There are two cases to consider. The first is one in which you have a table which is generated using a linear equation and you merely want to reproduce the linear equation.Select any two distinct points, each of which will be represented by an ordered pair.
Suppose the pairs are (p, q) and (r, s).
Then the gradient of the line is (q - s)/(p - r).
Then using the point-and-gradient form of the equation:
y - s = [(q - s)/(p - r)]*(x - r)
Then simplify and rearrange to the required form.
The second case is where the table is based on observations for two variables which are linearly related. However, due to random variations or measurement errors (or rounding), the scatter plot for the data is nearly - but not quite a straight line. You will then need to use statistical techniques to obtain the equation. The best known is the method of least squares. However, this site does not support the mathematical symbols to illustrate the procedure.
by figuring out the equation
Because it fits the data. That's an extremely vague answer, but it was an extremely vague question.
Linear interpolation is used as a method used in mathematics of constructing a curve that has the best fit to a series of points of data using linear polynomials.
A linear equation is an equation in the format y=mx+b, with y being the y-value in a data set, x being the x-value in a data set, m being the constant rate of change(also known as slope, which can be found on a graph by using rise/run, and can be found on a table as the change in y/the change in x) and b is the y-intercept(the value of y when x is 0 aka the starting point). All linear equations appear as a straight line on a graph.
What you are asking is not precisely clear, but in general missing data is filled in by a process of interpolation. eg. Linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.
by figuring out the equation
Because it fits the data. That's an extremely vague answer, but it was an extremely vague question.
If the data have a positive or negative correlation, it means the data have a linear relationship in the form of an equation of a line; or Y = mX + b.
Linear interpolation is used as a method used in mathematics of constructing a curve that has the best fit to a series of points of data using linear polynomials.
A linear equation is an equation in the format y=mx+b, with y being the y-value in a data set, x being the x-value in a data set, m being the constant rate of change(also known as slope, which can be found on a graph by using rise/run, and can be found on a table as the change in y/the change in x) and b is the y-intercept(the value of y when x is 0 aka the starting point). All linear equations appear as a straight line on a graph.
It depends on which calculator! If the data is linear, you can estimate the slope of the line and the y-intercept from graphing the data. By graphing the data, you will be able to tell if it forms a straight line or not.
A tree is an example for a non-linear data structure.
Linear data structures are 1-dimensional arrays, as in: vectors.
Lookup time for an element in a link list is equal to number of elements in list. Hence linear, like a linear equation: t = n. Compare that to lookup in a tree which is logarithmic: t = log2 n.
java
A trend equation is a regression equation that models the relationship between a variable and time. It is used to identify and forecast trends in data over time, helping to predict future values based on historical patterns. Trend equations can be linear or nonlinear, depending on the nature of the data being analyzed.
What you are asking is not precisely clear, but in general missing data is filled in by a process of interpolation. eg. Linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete set of known data points.