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Bivariate Data - Coggle Diagram
Bivariate Data
Types of Variables
Numerical Variable
A numerical variable is a variable where the measurement or number has a numerical meaning. For example, total rainfall measured in inches is a numerical value, heart rate is a numerical value, number of cheeseburgers consumed in an hour is a numerical value.
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Categorical Variable
In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.
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Continuous Variable
Continuous variables can take on an unlimited number of values between the lowest and highest points of measurement. Continuous variables include such things as speed and distance.
Discrete Variables
What is a Discrete Variable? Discrete variables are countable in a finite amount of time. For example, you can count the change in your pocket. You can count the money in your bank account. You could also count the amount of money in everyone's bank accounts.
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Variables
Response 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. For example, in a plant growth study, the response variable is the amount of growth that occurs during the study.
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Explanatory Variables
An explanatory variable is a type of independent variable. The two terms are often used interchangeably. But there is a subtle difference between the two. When a variable is independent, it is not affected at all by any other variables. When a variable isn’t independent for certain, it’s an explanatory variable.
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Data
Bivariate Data
In statistics, 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. Typically it would be of interest to investigate the possible association between the two variables.
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Multivariate Data
Multivariate data analysis refers to all statistical methods that simultaneously analyze multiple measurements on each individual respondent or object under investigation. Thus, any simultaneous analysis of more than two variables can be considered multivariate analysis.
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Predictions
Extrapolations
Extrapolation is a statistical technique aimed at inferring the unknown from the known. 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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Interpolations
Interpolation is a statistical method by which related known values are used to estimate an unknown price or potential yield of a security. Interpolation is achieved by using other established values that are located in sequence with the unknown value.
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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.
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Causation
Causation indicates a relationship between two events where one event is affected by the other. In statistics, when the value of one event, or variable, increases or decreases as a result of other events, it is said there is causation.
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Regression
Regression is a statistical method used in finance, investing, and other disciplines that attempt to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).
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