English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 137 lectures (7h 51m) | 2.02 GB
Learn statistics for data analysis with fun, real-world Excel demos: probability, hypothesis testing, regression and more!
This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis and business intelligence.
Our goal is to simplify and demystify the world of statistics using familiar spreadsheet 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
Table of Contents
Getting Started
1 Course Structure & Outline
2 READ ME Important Notes for New Students
3 DOWNLOAD Course Resources
4 Setting Expectations
5 The Course Project
6 Helpful Resources
Why Statistics
7 Section Intro
8 Why Statistics
9 Populations & Samples
10 The Statistics Workflow
Understanding Data with Descriptive Statistics
11 Section Intro
12 Descriptive Statistics Basics
13 Types of Variables
14 Types of Descriptive Statistics
15 Categorical Frequency Distributions
16 Numerical Frequency Distributions
17 Histograms
18 ASSIGNMENT Frequency Distributions
19 KNOWLEDGE CHECK Frequency Distributions
20 SOLUTION Frequency Distributions
21 Mean, Median, and Mode
22 Left & Right Skew
23 ASSIGNMENT Measures of Central Tendency
24 KNOWLEDGE CHECK Measures of Central Tendency
25 SOLUTION Measures of Central Tendency
26 Min, Max & Range
27 Interquartile Range
28 Box & Whisker Plots
29 Variance & Standard Deviation
30 PRO TIP Coefficient of Variation
31 ASSIGNMENT Measures of Variability
32 KNOWLEDGE CHECK Measures of Variability
33 SOLUTION Measures of Variability
34 Key Takeaways
PROJECT #1 Maven Pizza Parlor
35 PROJECT BRIEF Maven Pizza Parlor
36 SOLUTION Maven Pizza Parlor
Modeling Data with Probability Distributions
37 Section Intro
38 Probability Distribution Basics
39 Types of Probability Distributions
40 The Normal Distribution
41 Z Scores
42 The Empirical Rule
43 ASSIGNMENT Normal Distributions
44 KNOWLEDGE CHECK Normal Distributions
45 SOLUTION Normal Distributions
46 Excel’s Normal Distribution Functions
47 Calculating Probabilities with the Normal Distribution
48 The NORM.DIST Function
49 The NORM.S.DIST Function
50 ASSIGNMENT Calculating Probabilities
51 KNOWLEDGE CHECK Calculating Probabilities
52 SOLUTION Calculating Probabilities
53 PRO TIP Plotting the Normal Curve
54 Estimating X or Z Values with the Normal Distribution
55 The NORM.INV Function
56 The NORM.S.INV Function
57 ASSIGNMENT Estimating Values
58 KNOWLEDGE CHECK Estimating Values
59 SOLUTION Estimating Values
60 Key Takeaways
PROJECT #2 Maven Medical Center
61 PROJECT BRIEF Maven Medical Center
62 SOLUTION Maven Medical Center
The Central Limit Theorem
63 Section Intro
64 The Central Limit Theorem
65 DEMO Proving the Central Limit Theorem
66 Standard Error
67 Implications of the Central Limit Theorem
68 Applications of the Central Limit Theorem
69 Key Takeaways
Making Estimates with Confidence Intervals
70 Section Intro
71 Confidence Intervals Basics
72 Confidence Level
73 Margin of Error
74 DEMO Calculating Confidence Intervals
75 The CONFIDENCE.NORM Function
76 ASSIGNMENT Confidence Intervals
77 KNOWLEDGE CHECK Confidence Intervals
78 SOLUTION Confidence Intervals
79 Types of Confidence Intervals
80 T Distribution
81 Excel’s T Distribution Functions
82 Confidence Intervals with the T Distribution
83 ASSIGNMENT Confidence Intervals (T Distribution)
84 KNOWLEDGE CHECK Confidence Intervals (T Distribution)
85 SOLUTION Confidence Intervals (T Distribution)
86 Confidence Intervals for Proportions
87 ASSIGNMENT Confidence Intervals (Proportions)
88 KNOWLEDGE CHECK Confidence Intervals (Proportions)
89 SOLUTION Confidence Intervals (Proportions)
90 Confidence Intervals for Two Populations
91 Dependent Samples
92 ASSIGNMENT Confidence Intervals (Dependent Samples)
93 KNOWLEDGE CHECK Confidence Intervals (Dependent Samples)
94 SOLUTION Confidence Intervals (Dependent Samples)
95 Independent Samples
96 ASSIGNMENT Confidence Intervals (Independent Samples)
97 KNOWLEDGE CHECK Confidence Intervals (Independent Samples)
98 SOLUTION Confidence Intervals (Independent Samples)
99 PRO TIP Difference Between Proportions
100 Key Takeaways
PROJECT #3 Maven Pharma
101 PROJECT BRIEF Maven Pharma
102 SOLUTION Maven Pharma
Drawing Conclusions with Hypothesis Tests
103 Section Intro
104 Hypothesis Testing Basics
105 Null & Alternative Hypothesis
106 Significance Level
107 Test Statistic (T-score)
108 P-Value
109 Drawing Conclusions from Hypothesis Tests
110 ASSIGNMENT Hypothesis Tests
111 KNOWLEDGE CHECK Hypothesis Tests
112 SOLUTION Hypothesis Tests
113 Relationship between Confidence Intervals & Hypothesis Tests
114 Type I & Type II Errors
115 One Tail & Two Tail Hypothesis Tests
116 DEMO One Tail Hypothesis Test
117 Hypothesis Tests for Proportions
118 ASSIGNMENT Hypothesis Tests (Proportions)
119 KNOWLEDGE CHECK Hypothesis Tests (Proportions)
120 SOLUTION Hypothesis Tests (Proportions)
121 Hypothesis Tests for Dependent Samples
122 ASSIGNMENT Hypothesis Tests (Dependent Samples)
123 KNOWLEDGE CHECK Hypothesis Tests (Dependent Samples)
124 SOLUTION Hypothesis Tests (Dependent Samples)
125 Hypothesis Tests for Independent Samples
126 ASSIGNMENT Hypothesis Tests (Independent Samples)
127 KNOWLEDGE CHECK Hypothesis Tests (Independent Samples)
128 SOLUTION Hypothesis Tests (Independent Samples)
129 Key Takeaways
PROJECT #4 Maven Safety Council
130 PROJECT BRIEF Maven Safety Council
131 SOLUTION Maven Safety Council
Making Predictions with Regression Analysis
132 Section Intro
133 Linear Relationships
134 Correlation (R)
135 ASSIGNMENT Linear Relationships
136 KNOWLEDGE CHECK Linear Relationships
137 SOLUTION Linear Relationships
138 Linear Regression & Least Squared Error
139 Excel’s Linear Regression Functions
140 ASSIGNMENT Simple Linear Regression
141 KNOWLEDGE CHECK Simple Linear Regression
142 SOLUTION Simple Linear Regression
143 Determination (R-Squared)
144 Standard Error
145 Homoskedasticity & Heteroskedasticity
146 Hypothesis Testing with Regression
147 ASSIGNMENT Model Evaluation
148 KNOWLEDGE CHECK Model Evaluation
149 SOLUTION Model Evaluation
150 Excel’s Regression Tool (Analysis ToolPak)
151 PRO TIP Multiple Linear Regression
152 Key Takeaways
PROJECT #5 Maven Airlines
153 PROJECT BRIEF Maven Airlines
154 SOLUTION Maven Airlines
BONUS LESSON
155 BONUS LESSON
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