생물통계학

건국대학교
윤성호

페이스북 트위터 카카오톡 네이버밴드 링크복사

주제분류

자연과학 >생물ㆍ화학ㆍ환경 >생물학
강의학기

2017년 2학기

조회수

24,850
평점

4.3/5.0 (3)

강의계획서

This is an introductory biostatistics course to provide foundation and application of statistics in the field of public health. The course covers both exploratory and numerical methods of describing and performing basic confirmatory analysis of data sets. The topics consist of measurement, types of studies, and concepts of probability and statistical inference (estimation and hypothesis testing). R programming language will be taught.

수강안내 및 수강신청
※ 수강확인증 발급을 위해서는 수강신청이 필요합니다

Course info & 1. Measurement

차시별 강의

1.	Course info & 1. Measurement	1.1 What is Biostatistics? 1.2 Origanization of Data 1.3 Type of Measurements 1.4 Data Quality
2.	1. Measurement	1.1 What is Biostatistics? 1.2 Origanization of Data 1.3 Type of Measurements 1.4 Data Quality
3.	2. Types of Studies	Surveys and comparative studies
4.	3. Frequency Distributions	Stemplots / distributional shape, location, and spread / frequency tables
5.	3. Frequency Distributions	Stemplots / distributional shape, location, and spread / frequency tables
6.	4. Summary Statistics	Measures of central tendency and variability
7.	5. Probability Concepts	Discrete and continuous random variables / probability mass function (pmfs) / probability density function (pdfs)
8.	5. Probability Concepts	Discrete and continuous random variables / probability mass function (pmfs) / probability density function (pdfs)
9.	6. Binomial Distributions	Calculate and interpret binomial probabilities
10.	7. Normal Distributions	Determine approximate probabilities for Normal random variables
11.	8. Introduction to Statistical Inference	Sampling distribution of a sample mean from a Normal population
12.	8. Introduction to Statistical Inference	Sampling distribution of a sample mean from a Normal population
13.	2. Data type 11/01	Rstudio, Basic Data Type, Vector Operations, Factor
14.	9. Basics of Hypothesis Testing	P-value, Significance Level and Conclusion, One-Sample z-Test
15.	9. Basics of Hypothesis Testing	Multi-vector, Subset Data
	9. Basics of Hypothesis Testing	Type of Decision Errors, Power of z test
	10. Basics of Confidence Intervals	List, Loops, Function, Package
	10. Basics of Confidence Intervals	Confidence intervals around means
	10. Basics of Confidence Intervals	Writing Data, Reading Data, Looping on the Command line, tapply
	11. Inference about a Mean	One-sample, two-sample, and paired t hypothesis tests on means
	11. Inference about a Mean	Functions for Probabability Distributions, QQ-plot, Inferential Statistics, Paired sample t-test
	12. Comparing Independent Means	One-sample, two-sample, and paired t hypothesis tests on means
	13. ANOVA6	z-tests and chi square tests of independence and homogeneity