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Introduction to Biostatistics
Transcript of Introduction to Biostatistics
statistics has two main definitions:
1. Statistics in singular sense and,
2. statistics in plural sense
Types of Statistics
There are two types of Statistics:
1. Parametric statistics
2. Nonparametric statistics
Branches of Statistics
There are two main branches of statisitcs:
1. Descriptive Statistics
2. Inferential statistics
statistics is a set of numerical characteristics.
it is a branch of science that deals the processes of collecting, organizing, presentation, analysis, and interpretation of data.
Fields of Statistics
The field of statistics is divided into:
this deals with the development of theories that serve as a basis for statistical methods.
this refers to the application of statistical methods to solve real life problems as well as the development of new statistical methods motivated by real problems.
This refers to the methods of summarazing and presenting data in the form which will make them easier to interpret.
This includes the following:
Measures of Central Tendency
Measures of Position/Variability
It refers to the process of drawing and making decision on the population on the evidence obtained from a sample.
It is a statisical approach that assumes random sample from a normal distribution and involves testing of hypothesis about the population mean.
z test/ t test
ANOVA and etc.
It is a statistical approach with no underlying data distribution assumed and involves hypothesis testing about a population median.
Mann-Whitney and etc.
A variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item.
Age, sex, business income and expenses, country of birth, capital expenditure, class grades, eye colour and vehicle type are examples of variables.
Population VS Sample
Parameter VS Statistic
A parameter is a numerical characteristic of a population.
it is a numerical characteristic of a sample.
A population is a group of phenomena that have something in common. The term often refers to a group of people, as in the example:
All basal ganglia cells from a particular rhesus monkey.
A sample is a smaller group of members of a population selected to represent the population.
Types of Variables
Categorical variables have values that describe a 'quality' or 'characteristic' of a data unit, like 'what type' or 'which category'. Categorical variables fall into mutually exclusive and exhaustive categories.
Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value.
Types of Quantitative Variable
Numeric variables have values that describe a measurable quantity as a number, like 'how many' or 'how much'.
Therefore numeric variables are quantitative variables.
A discrete variable is a numeric variable. Observations can take a value based on a count from a set of distinct whole values.
A discrete variable cannot take the value of a fraction between one value and the next closest value.
A continuous variable is a numeric variable. Observations can take any value between a certain set of real numbers.
The value given to an observation for a continuous variable can include values as small as the instrument of measurement allows.
Inferential statistics enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population .
There are four measurement scales for data analysis:
When measuring using a nominal scale, one simply names or categorizes responses.
The essential point about nominal scales is that they do not imply any ordering among the responses.
The items in this scale are ordered, ranging from least to most.
Unlike nominal scales, ordinal scales allow comparisons of the degree to which two subjects possess the dependent variable.
Interval scales are numerical scales in which intervals have the same interpretation throughout
Interval scales are not perfect, however. In particular, they do not have a true zero point.
General Weighted Average
The ratio scale of measurement is the most informative scale.
it is an interval scale with the additional property that its zero position indicates the absence of the quantity being measured