The problems below are designed to introduce work with MATLAB and to do some bas

Question

The problems below are designed to introduce work with MATLAB and to do some basic data manipulation and simple programming. The data in the file “ppt.dat” are precipitation in mm for the Southern Piedmont of the U.S. (Karl et al. 1994). The rows represent years from 1960 through 1989, and the columns represent months from January to December.

Solve the following five problems and turn in all MATLAB files and needed figures.

Additional requirement:

(1) Plot the results of Problems 2 – 3 in another figure. (tip: use “figure” to open new figure window)

(2) Comment on each line of the M-file of problem 3

1. Read the data into MATLAB.

2. Write an m-file program to: (a) query for a year (see the input command); (b) for the selected year, calculate and display the minimum, maximum, and mean monthy precipitation (see the min and max commands); (c) make a stem plot of the data (see the stem command), labelling the axes and placing an appropriate title (see the xlabel, ylabel and title commands).

3. Write a function file that accepts an N × M matrix (such as the precipitation file) as an argument, finds the maximum correlation coefficient (absolute value) between any two columns of the data, reports that value as output, and presents a scatter plot of the two columns of data with maximal correlation. Apply the code to the precipitation file and briefly comment on any interpretation of the result. (You may want to use corrcoef and eye.)

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 133.8 157.6 120.9 84.5 87.5 71.3 130.0 112.7 100.1 55.2 28.0 48.1 70.4 153.6 115.7 136.7 91.7 143.1 96.4 159.4 35.6 44.8 54.2 127.1 139.2 100.0 124.7 94.4 63.3 136.0 96.4 77.0 121.5 35.6 130.1 73.1 92.0 78.5 127.1 69.3 80.2 106.5 87.5 53.9 114.0 4.7 135.7 70.3 142.7 125.5 129.5 118.6 51.9 90.1 170.1 166.0 84.4 178.4 49.6 111.8 48.9 104.7 159.1 81.7 46.6 157.4 151.6 89.5 68.2 64.3 44.1 15.8 130.5 109.0 69.8 67.7 116.2 75.6 77.5 94.7 130.9 74.9 40.1 78.5 62.5 86.9 67.3 55.3 127.3 73.8 115.7 176.8 64.2 40.4 68.0 123.0 111.9 19.4 79.4 68.4 107.9 125.4 138.9 69.0 34.3 92.6 107.1 69.3 64.8 84.8 103.6 112.5 84.8 107.2 120.5 123.6 105.2 37.6 37.1 114.4 58.5 79.4 121.5 66.6 75.9 67.9 136.5 140.7 50.1 125.8 63.6 75.3 100.1 116.5 133.8 73.8 153.0 87.5 147.7 137.7 108.9 171.9 69.5 43.7 107.8 108.1 75.3 58.0 145.9 181.2 117.4 86.1 84.0 89.2 125.7 140.2 97.9 114.1 144.2 126.6 109.0 172.1 85.7 101.5 72.9 60.5 27.9 146.6 122.4 90.2 78.6 66.4 126.9 91.1 91.4 139.6 121.0 14.4 61.5 132.5 137.9 108.9 185.7 66.0 141.0 90.3 213.7 71.7 182.5 58.7 71.6 94.5 84.0 36.5 98.0 24.2 146.3 144.2 85.4 66.2 116.3 184.6 77.2 107.6 68.3 31.9 145.1 52.5 60.7 84.0 54.8 114.9 111.8 134.6 103.4 89.7 199.0 22.2 111.0 101.5 116.5 95.6 140.7 116.0 54.9 28.9 75.8 76.3 148.8 157.4 84.4 134.4 116.3 131.2 95.9 93.8 208.8 77.9 109.4 35.6 120.7 42.6 211.3 68.3 86.3 80.3 82.5 53.5 128.2 88.4 72.6 29.9 17.1 98.0 58.5 45.2 75.9 102.4 146.0 123.2 72.5 82.8 24.0 139.6 120.3 131.1 57.3 114.4 93.7 170.0 118.4 93.9 67.0 88.2 74.0 106.4 68.4 126.5 176.7 147.2 81.6 79.2 51.5 79.7 77.2 98.0 119.4 172.8 95.1 137.5 148.7 119.5 142.1 62.9 207.8 113.0 19.7 57.0 52.4 49.2 92.4 124.4 26.5 28.9 104.3 85.5 158.1 166.1 17.3 115.3 213.2 31.9 33.8 50.3 67.4 28.7 67.8 36.4 83.5 192.1 35.7 85.7 118.2 101.6 153.5 98.3 117.9 107.2 56.3 116.3 73.7 75.8 193.0 32.1 98.2 70.5 85.7 44.5 60.7 71.7 86.7 73.2 102.7 109.1 109.3 74.1 96.7 22.2 53.6 112.3 131.4 102.9 135.7 172.8 178.8 99.6 131.1 121.9 71.1 80.3

Explanation / Answer

The table of data is stored in a file

The file is being read into Matlab

ab1 = addsample(ab, c)

ab = ab.addsample(‘Time’, 5, ‘Data’, 637);

% The command readtable wil create a table in matlab based on the data from a file

% The syntax will be:

% Tabl1 = readtable(nameOfTheFile, Name Value Pair)

Table1 = readtable(‘ppt.dat’)

% to get the rows from r1 to r2 and the columns from c2 to c2, use

% Table19r1:r2, c1:c2)

% The syntax for the figure would be:

% figure (‘Name of the property’, Value of the property, etc,…..)

ScreenSize1 = get(groot, ‘ScreenSize’);

figure(‘Position’,[1 ScreenSize1(6)/3 ScreenSize1(6)/4 ScreenSize1(10)/5])

% make this as the current and active figure

figure(ScreenSize1);

set(groot, ‘CurrentFigure’, ScreenSize1);

% now to the correlation co efficient:

% Relation1 = corrcoef(A,B)

P = randn(360,1);

% there are 12 months and 30 years hence 30 * 12 = 360 cells of data

Q = randn(360,1);

R = [P Q 360*q+12];

S = corrcoef(R)

% regarding the identity matrix by eye:

% the syntax varies like

% I = eye(sizeNameOfTheClass)

% I = eye(5) will create an identity matrix of 5 rows by 5 columns

our result will be plotted into an identity matrix of 19 rows and 19 columns

I = eye(19)

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