How Do You Relate Fitted Curve to Mean, Standard Deviation, and Standard Error?

[coffeem]

Homework Statement

Given:

clear
n = 100;
x = 1:n;
err = randn(1,n);
mean(err);
std(err);
y = x + err;
cftool

Q - Relate your fitted to the data in y to the mean, S and SE values. You should also compare the fit results to the 95% point on the curve of the integral of the normal distribution.


2. The attempt at a solution

I did the following:

Mean = mean(y)

Standard_Deviation = std(y)

Standard_Error = ((Standard_Deviation).^2)./sqrt(100)


But in all honestly don't know what I have to do. I mean how do I relate the data? I think the problem here is that I do not understand the question. Thanks

 

[coffeem]

hey does anyone have any ideas for this? i am still stuck... thanks

 

[baba89]

I would suggest that you start by understanding the question and the data given. The data provided is a set of 100 points (n=100) with random errors (err) added to the values of x. The mean and standard deviation of these errors have been calculated for you. The y values are then calculated by adding the errors to the original x values.

The cftool command opens up a curve fitting tool in MATLAB, which allows you to fit a curve to your data and obtain various statistical measures of the fit.

To relate the fitted curve to the data in y, you can use the mean, standard deviation, and standard error values that you calculated. These values represent the central tendency and spread of the data. You can compare these values to the parameters of the fitted curve, such as the mean, standard deviation, and standard error of the fitted curve. This will give you an idea of how well the fitted curve represents the data in y.

Additionally, you can compare the fit results to the 95% point on the curve of the integral of the normal distribution. This means that you can compare the fit results to the values that would be expected if the data were normally distributed. This can help you determine if the fitted curve is a good representation of the data.

In summary, to relate the fitted curve to the data in y, you can compare the statistical measures of the data (mean, standard deviation, standard error) to the parameters of the fitted curve and compare the fit results to the expected values from a normal distribution. This will give you an idea of how well the fitted curve represents the data in y.