Question

Obtain a temperature data of Reno from September 2016. Find the data for a typical sunny day, between 6am to 6pm. Write a MATLAB code that uses linear least squares to fit the data. Then, obtain the same temperature data from the same day in year 2017. Plot the data points and the regression line on the same graph. Does the least-squares line fit the data from 2017? Why or why not? Discuss your result.

MATLAB code please

Answer 1

% day is 26 september
time_2016 = (555:100:1755)' ; % where 555 is 5:55 am and 1755 is 5:55 pm
temp_2016 = [9 11 11 14 17 22 25 27 28 29 31 30 29]' ;
temp_2017 = [6 6 7 10 14 17 18 19 21 21 22 22 21]' ;
len = length(time_2016) ;
X = [ones(len,1) time_2016];
b = X\temp_2016 ;
regression = X*b ;
plot(time_2016, regression,'-') ;
hold on ;
scatter(time_2016,temp_2016,'r') ;
scatter(time_2016,temp_2017,'o') ;
legend('Least Square', 'data 2016', 'data 2017','Location', 'best') ;
xlabel('time (600 is 6 am and 1800 is 6 pm)') ;
ylabel('Temperature (Celcius)') ;

ooooo Temperature (Celcius) ooo oo o o Least Square data 2016 data 2017 400 600 800 1000 1200 1400 time (600 is 6 am and 1800 is 6 pm) 1600 1800