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Curve Fitting:
The data is fundamentally either discrete or continuous. In most of the applications, data is sampled, for illustration,
This gives data of the form of (x,y) points, which then could be plotted. For illustration, let's say the temperature was recorded every hour one afternoon from 2 to 6 p.m.; the vectors may be:
>> x = 2:6;
>> y = [65 67 72 71 63];
and then the plot may appear like figure as shown below:
Example of Mode When no value appears more often than any other, the smallest value in the vector will be the mode of the vector. >> shortx = [2 5 1 4]; >> mode(shortx
Illustration of Nested loops: Running the script shows the output: >> printstars ***** ***** ***** The variable rows identifies the number of rows to print, and
Sequential Search: A sequential search is completed by looping through the vector element-by-element starting from the beginning, looking for the key. Usually the index of the
Persistent Variables: Generally, whenever a function stops executing, then the local variables from that function are cleared. That means that each and every time a function i
Representing Logical true and False: It has been stated that expressions which are theoretically true really have the integer value of 1, and expressions which are theoretical
Execution of persistent variables: The functions can also be called from the script or from the Command Window, as shown here. For illustration, the functions are called first
Nested Functions: We have seen that the loops can be nested, that means that one inside of the other, functions can be nested. The terminology for nested functions is that an
Combining Nested FOR Loops and the IF Statements: The statements inside a nested loop can be any of the valid statement, involving any selection statement. For e.g., there can
Rectangle - graphics objects: The other core graphics object is the rectangle that can have curvature added to it (!!). Merely calling the function rectangle without any argum
Introduction and Purpose In the lectures and tutorials you have been introduced to a number of probability distributions. You have also learned that modelling any system (such
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