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Bivariate Data - Coggle Diagram
Bivariate Data
Multivariate Data :
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Multivariate analysis is a set of statistical techniques used for analysis of data that contain more than one variable
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Bivariate Data
Bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable
It involves the analysis of two variables for the purpose of determining the empirical relationship between them.
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Such a plot permits you to see at a glance the degree and pattern of relationships between the two variables
Categorical Variables
Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational leve
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Categorical data analysis is the analysis of data where the response variable has been grouped into a set of mutually exclusive ordered (such as age group) or unordered (such as eye color) categories.
Discrete Variables
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Bar charts are a standard way to graph discrete variables. Each bar represents a distinct value, and the height represents its proportion in the entire sample.
Continuous Variables
Continuous variables can take on an unlimited number of values between the lowest and highest points of measurement.
Compare the means of two (or more) data sets to determine whether the data sets differ significantly from one another.
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Histograms are used to graph continuous variables as they show the distribution of the values. (When you have continuous variables that are divided into groups, you can use a boxplot to display the central tendency and spread of each group)
Response Variables
The response variable is the subject of change within an experiment, often as a result of differences in the explanatory variables.
Response variables are also known as dependent variables, y-variables, and outcome variables. Typically, you want to determine whether changes in the predictors are associated with changes in the response
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Responding variable is the variable that will change as a result of the change in the manipulated variable. It is observed and measured to determine the quantity or quality of change.
Correlation
Correlation is a technique for investigating the relationship between two quantitative, continuous variables, for example, age and blood pressure. ...
The nearer the scatter of points is to a straight line, the higher the strength of association between the variables.
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Correlation analysis is a statistical method used to evaluate the strength of relationship between two quantitative variables. A high correlation means that two or more variables have a strong relationship with each other, while a weak correlation means that the variables are hardly related
Causation
Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. ... Such analysis usually involves one or more artificial or natural experiments.
The causal graph can be drawn in the following way. Each variable in the model has a corresponding vertex or node and an arrow is drawn from a variable X to a variable Y whenever Y is judged to respond to changes in X when all other variables are being held constant.
Extrapolation
Extrapolation is a prediction that is made from outside (extra) the data points collected and represented on the graph.
Extrapolation is the process of taking data values at points x1, ..., xn, and approximating a value outside the range of the given points.
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Interpolation
Interpolation is a prediction that is made between the data points (inter, like interstate, between or among states)
Interpolation is the process of using known data values to estimate unknown data values. Various interpolation techniques are often used in the atmospheric sciences.
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