Lab i$1$

Introduction to Data Analysis Using

Python

I Objectives

$$ To explore the theoretical basics of probability distributions, understanding the signifi$-$

cance of bin size selection in histograms and the mathematical formulation of probability

density functions for accurate data representation.

$$ To gain practical insights into statistical analysis by extracting the mean and standard de$-$

viation of normally$-$distributed data to characterize center and spread, and by using slope

and uncertainty obtained from the linear regression of linear data as tools for analyzing

relationships and trends in datasets.

$$ To develop skills in using Python code for various curve$-$fitting techniques to be able to

visualize relationships between variables.

$$ To apply theoretical knowledge of probability distributions, FWHM$,$ standard deviation,

and linear regression to real$-$world scenarios, enhancing problem$-$solving skills in experi$-$

mental data analysis and drawing meaningful conclusions.

II Equipment

$1.$ Computer

III Introduction

When making several measurements of the same physical quantity, random errors will cause

some of your measurements to vary slightly from the true value of the quantity being measured.

To illustrate this concept, we will use data from two physical systems that you are probably

passingly familiar with$$specifically$,$ the period of a simple pendulum and the masses of various

pennies. Unlike the laboratories that will follow this, we are not going to focus on the physics

of these systems but merely use them as tools to explain the origin of some types of error and

how to analyze the data that is collected. We will cover these systems in much greater detail

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