Determining Linearity in First-Order Differential Equations

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In summary, the conversation was about a first-order differential equation and determining whether it is linear in the indicated dependent variable. The answer to the question is that it is non-linear when y is dependent and linear when x is dependent. The person is confused about the meaning of 'dy' and 'dx' and is seeking help on a forum. A hint was provided for solving the problem.
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NJJ289
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So I just started taking an intro diff eq course and here's one of my homework problems:

"Determine whether the given first-order diff eq is linear in the indicated dependent variable."

(y2-1)dx + xdy=0; in y; in x

I got the whole bit about the general form for linearity but I was thrown off by having just a 'dx' and 'dy' instead of the more familiar 'dy/dx'

(answer to the question is non linear when y is dependant, linear when x is dependant)

I'm confused as to what exactly 'dy' or 'dx' means, both conceptually and mathematically. I have a feeling there's a nice thread on this somewhere...

thanks for the help!
 
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Please note forum policy on homework.

https://www.physicsforums.com/showthread.php?t=88061

Having said that here is a hint.

[tex]\begin{array}{l}
\left( {{y^2} - 1} \right)dx = - xdy \\
\left( {1 - {y^2}} \right)dx = xdy \\
\frac{{dy}}{{dx}} = \frac{{\left( {1 - {y^2}} \right)}}{x} \\
\frac{{dx}}{{dy}} = \frac{x}{{\left( {1 - {y^2}} \right)}} \\
\end{array}[/tex]

Can you see which has x as the dependent variable and which has y?
 

What is linearity and why is it important in scientific research?

Linearity is the relationship between two variables that can be represented by a straight line on a graph. It is important in scientific research because it allows us to make accurate predictions and draw conclusions about how changing one variable affects the other.

How is linearity determined in experiments?

Linearity can be determined by plotting the data points on a graph and assessing whether they fall along a straight line. The closer the data points are to the line, the more linear the relationship between the variables.

What are some common methods for testing linearity?

Straight line fit, correlation coefficient, and residual analysis are some common methods for testing linearity. These methods involve plotting the data, calculating the correlation between the variables, and analyzing the difference between the observed data points and the predicted values.

What factors can affect linearity in experiments?

The presence of outliers, non-linear relationships between the variables, and measurement error can all affect linearity in experiments. It is important to carefully design experiments and analyze data to minimize these factors and ensure accurate results.

How can non-linearity be addressed in data analysis?

If a non-linear relationship is present between the variables, it may be necessary to use transformation techniques such as logarithmic or power transformations to achieve linearity. Alternatively, non-parametric tests can be used to analyze the data without assuming a linear relationship between the variables.

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