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
10.476 bereits angemeldet
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Spezialisierung Biostatistics in Public Health Johns Hopkins UniversityÜber diesen Kurs
19.759 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 4 in
Stufe „Anfänger“
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
Ca. 19 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Deutsch, Russisch, Englisch, Spanisch
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
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 4 in
Stufe „Anfänger“
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
Ca. 19 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Deutsch, Russisch, Englisch, Spanisch
von
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.
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
6 Videos (Gesamt 74 min), 1 Lektüre, 2 Quiz
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
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
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
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)
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
Bewertungen
Top-Bewertungen von HYPOTHESENÜBERPRÜFUNG IM ÖFFENTLICHEN GESUNDHEITSWESEN
von SF21. Mai 2020
You have to use outside sources and practice questions to really understand the material. This course makes you think and demands that you know the information. It was a great class. Thank you.
von RC23. Juni 2020
Huge coverage of hypothesis testing. Some lectures were quite repetitive or similar in nature and those could be reshaped as it seemed puzzling and boring. However, It was an informative one.
von DK18. Juli 2020
excellant descriptions, good examples and challenging practice sessions. Better if some more were added about ANOVA also. If it is considered as advanced , then it is ok. Good experience
von AA8. Juli 2021
Excellent course, excellent teaching. Prof McGready knows his stuff and also knows how to teach it. The projects exercices are fun to work on and see how statistics is used in research.
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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