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What is similar between an inverse relationship and a linear relationship What is different?
decreases
Why was there a need to invent linear equation in two variables?
If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.If two variables are related, then the simplest relationship between them is a linear one. The linear equation expresses such a relationship.
Is the relationship between inches and centimeters linear or non linear?
It is linear.
Is the relationship between x and y always linear?
The relationship between X and Y is linear only if it is of a form: Y=mX+b, or Y=(m/X)+b If they are related in other forms, such as: Y=X2, or Y=nX5+mX3+b, or Y= SQRT(X), or Y= COS (X), or many other relationships, it is not linear. * * * * * Nearly correct. Y=(m/X)+b is not a linear relationship but a [translated] inverse relationship. The curve of the graph is a hyperbola, not a straight line.
Can a best fit line be inverse?
Yes, but not at the level of mathematics you are at. In elementary statistics, the line of best fit (if it exists) is always a straight line representing a linear relationship between two variables. The equation of the line is most often calculated using the least squares method. [This minimises the sum of the squares of the vertical differences between the values "predicted" by the line and those actually recorded. The process always leads to a straight line. However, in more advanced statistics, you will learn about transformations. If the relationship between two variables, X and Y, is an inverse relationship, then the relationship between 1/X and Y is linear and you can fit a linear best fit line to the data set given by 1/X and Y. This can then be used to calculate the best fit inverse curve.
