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.
제공자:
이 강좌에 대하여
최근 조회 27,961회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
완료하는 데 약 19시간 필요
영어
자막: 영어
배울 내용
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
귀하가 습득할 기술
Confidence IntervalStatistical Hypothesis Testingp valuessampling
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
초급 단계
The recommended math prerequisite is up through and including basic algebra including logarithms and the equation of a line.
완료하는 데 약 19시간 필요
영어
자막: 영어
제공자:
존스홉킨스대학교
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.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
7개 동영상 (총 90분), 1 개의 읽기 자료, 2 개의 테스트
7개의 동영상
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
1개의 읽기 자료
Formative Quiz 1 Solutions10m
2개 연습문제
Practice Quiz1시간
Sampling Distributions and Standard Errors45m
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
6개 동영상 (총 70분), 1 개의 읽기 자료, 2 개의 테스트
6개의 동영상
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
1개의 읽기 자료
Formative Quiz 2 Solutions10m
2개 연습문제
Practice Quiz: Confidence Intervals for Single Population Parameters1시간
Quiz: Confidence Intervals for Single Population Parameters1시간
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
8개 동영상 (총 121분), 1 개의 읽기 자료, 2 개의 테스트
8개의 동영상
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
1개의 읽기 자료
Solutions, Formative Assessment10m
2개 연습문제
Practice Quiz: Confidence Intervals for Population Comparison Measures1시간
Confidence Intervals for Population Comparison Measures1시간
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
6개 동영상 (총 95분), 1 개의 읽기 자료, 2 개의 테스트
6개의 동영상
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
1개의 읽기 자료
Solutions, Formative Asessment10m
2개 연습문제
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 Material1시간
완료하는 데 4시간 필요
Hypothesis Testing (Comparing Proportions and Incidence Rates Between Two Populations) & Extended Hypothesis Testing
완료하는 데 4시간 필요
11개 동영상 (총 138분)
11개의 동영상
(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
2개 연습문제
Practice Quiz1시간
Hypothesis Testing (Comparing Proportions and Incidence Rates Between Two Populations) & Extended Hypothesis Testing1시간
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
1 개의 읽기 자료
1개의 읽기 자료
Biostatistical Consulting Project30m
1개 연습문제
Course Project Quiz1시간
검토
공중 보건학의 가설 테스트 의 최상위 리뷰
SF 제공2020년 5월 21일
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.
RC 제공2020년 6월 23일
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.
DK 제공2020년 7월 18일
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
AR 제공2019년 5월 26일
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.
Biostatistics in Public Health 특화 과정 정보
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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