## Essential Statistics for Data Analysis

### What you’ll learn

Learn powerful statistics tools and techniques for data analysis & business intelligence

Understand how to apply foundational statistics concepts like the central limit theorem and empirical rule

Explore data with descriptive statistics, including probability distributions and measures of variability & central tendency

Model data and make estimates using probability distributions and confidence intervals

Make data-driven decisions and draw conclusions with hypothesis testing

Use linear regression models to explore variable relationships and make predictions

### Requirements

No math or stats background is required – we’ll start with the absolute basics!

We’ll use Microsoft Excel (Office 365) for course projects and demos

### Description

This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence. Our goal is to simplify and demystify the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques – even if you have absolutely no background in math or stats!We’ll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions.Next we’ll explore our data using descriptive statistics and probability distributions, introduce the normal distribution and empirical rule, and learn how to apply the central limit theorem to make inferences about populations of any type.From there we’ll practice making estimates with confidence intervals, and using hypothesis tests to evaluate assumptions about unknown population parameters. We’ll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs. type II errors, and more.Last but not least, we’ll introduce the fundamentals of regression analysis, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions using Excel’s Analysis Toolpak.Throughout the course, you’ll play the role of a Recruitment Analyst for Maven Business School. Your goal is to use the statistical techniques you’ve learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes.You’ll also practice applying your skills to 5 real-world BONUS PROJECTS, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety teams, airlines, and more.COURSE OUTLINE:Why Statistics?Discuss the role of statistics in the context of business intelligence and decision-making, and introduce the statistics workflowUnderstanding Data with Descriptive StatisticsUnderstand data using descriptive statistics, including frequency distributions and measures of central tendency & variabilityPROJECT #1: Maven Pizza ParlorModeling Data with Probability DistributionsModel data with probability distributions, and use the normal distribution to calculate probabilities and make value estimatesPROJECT #2: Maven Medical CenterThe Central Limit TheoremIntroduce the Central Limit Theorem, which leverages the normal distribution to make inferences on populations with any distributionMaking Estimates with Confidence IntervalsMake estimates with confidence intervals, which use sample statistics to define a range where an unknown population parameter likely liesPROJECT #3: Maven PharmaDrawing Conclusions with Hypothesis TestsDraw conclusions with hypothesis tests, which let you evaluate assumptions about population parameters using sample statisticsPROJECT #4: Maven Safety CouncilMaking Predictions with Regression AnalysisMake predictions with regression analysis, and estimate the values of a dependent variable via its relationship with independent variablesPROJECT #5: Maven AirlinesJoin today and get immediate, lifetime access to the following:7.5 hours of high-quality videoStatistics for Data Analysis PDF ebook (150+ pages)Downloadable Excel project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you’re an analyst, data scientist, business intelligence professional, or anyone looking to use statistics to make smart, data-driven decisions, this course is for you!Happy learning!-Enrique Ruiz (Lead Statistics & Excel Instructor, Maven Analytics)

### Overview

Section 1: Getting Started

Lecture 1 Course Structure & Outline

Lecture 2 READ ME: Important Notes for New Students

Lecture 3 DOWNLOAD: Course Resources

Lecture 4 Setting Expectations

Lecture 5 The Course Project

Lecture 6 Helpful Resources

Section 2: Why Statistics?

Lecture 7 Section Intro

Lecture 8 Why Statistics?

Lecture 9 Populations & Samples

Lecture 10 The Statistics Workflow

Section 3: Understanding Data with Descriptive Statistics

Lecture 11 Section Intro

Lecture 12 Descriptive Statistics Basics

Lecture 13 Types of Variables

Lecture 14 Types of Descriptive Statistics

Lecture 15 Categorical Frequency Distributions

Lecture 16 Numerical Frequency Distributions

Lecture 17 Histograms

Lecture 18 ASSIGNMENT: Frequency Distributions

Lecture 19 SOLUTION: Frequency Distributions

Lecture 20 Mean, Median, and Mode

Lecture 21 Left & Right Skew

Lecture 22 ASSIGNMENT: Measures of Central Tendency

Lecture 23 SOLUTION: Measures of Central Tendency

Lecture 24 Min, Max & Range

Lecture 25 Interquartile Range

Lecture 26 Box & Whisker Plots

Lecture 27 Variance & Standard Deviation

Lecture 28 PRO TIP: Coefficient of Variation

Lecture 29 ASSIGNMENT: Measures of Variability

Lecture 30 SOLUTION: Measures of Variability

Lecture 31 Key Takeaways

Section 4: PROJECT #1: Maven Pizza Parlor

Lecture 32 PROJECT BRIEF: Maven Pizza Parlor

Lecture 33 SOLUTION: Maven Pizza Parlor

Section 5: Modeling Data with Probability Distributions

Lecture 34 Section Intro

Lecture 35 Probability Distribution Basics

Lecture 36 Types of Probability Distributions

Lecture 37 The Normal Distribution

Lecture 38 Z Scores

Lecture 39 The Empirical Rule

Lecture 40 ASSIGNMENT: Normal Distributions

Lecture 41 SOLUTION: Normal Distributions

Lecture 42 Excel’s Normal Distribution Functions

Lecture 43 Calculating Probabilities with the Normal Distribution

Lecture 44 The NORM.DIST Function

Lecture 45 The NORM.S.DIST Function

Lecture 46 ASSIGNMENT: Calculating Probabilities

Lecture 47 SOLUTION: Calculating Probabilities

Lecture 48 PRO TIP: Plotting the Normal Curve

Lecture 49 Estimating X or Z Values with the Normal Distribution

Lecture 50 The NORM.INV Function

Lecture 51 The NORM.S.INV Function

Lecture 52 ASSIGNMENT: Estimating Values

Lecture 53 SOLUTION: Estimating Values

Lecture 54 Key Takeaways

Section 6: PROJECT #2: Maven Medical Center

Lecture 55 PROJECT BRIEF: Maven Medical Center

Lecture 56 SOLUTION: Maven Medical Center

Section 7: The Central Limit Theorem

Lecture 57 Section Intro

Lecture 58 The Central Limit Theorem

Lecture 59 DEMO: Proving the Central Limit Theorem

Lecture 60 Standard Error

Lecture 61 Implications of the Central Limit Theorem

Lecture 62 Applications of the Central Limit Theorem

Lecture 63 Key Takeaways

Section 8: Making Estimates with Confidence Intervals

Lecture 64 Section Intro

Lecture 65 Confidence Intervals Basics

Lecture 66 Confidence Level

Lecture 67 Margin of Error

Lecture 68 DEMO: Calculating Confidence Intervals

Lecture 69 The CONFIDENCE.NORM Function

Lecture 70 ASSIGNMENT: Confidence Intervals

Lecture 71 SOLUTION: Confidence Intervals

Lecture 72 Types of Confidence Intervals

Lecture 73 T Distribution

Lecture 74 Excel’s T Distribution Functions

Lecture 75 Confidence Intervals with the T Distribution

Lecture 76 ASSIGNMENT: Confidence Intervals (T Distribution)

Lecture 77 SOLUTION: Confidence Intervals (T Distribution)

Lecture 78 Confidence Intervals for Proportions

Lecture 79 ASSIGNMENT: Confidence Intervals (Proportions)

Lecture 80 SOLUTION: Confidence Intervals (Proportions)

Lecture 81 Confidence Intervals for Two Populations

Lecture 82 Dependent Samples

Lecture 83 ASSIGNMENT: Confidence Intervals (Dependent Samples)

Lecture 84 SOLUTION: Confidence Intervals (Dependent Samples)

Lecture 85 Independent Samples

Lecture 86 ASSIGNMENT: Confidence Intervals (Independent Samples)

Lecture 87 SOLUTION: Confidence Intervals (Independent Samples)

Lecture 88 PRO TIP: Difference Between Proportions

Lecture 89 Key Takeaways

Section 9: PROJECT #3: Maven Pharma

Lecture 90 PROJECT BRIEF: Maven Pharma

Lecture 91 SOLUTION: Maven Pharma

Section 10: Drawing Conclusions with Hypothesis Tests

Lecture 92 Section Intro

Lecture 93 Hypothesis Testing Basics

Lecture 94 Null & Alternative Hypothesis

Lecture 95 Significance Level

Lecture 96 Test Statistic (T-score)

Lecture 97 P-Value

Lecture 98 Drawing Conclusions from Hypothesis Tests

Lecture 99 ASSIGNMENT: Hypothesis Tests

Lecture 100 SOLUTION: Hypothesis Tests

Lecture 101 Relationship between Confidence Intervals & Hypothesis Tests

Lecture 102 Type I & Type II Errors

Lecture 103 One Tail & Two Tail Hypothesis Tests

Lecture 104 DEMO: One Tail Hypothesis Test

Lecture 105 Hypothesis Tests for Proportions

Lecture 106 ASSIGNMENT: Hypothesis Tests (Proportions)

Lecture 107 SOLUTION: Hypothesis Tests (Proportions)

Lecture 108 Hypothesis Tests for Dependent Samples

Lecture 109 ASSIGNMENT: Hypothesis Tests (Dependent Samples)

Lecture 110 SOLUTION: Hypothesis Tests (Dependent Samples)

Lecture 111 Hypothesis Tests for Independent Samples

Lecture 112 ASSIGNMENT: Hypothesis Tests (Independent Samples)

Lecture 113 SOLUTION: Hypothesis Tests (Independent Samples)

Lecture 114 Key Takeaways

Section 11: PROJECT #4: Maven Safety Council

Lecture 115 PROJECT BRIEF: Maven Safety Council

Lecture 116 SOLUTION: Maven Safety Council

Section 12: Making Predictions with Regression Analysis

Lecture 117 Section Intro

Lecture 118 Linear Relationships

Lecture 119 Correlation (R)

Lecture 120 ASSIGNMENT: Linear Relationships

Lecture 121 SOLUTION: Linear Relationships

Lecture 122 Linear Regression & Least Squared Error

Lecture 123 Excel’s Linear Regression Functions

Lecture 124 ASSIGNMENT: Simple Linear Regression

Lecture 125 SOLUTION: Simple Linear Regression

Lecture 126 Determination (R-Squared)

Lecture 127 Standard Error

Lecture 128 Homoskedasticity & Heteroskedasticity

Lecture 129 Hypothesis Testing with Regression

Lecture 130 ASSIGNMENT: Model Evaluation

Lecture 131 SOLUTION: Model Evaluation

Lecture 132 Excel’s Regression Tool (Analysis ToolPak)

Lecture 133 PRO TIP: Multiple Linear Regression

Lecture 134 Key Takeaways

Section 13: PROJECT #5: Maven Airlines

Lecture 135 PROJECT BRIEF: Maven Airlines

Lecture 136 SOLUTION: Maven Airlines

Section 14: BONUS LESSON

Lecture 137 BONUS LESSON

Aspiring data professionals who want an intuitive, beginner-friendly introduction to the world of statistics,Business intelligence professionals who want to make confident, data-driven decisions,Anyone using data to make assumptions, estimates or predictions on the job,Students looking to learn powerful, practical skills with unique, hands-on projects and demos

#### Course Information:

Udemy | English | 7h 51m | 2.84 GB

Created by: Maven Analytics

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