Statistics

School

University of Saskatchewan *

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Course

317

Subject

Computer Science

Date

Feb 20, 2024

Type

Pages

Uploaded by ananddiya80

# CMPT 317: Assignment 2 Question 1 Model Solution # Copyright (c) 2016-2019 Michael C Horsch, # Department of Computer Science, University of Saskatchewan # This file is provided solely for the use of CMPT 317 students. Students are permitted # to use this file for their own studies, and to make copies for their own personal use. # This file should not be posted on any public server, or made available to any party not # enrolled in CMPT 317. # This implementation is provided on an as-is basis, suitable for educational purposes only. # Defines the Statistics ADT (a tool developed for CMPT 145 as an example ADT - code reuse anyon e?) # methods: # add(data): add the given data value to the statistics # mean() return the average data value # var(): return the calculated variance in the data # min(): return the minimum data value # max(): return the maximum data value class Statistics(object): def __init__(self): """ Purpose: Create a Statistics object. """ self.__count = 0 # how many data values have been seen self.__avg = 0 # the running average so far self.__sumsqdiff = 0 # the sum of the square differences self.__min = None self.__max = None def add(self, value): """ Purpose: Use the given value in the calculation of statistics. Pre-Conditions: value: the value to be added Post-Conditions: none Return: none """ self.__count += 1 k = self.__count # convenience diff = value - self.__avg # convenience self.__avg += diff/k self.__sumsqdiff += ((k-1)/k)*(diff**2) if self.__max is None or self.__max < value: self.__max = value

if self.__min is None or self.__min > value: self.__min = value def mean(self): """ Purpose: Return the mean of all the values seen so far. Pre-conditions: (none) Post-conditions: (none) Return: The mean of the data seen so far. Note: if no data has been seen, 0 is returned. This is clearly false. """ return self.__avg def min(self): """ Purpose: Return the minimum of all the values seen so far. Pre-conditions: (none) Post-conditions: (none) Return: The minimum of the data seen so far. Note: if no data has been seen, None is returned. """ return self.__min def max(self): """ Purpose: Return the maximum of all the values seen so far. Pre-conditions: (none) Post-conditions: (none) Return: The maximum of the data seen so far. Note: if no data has been seen, None is returned. """ return self.__max def count(self): """ Purpose: Return the number of all the values seen so far. Pre-conditions: (none) Post-conditions: (none) Return: The number of the data seen so far. Note: if no data has been seen, 0 is returned. """

return self.__count def var(self): """ Purpose: Return the variance of all the values seen so far. (variance is the average of the squared difference between each value and the average of all values) Pre-conditions: (none) Post-conditions: (none) Return: The variance of the data seen so far. Note: if 0 or 1 data values have been seen, 0 is returned. This is clearly false. """ return self.__sumsqdiff/self.__count def sample_var(self): """ Purpose: Return the sample variance of all the values seen so far. Pre-conditions: (none) Post-conditions: (none) Return: The sample variance of the data seen so far. Note: if 0 or 1 data values have been seen, 0 is returned. This is clearly false. """ return self.__sumsqdiff/(self.__count - 1) # eof

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