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Biostatistics

Biostatistics. Frank H. Osborne, Ph. D. Professor. Biostatistics. Unit 1 Introduction. Biostatistics. Biostatistics can be defined as the application of the mathematical tools used in statistics to the fields of biological sciences and medicine. 

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Biostatistics

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  1. Biostatistics Frank H. Osborne, Ph. D. Professor

  2. Biostatistics Unit 1 Introduction

  3. Biostatistics • Biostatistics can be defined as the application of the mathematical tools used in statistics to the fields of biological sciences and medicine.  • Biostatistics is a growing field with applications in many areas of biology including epidemiology, medical sciences, health sciences, educational research and environmental sciences.

  4. Concerns of Biostatistics • Biostatistics is concerned withcollection, organization, summarization and analysis of data. • We seek to draw inferences about a body of data when only a part of the data is observed.

  5. Data • Data are numbers which can be measurements or can be obtained by counting.  • Biostatistics is concerned with the interpretation of the data and the communication of information about the data.   

  6. Sources of data Data are obtained from • Analysis of records • Surveys • Counting • Experiments • Reports

  7. Variables • A variable is an object, characteristic or property that can have different values.   • A quantitative variable can be measured in some way. • A qualitative variable is characterized by its inability to be measured but it can be sorted into categories.

  8. Random variables • A random variable is one that cannot be predicted in advance because it arises by chance.  Observations or measurements are used to obtain the value of a random variable. • Random variables may be discrete or continuous.

  9. Discrete random variable • A discrete random variable has gaps or interruptions in the values that it can have.  • The values may be whole numbers or have spaces between them.

  10. Continuous random variable • A continuous random variable does not have gaps in the values it can assume.  • Its properties are like the real numbers.

  11. Populations and samples • A population is the collection or set of all of the values that a variable may have. • A sample is a part of a population.

  12. Simple random sample • Scientific sampling of the population is necessary to make a valid inference about the population.  • The simplest is called a simple random sample.  • The size of the population is designated by N and the size of the sample is designated by n. (continued)

  13. Simple random sample • A simple random sample (n) is drawn from a population (N) in such a way that every possible sample of size (n) has an equal opportunity of being chosen. • Scientific samples are selected using the Random Number Table

  14. Random Number Table

  15. Statistics and parameters • A statistic is a descriptive measure computed from the data of the sample. • A parameter is a descriptive measure computed from the data of the population

  16. Statistical inference • Statistical inference is the procedure used to reach a conclusion about a population based on the information derived from a sample that has been drawn from that population.

  17. fin

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