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- 2. COURSE OUTLINE • Introduction to biostatistics & definitions • Measures of location and dispersion • Introduction to Probability theory and Probability distributions • Sampling distributions and hypothesis testing • Sampling Techniques • Analysis of Variance: one way anova • Regression and Correlation • Introduction to R • Non parametric Statistics and goodness of fit tests
- 3. Introduction to Statistics & Definitions • Definition of Statistics – Statistics is the study of the collection, organization and interpretation of data. • OR – Statistics are the numerical statements of facts capable of analysis and interpretation. • Biostatistics – is the science of statistics applied to the analysis of biological or medical data. OR It is the application of statistics in biology, medicine and other health related disciplines.
- 4. Importance of statistics in biological and health sciences • Health related research needs statistics in order to come up with sound conclusions. • Descriptive statistics summarize the utility and costs of medical goods and services • Recording of biological related data • Government health and human science agencies gauge the overall health and well-being of populations with statistical information. • Demographic statistics helps to predict the types of services that people are using and the level of care that is affordable to them. • Health economists emphasize (use) statistics in the allocation of scarce medical resources. • Biological research requires statistics in order to make sound conclusions.
- 5. Basic terminology in Statistics • Population: – is defined as the totality of all elements of interest about which information is needed. • Sample: – Is a subset of a population included in an investigation • Sampling: – Is the practice concerned with the selection of a subset of individuals/items from within a population to yield some knowledge about the whole population. • Statistic: – Is a function of observable random variables which does not contain unknown parameters. • Random variable: – A random variable over a sample space is a function that maps every sample point (outcome) to a real number. OR informally: a random variable is the value of a measurement associated with an experiment
- 6. definitions • Data: – Facts, observations and information that come from investigations. • Covariance: – Is a measure of how much two variables change together. In essence, it is a measure of the relationship between two variables. • Correlation: – is a measure of the strength of the relationship between two variables and lies between values -1 and +1. • Discrete variable: – A variable that takes values from a finite or countable set, such as the number of legs of an animal
- 7. definitions • Continuous variable: – is a variable that takes on values within a range • Qualitative variable: – these are also known as categorical variables, they are variables with no natural sense of ordering. They are therefore measured on a nominal scale.e.g sex (male or female • Quantitative variable: – these are numerical variables. They represent a measurable quantity. E.g weight
- 8. Data presentation using graphical techniques • Pie charts • Bar charts • Histogram • Dot plots • Stem-and-leaf displays • Box plots
- 9. Dot plots
- 10. Stem-and –leaf plot • Example: the following figures show the number of customers to a certain pharmacy in a month. • 77 80 82 68 65 59 61 57 50 62 61 70 69 64 67 70 62 65 65 73 76 87 80 82 83 79 79 71 80 77 • Step 1: arrange all values in ascending order • Step 2: decide on your stem and develop a key • Example 82= 8|2 • Construct the stem and leaf as follows
- 11. Stem and leaf plot 5 | 0 5 | 79 6 | 11224 6 | 555789 7 | 0013 7 | 67799 8 | 000223 8 | 7 Here 5|0 equals to 50.