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Bivariate Data - Coggle Diagram
Bivariate Data
Multivariate data
Multivariate analysis is a set of statistical techniques used for analysis of data that contain more than one variable.
An example for this: A doctor has collected data on cholesterol, blood pressure, and weight. She also collected data on the eating habits of the subjects
When the data involves three or more variables, it is categorized under multivariate.
Bivariate Data
Bivariate data are pairs of data values, with two pieces of data from same subject i.e. a person's height and arm-spam
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Each pair of values is plotted as a point on scatter plot
Categorical Variables
categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values
Examples of categorical variables are race, sex, age group, and educational level.
A categorical variable is a category or type. For example, hair color. A categorical variable can be expressed as a number for the purpose of statistics, but these numbers do not have the same meaning as a numerical value .
Discrete Variables
For example, the variable number of boreal owl eggs in a nest is a discrete random variable. Shoe size is also a discrete random variable.
A discrete variable is a kind of statistics variable that can only take on discrete specific values, the variable is not continuous
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Continuous Variables
A continuous variable can be numeric or date/time. For example, the length of a part or the date and time a payment is received.
Continuous variables are also considered metric or quantitative variables, However, height is considered a continuous variable.
Continuous variables can take on an unlimited number of values between the lowest and highest points of measurement
Response Variables
A responding variable is something that “responds” to changes you make in an experiment. It's the effect or outcome in an experiment
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The amount of candy you collected in your zombie costume is known in statistics as the response variable. In our example, the variable was how much Halloween candy you collected.
Correlation
Correlation is a statistical technique that can show whether and how strongly pairs of variables are related
For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect.
Correlation is a statistical measure that expresses the extent to which two variables are linearly related
Causation
does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Causation indicates that one event is the result of the occurrence of the other event
For an example, the change of Candy color change production affects the color variables in a package.
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Extrapolation
It attempts to predict future data by relying on historical data, such as estimating the size of a population a few years in the future on the basis of the current population size and its rate of growth.
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extrapolation is a type of estimation, beyond the original observation range
Interpolation
In this example, a straight line passes through two points of known value.
The interpolated value of the middle point could be 9.5.
Interpolation is achieved by using other established values that are located in sequence with the unknown value.
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