i need some real life examples for two variable data.
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No correlation. Answer provided by
When the data on the graph is continuous,it does make sense to connect the points on the graph of 2 related variables.
An equation with only one variable has only one letter used in it, and that letter is usually an "x" An equation having two variables will have two different letters representing them, usually the letters "x" and "y" The first type could be the equation 5x^3 - 3x^2 + 6x - 50 = 0 The second type could be (x +y)^2 - 7x^3 + 12x = 58.8 1 equations with only 1 variable are usually much easier to solve than an equation with 2 variables, and you cannot solve the latter unless you have two separate equations containing the two variables.
An equation with more than one variable is called a multivariable equation or a multivariate equation. These equations involve two or more variables, allowing for a more complex representation of relationships between different quantities. Common examples include linear equations in two variables, such as (y = mx + b), and polynomial equations involving multiple variables.
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Bivariate
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algebraic
It is a term involving two variables, x and y.
An equation with two or more variables is called a polynomial. It can also be a literal equation.
a set of data that is made up of two paired variables
In statistics, bivariate data refers to data that comes with two variables.
Data with two variables is commonly referred to as bivariate data. This type of data allows for the analysis of the relationship between the two variables, which can be represented through various statistical methods, including scatter plots and correlation coefficients. Bivariate analysis helps identify patterns, trends, and potential causal relationships between the variables.
Correlation
A diagram that shows how two variables are related is called a "scatter plot." It is a visual representation of the relationship between the two variables, often used to identify patterns or trends in the data.
The intersection of the two lines of best fit in a data set indicates the point where the predicted values of the variables are equal. This suggests that there is a common value or relationship between the variables at that specific point.