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Biostatistics

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. . Biostatistics.

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Biostatistics

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  1. Biostatistics Unit 1 - Introduction

  2. Biostatistics Biostatistics can be defined as the application of the mathematical tools used in statistics to the fields of biological sciences and medicine. 

  3. Biostatistics 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 drawing 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.

  8. Variables • A qualitative variable is characterized by its inability to be measured but it can be sorted into categories.

  9. 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.

  10. 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.

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

  12. 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.

  13. 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)

  14. 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

  15. Random Number Table

  16. 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

  17. 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.

  18. fin

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