## Statistical Analysis with Python

Statistical analysis means investigating trends, patterns, and relationships using quantitative data. It is an important research tool used by scientists, governments, businesses, and other organizations. In this lab we are going do lot of analysis on data sets using Python and R.

Pre Requisites:

• The students must have knowledge on the basics of Mathematics Syllabus

Course Objectives:

1. To provide an overview of a new language R used for data science.
2. To familiarize students with how various statistics like mean median etc. can be collected for data exploration in PYTHON
3. To provide a solid undergraduate foundation in both probability theory and mathematical statistics and at the same time provides an indication of the relevance and importance of the theory in solving practical problems in the real world

Experiment 1: Basics of PYTHON Programming

Experiment 2: Decision making, Looping Statement and Functions

1. Write a program to illustrate if-else-else if in PYTHON
2. Write a Program to illustrate While and For loops in PYTHON
3. Write a program to demonstrate working with functions in PYTHON.

Experiment 3: Data Structures in PYTHON

Experiment 4: Packages & Data Reshaping

Experiment 5: Functions in Python

Experiment 6: Text file processing & Basic Statistics

2.Write a program in Python with functions to calculate the following for comma-separated numbers in a text file(.txt)

Notes:- First three bits a,b,c solutions are in a single program

Experiment 7: Exploring the Numpy library for multi-dimensional array processing

1.Develop programs in Python to implement the following in Numpy

a.Array slicing, reshaping, concatenation and splitting
b.Universal functions in Numpy
c.Aggregations
e.Fast sorting
Experiment 8: Data cleaning and processing with Pandas

1.Develop the following programs in Python
a) Implementing and querying the Series data structure
b) Implementing and querying the Data Frame data structure
c) Merge two different Data Frames

Experiment 9: Advanced Data Processing and Transformation-Implement the following using the Pandas library

Experiment 10: Data Interfaces
1. Write a program to demonstrate following operations on the given datasets.
1)  Load data from different files like CSV and Excel PYTHON
2)   Data Description

Experiment 11: Data Visualization-I in PYTHON
Experiment 12: Data Visualization-II in PYTHON
Experiment 13: Probability Distributions
1. Generate and Visualize Discrete and continuous distributions using the statistical environment.
2. Demonstration of normal, binomial and Poisson distributions.
3. Students are expected to generate artificial data using and explore various distribution and its properties. Various parameter changes may be studied.

Experiment 14: Building Confidence in Confidence Intervals

1. Populations Versus Samples
2. Large Sample Confidence Intervals
3. Simulating Data Sets
4. Evaluating the Coverage of Confidence Intervals

Experiment 15: Perform Tests of Hypotheses

1. How to perform tests of hypotheses about the mean when the variance is known.
2. How to compute the p-value.
3. Explore the connection between the critical region, the test statistic, and the p-value

Experiment 16: Regression

1. Write a program to demonstrate line regression in PYTHON for the given dataset by following the below steps.

2.Hypothesis Testing in Linear Regression
3.Building a Linear Model
4.Residual Analysis and Predictions

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