MatLab code that satisfies the following prompt and restrictions while being well commented
thanks! Summary Lets recap what you need to do. I'm only partly sorry for writing a problem
that needs a table of contents and summary. 1. Filter outliers from your data set. 2. Compute the
linear regression on your filtered data set. 3. Compute the R2 value from your filtered data and
your linear regression function. Here is the function specification. Name: linearRegression
Inputs: 1. x-values of the data set 2. y-values of the data set Outputs: 1. Filtered x-values (i.e. the
input x-values but without the outlier points), sorted from smallest to largest 2. Filtered y-valeus
(i.e. the input y-values but without the outlier points), sorted from smallest to largest 3. Slope
from the linear regression ( m in f(x)=mx+b ) 4. Intercept from the linear regression (b in
f(x)=mx+b ) 5. Rsquared value
\begin{tabular}{|l|ll} 1 & function [fx,fY, slope, intercept, Rsquared] = linearRegression (x,y) \\
2 & % inearRegression Computes the linear regression of a data set \\ 3 & % & Compute the
linear regression based on inputs: \\ 4 & % & 1. x:x-values for our data set \\ 5 & % & 2. y:y-
values for our data set \\ 6 & % & \\ 7 & % & Outputs: \\ 8 & % & 1. fX:x-values with outliers
removed \\ 9 & % & 2. fY:y-values with outliers removed \\ 10 & % & 3. slope: slope from the
linear regression y=mx+b \\ 11 & % & 4. intercept: intercept from the linear regression y=mx+b
\\ 12 & % & 5. Rsquared: R2, a.k.a. coefficient of determination \\ 13 & \end{tabular} Code to
call your function?.
MatLab code that satisfies the following prompt and restrictions whi.pdf
1. MatLab code that satisfies the following prompt and restrictions while being well commented
thanks! Summary Lets recap what you need to do. I'm only partly sorry for writing a problem
that needs a table of contents and summary. 1. Filter outliers from your data set. 2. Compute the
linear regression on your filtered data set. 3. Compute the R2 value from your filtered data and
your linear regression function. Here is the function specification. Name: linearRegression
Inputs: 1. x-values of the data set 2. y-values of the data set Outputs: 1. Filtered x-values (i.e. the
input x-values but without the outlier points), sorted from smallest to largest 2. Filtered y-valeus
(i.e. the input y-values but without the outlier points), sorted from smallest to largest 3. Slope
from the linear regression ( m in f(x)=mx+b ) 4. Intercept from the linear regression (b in
f(x)=mx+b ) 5. Rsquared value
begin{tabular}{|l|ll} 1 & function [fx,fY, slope, intercept, Rsquared] = linearRegression (x,y)
2 & % inearRegression Computes the linear regression of a data set 3 & % & Compute the
linear regression based on inputs: 4 & % & 1. x:x-values for our data set 5 & % & 2. y:y-
values for our data set 6 & % & 7 & % & Outputs: 8 & % & 1. fX:x-values with outliers
removed 9 & % & 2. fY:y-values with outliers removed 10 & % & 3. slope: slope from the
linear regression y=mx+b 11 & % & 4. intercept: intercept from the linear regression y=mx+b
12 & % & 5. Rsquared: R2, a.k.a. coefficient of determination 13 & end{tabular} Code to
call your function?
![MatLab code that satisfies the following prompt and restrictions while being well commented
thanks! Summary Lets recap what you need to do. I'm only partly sorry for writing a problem
that needs a table of contents and summary. 1. Filter outliers from your data set. 2. Compute the
linear regression on your filtered data set. 3. Compute the R2 value from your filtered data and
your linear regression function. Here is the function specification. Name: linearRegression
Inputs: 1. x-values of the data set 2. y-values of the data set Outputs: 1. Filtered x-values (i.e. the
input x-values but without the outlier points), sorted from smallest to largest 2. Filtered y-valeus
(i.e. the input y-values but without the outlier points), sorted from smallest to largest 3. Slope
from the linear regression ( m in f(x)=mx+b ) 4. Intercept from the linear regression (b in
f(x)=mx+b ) 5. Rsquared value
begin{tabular}{|l|ll} 1 & function [fx,fY, slope, intercept, Rsquared] = linearRegression (x,y)
2 & % inearRegression Computes the linear regression of a data set 3 & % & Compute the
linear regression based on inputs: 4 & % & 1. x:x-values for our data set 5 & % & 2. y:y-
values for our data set 6 & % & 7 & % & Outputs: 8 & % & 1. fX:x-values with outliers
removed 9 & % & 2. fY:y-values with outliers removed 10 & % & 3. slope: slope from the
linear regression y=mx+b 11 & % & 4. intercept: intercept from the linear regression y=mx+b
12 & % & 5. Rsquared: R2, a.k.a. coefficient of determination 13 & end{tabular} Code to
call your function?](https://image.slidesharecdn.com/matlabcodethatsatisfiesthefollowingpromptandrestrictionswhi-230330210336-988eca57/85/MatLab-code-that-satisfies-the-following-prompt-and-restrictions-whi-pdf-1-320.jpg)
