4. Turn the algorithm into MATLAB statements The final MATLAB program is as follows: Purpose: To perform a least-squares fit of an input data set to a straight line, and print out the resulting slope and intercept values. The input data for this fit comes from a user-specified input data file.
4. Turn the algorithm into MATLAB statements The final MATLAB program is as follows: Purpose: To perform a least-squares fit of an input data set to a straight line, and print out the resulting slope and intercept values. The input data for this fit comes from a user-specified input data file.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
5.14
data points before entering the values. Modify the program so that it reads an
arbitrary number of data values using a while loop, and stops reading input
values when the user presses the Enter key without typing any values. Test your program using the same two data sets that were used in Example 5.6. (Hint: The
input function returns an empty array ([]) if you press “Enter” without supplying any data. You can use function isempty to test for an empty array, and
stop reading data when one is detected.)
Using MATLAB
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| Chapter 5 Loops and Vectorization
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Purpose:
To perform a least-squares fit of an input data set
to a straight line, and print out the resulting slope
and intercept values. The input data for this fit
comes from a user-specified input data file.
Record of revisions:
Date
4. Turn the algorithm into MATLAB statements
The final MATLAB program is as follows:
end
====
01/30/18 S. J. Chapman
Define variables:
ii
n_points
slope
sum_x
sum x2
sum_xy
sum_y
temp
X
x_bar
y
y_bar
y_int
=
--
--
Programmer
==========
--
--
--
-- Sum of all input x values
--
Sum of all input x values squared
Sum of all input x*y yalues
Sum of all input y values
Variable to read user input
Array of x values.
Average x value
Array of y values
Average y value
y-axis intercept of the line.
--
--
Description of change
Loop index
Number in input [x y] points
Slope of the line
disp('This program performs a least-squares fit of an');
disp('input data set to a straight line.');
n_points
input('Enter the number of input [x y points: ');
Read the input data
1:n_points
==‒‒‒‒‒‒
Original code
for ii
temp = input ('Enter [x y] pair: ');
x(ii)
temp (1);
y (ii) = temp (2);](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb2f5b0ad-df1d-48c6-a9a3-5338b8167143%2F43748130-3471-4630-9951-097e4890e5ae%2F32mbilk_processed.png&w=3840&q=75)
Transcribed Image Text:&
응
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| Chapter 5 Loops and Vectorization
%
%
%
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%
%
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%
%
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Purpose:
To perform a least-squares fit of an input data set
to a straight line, and print out the resulting slope
and intercept values. The input data for this fit
comes from a user-specified input data file.
Record of revisions:
Date
4. Turn the algorithm into MATLAB statements
The final MATLAB program is as follows:
end
====
01/30/18 S. J. Chapman
Define variables:
ii
n_points
slope
sum_x
sum x2
sum_xy
sum_y
temp
X
x_bar
y
y_bar
y_int
=
--
--
Programmer
==========
--
--
--
-- Sum of all input x values
--
Sum of all input x values squared
Sum of all input x*y yalues
Sum of all input y values
Variable to read user input
Array of x values.
Average x value
Array of y values
Average y value
y-axis intercept of the line.
--
--
Description of change
Loop index
Number in input [x y] points
Slope of the line
disp('This program performs a least-squares fit of an');
disp('input data set to a straight line.');
n_points
input('Enter the number of input [x y points: ');
Read the input data
1:n_points
==‒‒‒‒‒‒
Original code
for ii
temp = input ('Enter [x y] pair: ');
x(ii)
temp (1);
y (ii) = temp (2);
![Accumulate statistics
sum_x = 0;
sum_y = 0;
sum_x2 = 0;
sum_xy = 0;
for ii = 1:n_points
end
sum_x = sum_x + x(ii);
sum_y = sum_y + y(ii);
sum x2 = sum_x2 + x(ii)^2;
sum_xy = sum_xy + x(ii) * y(ii);
Now calculate the slope and intercept.
x_bar = sum_x / n_points;
y bar = sum_y / n_points;
slope
y_int =
(sum_xy
sum_x * y_bar) / (sum_x2
y_bar slope * x_bar;
Tell user.
disp('Regression coefficients for the least-squares line: ');
= $8.3f\n', slope);
fprintf( Slope (m)
fprintf(
Intercept (b) = 8.3f\n', y_int);
fprintf(' No. of points = %8d\n', n_points);
Create the fitted line.
xmin = min(x);
xmax = max (x);
ymin = slope* xmin+y_int;
ymax = slope xmax+y_int;
sum_x * x_bar);
Plot the data points as blue circles with no
connecting lines.
plot (x, y, 'bo');
hold on;
5.5 Additional Examples | 239
Add a title and legend
title ('\bfLeast-Squares Fit');
Plot a solid red line with no markers
plot ( [xmin xmax], [ymin ymax], 'r-', 'LineWidth', 2);
hold off;
xlabel('\bf\itx');
ylabel('\bf\ity');
legend (Input data', 'Fitted line');
grid on](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb2f5b0ad-df1d-48c6-a9a3-5338b8167143%2F43748130-3471-4630-9951-097e4890e5ae%2Fn9301f7_processed.png&w=3840&q=75)
Transcribed Image Text:Accumulate statistics
sum_x = 0;
sum_y = 0;
sum_x2 = 0;
sum_xy = 0;
for ii = 1:n_points
end
sum_x = sum_x + x(ii);
sum_y = sum_y + y(ii);
sum x2 = sum_x2 + x(ii)^2;
sum_xy = sum_xy + x(ii) * y(ii);
Now calculate the slope and intercept.
x_bar = sum_x / n_points;
y bar = sum_y / n_points;
slope
y_int =
(sum_xy
sum_x * y_bar) / (sum_x2
y_bar slope * x_bar;
Tell user.
disp('Regression coefficients for the least-squares line: ');
= $8.3f\n', slope);
fprintf( Slope (m)
fprintf(
Intercept (b) = 8.3f\n', y_int);
fprintf(' No. of points = %8d\n', n_points);
Create the fitted line.
xmin = min(x);
xmax = max (x);
ymin = slope* xmin+y_int;
ymax = slope xmax+y_int;
sum_x * x_bar);
Plot the data points as blue circles with no
connecting lines.
plot (x, y, 'bo');
hold on;
5.5 Additional Examples | 239
Add a title and legend
title ('\bfLeast-Squares Fit');
Plot a solid red line with no markers
plot ( [xmin xmax], [ymin ymax], 'r-', 'LineWidth', 2);
hold off;
xlabel('\bf\itx');
ylabel('\bf\ity');
legend (Input data', 'Fitted line');
grid on
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