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Rinku Aiml 3.1

The document outlines an experiment for a Computer Science and Engineering course focused on calculating statistical measures such as Mean, Median, Mode, Variance, and Standard Deviation using Python. It includes the aim, objectives, and sample code for implementing these calculations. Additionally, it provides definitions and learning outcomes for each statistical measure discussed.

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attririnku83
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7 views2 pages

Rinku Aiml 3.1

The document outlines an experiment for a Computer Science and Engineering course focused on calculating statistical measures such as Mean, Median, Mode, Variance, and Standard Deviation using Python. It includes the aim, objectives, and sample code for implementing these calculations. Additionally, it provides definitions and learning outcomes for each statistical measure discussed.

Uploaded by

attririnku83
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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DEPARTMENT OF

COMPUTER SCIENCE & ENGINEERING

Experiment 3.1

Student Name: Rinku Attri UID: 21BCS9862

Branch: CSE Section/Group: NTPP_603-A

Semester: 5th Date of Performance: 27/10/2023

Subject Name: Artificial Intelligence & Machine Subject Code: 21CSH-316

Learning Lab

1. Aim: Write a python program to compute Mean, Median, Mode, Variance and Standard
Deviation using Datasets

2. Objective: The objective of this experiment is to implement program to compute Mean,


Median, Mode, Variance and Standard Deviation using Datasets

3. Program: Mean:

import statistics data = [7, 10, 11, 17, 18, 45] x = statistics.mean(data)
print("Mean of the given dataset
is :", x)

Median:
import statistics
data = [7, 10, 11, 17, 18, 45] x =

statistics.median(data) print("Median of the given

dataset is :", x) Mode:

import statistics data = [7, 10, 11, 17, 18, 45] x = statistics.mode(data)
print("Mode of the given dataset
is :", x)
Variance: import statistics data = [7, 10,
11, 17, 18, 45] x = statistics.variance(data)
print("variance of the given dataset is :", x)

DEPARTMENT OF
COMPUTER SCIENCE & ENGINEERING

Standard Deviation: import statistics data = [7, 10,


11, 17, 18, 45] x = statistics.std(data) print("Mean of
the given dataset is :", x)

4. OUTPUT:

Learning Outcomes: you should first understand what each of these statistical measures

represents. Here are the definitions and learning outcomes for each of these measures:

• The mean is the sum of all values in a dataset divided by the number of values.

• The median is the middle value in a dataset when it is sorted. If there is an even number of
values, it's the average of the two middle values.

• The mode is the value that appears most frequently in a dataset.


• Variance measures how much the values in a dataset deviate from the mean.

• The standard deviation is the square root of the variance.

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