CME 281 Computational Methods For All Parts The Spreadsheet

Cme 281 Computational Methods1for All Parts The Spreadsheet Is Onl

The assignment involves developing a series of programming tasks centered around creating, manipulating, and analyzing arrays in a spreadsheet environment. The tasks include generating random data, performing calculations via functions and sub-procedures, and outputting results to the spreadsheet. The key constraints emphasize using only the arrays created during the task, restricting the use of additional arrays or module-level variables, and ensuring that all operations are based on array data with output limited to the spreadsheet.

Paper For Above instruction

Computational methods for data analysis and processing are essential skills in many technical fields, especially when working with spreadsheet environments. This assignment explores array manipulation, function utilization, and sub-procedure implementation through two main parts, each with its specific tasks and constraints.

Part 1 revolves around creating and processing a one-dimensional array of 12 elements. First, the programmer must generate a dynamic array of size 12, then populate it with random numbers within the range of [-3, 17]. This task emphasizes understanding array initialization and the significance of dynamic sizing in programming. Once filled, the array's contents are to be outputted to the spreadsheet, serving as the primary interface between program logic and the user.

The second step involves passing this array to a dedicated function that calculates its average. The function receives the array as an argument and must compute the average exclusively from array data, employing only local variables. The function cannot reference external data structures or spreadsheet cells directly. After computing, it returns the average to the main program, which then outputs this value to the spreadsheet.

Subsequently, Part 1 requires filtering the original array based on the calculated average. The array should be reduced to only those elements that lie within ± 4 of the average value. The filtering process must be performed solely on the original array, with no auxiliary arrays used during manipulation, and the resulting array must be resized to only contain the retained values. This process demonstrates understanding of conditional filtering and dynamic array resizing. The reduced array is then outputted to the spreadsheet, maintaining only the relevant data.

The final step in Part 1 involves recalculating the average but now only for the filtered array, again utilizing the same function. The output of this second average calculation should be displayed on the spreadsheet.

Part 2 shifts focus to creating and manipulating a two-dimensional array. First, a 15-element array is populated with random numbers within [1, 15], then output to the spreadsheet. Subsequently, this array is transferred into a multidimensional array with a defined shape that matches an example given in the assignment, with rows and columns appropriately structured. The contents are then outputted to the spreadsheet, illustrating the transition from one-dimensional to two-dimensional data structures.

The next task involves passing this two-dimensional array to a sub-procedure designed to count how many values exceed 9. The sub-procedure performs this count purely within the array data and outputs the count to the main procedure. The main procedure then writes this count to cell E8 on the spreadsheet, exemplifying modular programming without the use of global variables or module-level data.

Throughout, the assignment emphasizes strict adherence to constraints. Array manipulations must be performed only on the arrays created initially, with all results outputted to the spreadsheet for user review. No auxiliary arrays or global variables are permitted, and calculations should rely solely on array data, reinforcing best practices in data encapsulation and modular programming.

In conclusion, this assignment offers practical experience in array creation, dynamic resizing, data filtering, function-based calculations, and sub-procedure interactions within a spreadsheet programming context. Mastering these tasks enhances one's ability to perform complex data analysis efficiently and accurately while following structured programming principles.

References

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