# Essential Statistics for Data Analysis

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 137 lectures (7h 51m) | 2.83 GB

Learn statistics with fun, real-world projects; probability distributions, hypothesis tests, regression analysis & more!

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.

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

Getting Started
Course Structure & Outline
READ ME Important Notes for New Students
Setting Expectations
The Course Project

Why Statistics
Section Intro
Why Statistics
Populations & Samples
The Statistics Workflow

Understanding Data with Descriptive Statistics
Section Intro
Descriptive Statistics Basics
Types of Variables
Types of Descriptive Statistics
Categorical Frequency Distributions
Numerical Frequency Distributions
Histograms
ASSIGNMENT Frequency Distributions
KNOWLEDGE CHECK Frequency Distributions
SOLUTION Frequency Distributions
Mean, Median, and Mode
Left & Right Skew
ASSIGNMENT Measures of Central Tendency
KNOWLEDGE CHECK Measures of Central Tendency
SOLUTION Measures of Central Tendency
Min, Max & Range
Interquartile Range
Box & Whisker Plots
Variance & Standard Deviation
PRO TIP Coefficient of Variation
ASSIGNMENT Measures of Variability
KNOWLEDGE CHECK Measures of Variability
SOLUTION Measures of Variability
Key Takeaways

PROJECT #1 Maven Pizza Parlor
PROJECT BRIEF Maven Pizza Parlor
SOLUTION Maven Pizza Parlor

Modeling Data with Probability Distributions
Section Intro
Probability Distribution Basics
Types of Probability Distributions
The Normal Distribution
Z Scores
The Empirical Rule
ASSIGNMENT Normal Distributions
KNOWLEDGE CHECK Normal Distributions
SOLUTION Normal Distributions
Excel’s Normal Distribution Functions
Calculating Probabilities with the Normal Distribution
The NORM.DIST Function
The NORM.S.DIST Function
ASSIGNMENT Calculating Probabilities
KNOWLEDGE CHECK Calculating Probabilities
SOLUTION Calculating Probabilities
PRO TIP Plotting the Normal Curve
Estimating X or Z Values with the Normal Distribution
The NORM.INV Function
The NORM.S.INV Function
ASSIGNMENT Estimating Values
KNOWLEDGE CHECK Estimating Values
SOLUTION Estimating Values
Key Takeaways

PROJECT #2 Maven Medical Center
PROJECT BRIEF Maven Medical Center
SOLUTION Maven Medical Center

The Central Limit Theorem
Section Intro
The Central Limit Theorem
DEMO Proving the Central Limit Theorem
Standard Error
Implications of the Central Limit Theorem
Applications of the Central Limit Theorem
Key Takeaways

Making Estimates with Confidence Intervals
Section Intro
Confidence Intervals Basics
Confidence Level
Margin of Error
DEMO Calculating Confidence Intervals
The CONFIDENCE.NORM Function
ASSIGNMENT Confidence Intervals
KNOWLEDGE CHECK Confidence Intervals
SOLUTION Confidence Intervals
Types of Confidence Intervals
T Distribution
Excel’s T Distribution Functions
Confidence Intervals with the T Distribution
ASSIGNMENT Confidence Intervals (T Distribution)
KNOWLEDGE CHECK Confidence Intervals (T Distribution)
SOLUTION Confidence Intervals (T Distribution)
Confidence Intervals for Proportions
ASSIGNMENT Confidence Intervals (Proportions)
KNOWLEDGE CHECK Confidence Intervals (Proportions)
SOLUTION Confidence Intervals (Proportions)
Confidence Intervals for Two Populations
Dependent Samples
ASSIGNMENT Confidence Intervals (Dependent Samples)
KNOWLEDGE CHECK Confidence Intervals (Dependent Samples)
SOLUTION Confidence Intervals (Dependent Samples)
Independent Samples
ASSIGNMENT Confidence Intervals (Independent Samples)
KNOWLEDGE CHECK Confidence Intervals (Independent Samples)
SOLUTION Confidence Intervals (Independent Samples)
PRO TIP Difference Between Proportions
Key Takeaways

PROJECT #3 Maven Pharma
PROJECT BRIEF Maven Pharma
SOLUTION Maven Pharma

Drawing Conclusions with Hypothesis Tests
Section Intro
Hypothesis Testing Basics
Null & Alternative Hypothesis
Significance Level
Test Statistic (T-score)
P-Value
Drawing Conclusions from Hypothesis Tests
ASSIGNMENT Hypothesis Tests
KNOWLEDGE CHECK Hypothesis Tests
SOLUTION Hypothesis Tests
Relationship between Confidence Intervals & Hypothesis Tests
Type I & Type II Errors
One Tail & Two Tail Hypothesis Tests
DEMO One Tail Hypothesis Test
Hypothesis Tests for Proportions
ASSIGNMENT Hypothesis Tests (Proportions)
KNOWLEDGE CHECK Hypothesis Tests (Proportions)
SOLUTION Hypothesis Tests (Proportions)
Hypothesis Tests for Dependent Samples
ASSIGNMENT Hypothesis Tests (Dependent Samples)
KNOWLEDGE CHECK Hypothesis Tests (Dependent Samples)
SOLUTION Hypothesis Tests (Dependent Samples)
Hypothesis Tests for Independent Samples
ASSIGNMENT Hypothesis Tests (Independent Samples)
KNOWLEDGE CHECK Hypothesis Tests (Independent Samples)
SOLUTION Hypothesis Tests (Independent Samples)
Key Takeaways

PROJECT #4 Maven Safety Council
PROJECT BRIEF Maven Safety Council
SOLUTION Maven Safety Council

Making Predictions with Regression Analysis
Section Intro
Linear Relationships
Correlation (R)
ASSIGNMENT Linear Relationships
KNOWLEDGE CHECK Linear Relationships
SOLUTION Linear Relationships
Linear Regression & Least Squared Error
Excel’s Linear Regression Functions
ASSIGNMENT Simple Linear Regression
KNOWLEDGE CHECK Simple Linear Regression
SOLUTION Simple Linear Regression
Determination (R-Squared)
Standard Error
Homoskedasticity & Heteroskedasticity
Hypothesis Testing with Regression
ASSIGNMENT Model Evaluation
KNOWLEDGE CHECK Model Evaluation
SOLUTION Model Evaluation
Excel’s Regression Tool (Analysis ToolPak)
PRO TIP Multiple Linear Regression
Key Takeaways

PROJECT #5 Maven Airlines
PROJECT BRIEF Maven Airlines
SOLUTION Maven Airlines

BONUS LESSON
BONUS LESSON

Homepage