Introduction to Statistics and Python
- Length: 199 pages
- Edition: 1
- Language: English
- Publication Date: 2021-10-06
- ISBN-10: B09HZH3S9M
- Sales Rank: #0 (See Top 100 Books)
This book is part of a series that includes MBA Core & Elective coursework taught at prestigious universities like Harvard and Wharton. The series consists of Core & Elective courses that stemmed from more than ten years of professional experience in Wall Street and Startups. The elective courses introduce Machine Learning, Python, Blockchain and Cryptocurrencies, Communications skills, R language, Excel advanced features, PowerPoint advanced features, interview questions, and more
List of Free Kindle eBooks: Preface Introduction to Statistics 1.1.What is Statistics? 1.2.Statistical Terms 1.2.1.Parameter 1.2.2.Variable Example 1.1 Solution Descriptive and Inferential Statistics 2.1.Descriptive vs. Inferential 2.1.1.Descriptive Statistics 2.2.Measures of Central Tendency (Mean, Median, Mode) 2.2.1.Mean 2.2.2.Median 2.2.3.Mode 2.3.Variability (Range, variance, Standard deviation) 2.3.1.Range 2.3.2.Variance 2.3.3.Standard Deviation Frequency Distribution, Graphs and Displays 3.1.Frequency and Class Limit Cumulative Frequency 3.2.Histogram 3.3.Histogram Showing Means, Medians, Modes 3.4.Ogive 3.5.Stem and Leaf Displays 3.5.1.Stem and Leaf 3.6.Box and Whisker Displays & Five Number Summaries 3.6.1.Five Number Summary 3.6.2.The Box and Whisker Display Elements of Probability and Random Variables 4.1.Probability 4.1.1.Complementary Event 4.1.2.Mutually Exclusive Events 4.1.3.Independent events 4.1.4.Factorial 4.1.5.Combination 4.2.Random Variables 4.2.1.Random Variables 4.2.2.Expected Value of a Random Variable 4.2.3.Variance of a Discrete Random Variable 4.2.4.Standard Deviation of a Discrete Random Variable Probability Distributions 5.1.Binomial Distributions 5.1.1.Binomial Distribution formula 5.2.Poisson Distributions 5.3.Normal Distributions 5.3.1.Table of Normal Curve Areas 5.4.Working Backwards The Population Mean 6.1.The Distributions of Sample Means 6.1.1.Central Limit Theorem 6.2.Confidence Interval Estimates 6.2.1.Assumptions of the model 6.3.Choosing a Sample Size 6.4.The Hypothesis Test 6.4.1.Null Hypothesis 6.4.2.Alternative Hypothesis 6.4.3.Steps in Conducting a Hypothesis Test for μ 6.5.Types of Errors in Testing a Hypothesis 6.5.1.Type I error 6.5.2.Type II error 6.6.More on Errors 6.6.1.Type I Errors and α-Risks 6.6.2.p-Value 6.6.3.Type II Errors and β-Risks 6.7.Comparing Two Means 6.7.1.Confidence Interval Estimates Real World Data Set in Statistical Learning 7.1.Statistical Learning 7.2.Wage Data 7.3.Stock Market Data 7.4.Gene Expression Data Supervised and Unsupervised Learning 8.1.Supervised Versus Unsupervised Learning 8.1.1.Supervised Learning 8.1.2.Unsupervised Learning 8.2.Regression Versus Classification Problems Introduction to R Lab 9.1.Basic Commands 9.2.Graphics 9.3.Indexing Data 9.4.Loading Data 9.5.Additional Graphical and Numerical Summaries References List of Free Kindle eBooks: Preface INTRODUCTION TO PYTHON AND PYTHON IDLE 1.1.PYTHON AND PYTHON IDLE 1.1.1 Interactive Mode 1.1.2. Script Mode 1.2.VARIABLES AND TYPES 1.3.KEYWORDS 1.4.OPERATORS AND OPERANDS 1.4.1.Mathematical/Arithmetic Operators 1.4.2.Relational Operators 1.4.3.Logical Operators 1.4.4.Assignment Operators 1.5.EXPRESSION AND STATEMENTS 1.5.1.Precedence 1.5.2.Operations on Strings DECISION MAKING AND LOOPING 2.1.DECISION MAKING STATEMENTS 2.1.1.“If” Statement 2.1.2.“If…else” Statement 2.1.3.“Elif” Statement 2.2.LOOPING STATEMENTS 2.2.1.“While” Loop 2.2.2.“For” Loop 2.2.3.Break Statement 2.2.4.Continue Statement 2.2.5.Nested Loop STRINGS, LIST, TUPLE AND DICTIONARY 3.1.STRINGS 3.1.1.Length of string 3.2.String Comparison 3.2.1.String Traversal 3.2.2.String traversal and counting 3.2.3.String Slices 3.3.List 3.3.1.Accessing List Elements 3.3.2.List Traversal 3.3.3.Length of a list 3.3.4.Operations on a List 3.3.5.List Membership 3.3.6.List Slices 3.3.7.Are Lists Mutable? 3.3.8.Clone List 3.3.9.List Aliasing 3.3.10.Nested List 3.3.11.Deleting a List 3.4.TUPLES 3.4.1.Selecting Elements of Tuple 3.4.2.Slice of a Tuple 3.4.3.Length of a Tuple 3.4.4.Tuple Assignment 3.5.DICTIONARIES 3.5.1.Dictionary Operations FUNCTIONS 4.1.FUNCTIONS 4.1.1.Functions in Module 4.1.2.Built-in Functions 4.1.3.Composition 4.1.4.User Defined Functions 4.1.5.Scope of Variables 4.1.6.Default arguments 4.1.7.Recursions 4.1.8.Type Conversion FILES AND EXCEPTIONS 5.1.FILE OPENING AND WRITING 5.2.READING AND DISPLAYING FILE CONTENTS 5.3.PICKLING 5.4.EXCEPTIONS CLASSES & OBJECTS 6.1.CLASSES 6.1.1.Passing Objects as Arguments in Function 6.1.2.Shallow Equality and Deep Equality 6.1.3.Objects as Return Values
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