
Additional Examples8m
Introduction2m
(Hypothesis Testing) Comparing Means Between More Than Two Populations: Analysis of Variance (ANOVA)18m
(Hypothesis Testing) Comparing Proportions Between More Than Two Populations: Chi-Square Tests14m
Hypothesis Testing) Comparing Survival Curves Between More Than Two Populations: Log-Rank Tests8m
Several Issues to Consider18m
2 praktische Übungen
Practice Quiz1h
Hypothesis Testing (Comparing Proportions and Incidence Rates Between Two Populations) & Extended Hypothesis Testing1h
2 Stunden zum Abschließen
Project
During this module, you get the chance to demonstrate what you've learned by putting yourself in the shoes of biostatistical consultant on two different studies, one about asthma medication and the other about self-administration of injectable contraception. The two research teams have asked you to help them interpret previously published results in order to inform the planning of their own studies. If you've already taken the Summarization and Measurement course, then this scenario will be familiar.
2 Stunden zum Abschließen
1 Lektüre
1 Lektüre
Biostatistical Consulting Project30m
1 praktische Übung
Course Project Quiz1h
Top-Bewertungen von Hypothesenüberprüfung im öffentlichen Gesundheitswesen
I really enjoyed the simplicity of the presentations. I feel I still need to review the materials to ensure it sinks. All in all, This is one of the 3 online courses I have taken.
Excellent course, very well explained and the scientific articles used were a superb way to boost my confidence that I can do this, meaning stats. Thank you!
Dozent
John McGready, PhD, MS
Associate Scientist, BiostatisticsBloomberg School of Public Health
33,496 Kursteilnehmer
6 Kursevon
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.
Über den Spezialisierung Biostatistics in Public Health
This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.
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