تبلیغات

# آموزش آمار با Excel این آموزش تصویری نحوه پیاده سازی و انجام محاسبات آماری با استفاده از Excel را فرامیگیرید.

این دوره آموزشی محصول موسسه Udemy می باشد.

سرفصل های دوره:

• آمار توصیفی در مقابل آمار استنباطی بخش 1
• آمار توصیفی در مقابل آمار استنباطی بخش 2
• کار با متغیرها
• کار با نوع متغیر
• انواع اندازه گیری ها
• مقدمه Excel
• توزیع فراوانی در Excel بخش 1
• توزیع فراوانی در Excel بخش 2
• توزیع فراوانی در Excel بخش 3
• توزیع فراوانی و امکانات آن
• Dotplots و نمودار در Excel
• نمودار هیستوگرام
• فرکانس در مقابل فرکانس نسبی
• کار با مجموعه Stemplots خلاصه توزیع
• کار با میانگین، میانه، حالت بخش 1
• کار با میانگین، میانه، حالت بخش 2
• کار با میانگین، میانه، حالت بخش 3
• کار با میانگین، میانه، حالت بخش 4
• Boxplots در اکسل
• اصلاح Boxplot
• مفهوم عدم تقارن
• محاسبه تقارن و در یک نمودار اماری بخش 1
• محاسبه تقارن و در یک نمودار اماری بخش 2
• محاسبه تقارن و در یک نمودار اماری بخش 3
• توزیع نرمال قسمت 1
• توزیع نرمال قسمت 2
• توزیع نرمال چیست
• استاندارد عادی توزیع نرمال
• کار با انواع مشکلات
• توزیع نرمال: PDF مقابل CDF، قسمت 1
• توزیع نرمال: PDF مقابل CDF، قسمت 2
• توزیع نرمال: PDF مقابل CDF، قسمت 3
• کار با Scatterplots
• مجموع خطاها مربعات (SSE)
• ضریب همبستگی (R)
• محاسبه R
• SST و SSE چیست؟
• کار با انواع داده ها
• و...

عنوان دوره: Udemy Statistics with Excel

توضیحات:

```to learn statistics using Excel to build more marketable skills or ace your college course? We'll help you through it with our super comprehensive 36 hour course broken down into easy to understand 159 lessons.
Each lesson begins with concept overviews before our instructor dives into step-by-step data manipulation in Excel. Data sets are downloadable so you can follow along.
Although our course is catered towards college students, professional students and even high school students taking the AP will find our information lifesaving.
Did we mention you'll also have an awesome teacher?
Dr. Son has a Ph.D. in Psychology and Cognitive Science, is a published researcher on how people learn and apply abstract concepts, and has been teaching college level statistics for over 10 years.
Category: Academics / Math & Science
What are the requirements?
Knowledge of math up to Algebra 2
Microsoft Excel installed
What am I going to get from this course?
Over 146 lectures and 35.5 hours of content!
Ace your college level General Statistics course
Master statistical analysis on Microsoft Excel
What is the target audience?
College students in an introductory statistics course
Professionals looking to add statistical analysis to their Excel skillset
High school students taking the AP Statistics course
SECTION 1:
Introduction
1
Descriptive Statistics vs. Inferential Statistics, Part 1
19:10
Statistics
Let's Think About High School Science
Statistics = Math of Distributions
Statistics
Two Skills of Statistics
Descriptive Statistics vs. Inferential Statistics: Apply to Distributions
Populations vs. Samples
Putting Together Descriptive/Inferential Stats & Populations/Samples
Example 1: Descriptive Statistics vs. Inferential Statistics
Example 2: Descriptive Statistics vs. Inferential Statistics
Example 3: Sample, Parameter, Population, and Statistic
Example 4: Sample, Parameter, Population, and Statistic
2
Descriptive Statistics vs. Inferential Statistics, Part 2
06:21
Example 1: Descriptive Statistics vs. Inferential Statistics
Example 2: Descriptive Statistics vs. Inferential Statistics
Example 3: Sample, Parameter, Population, and Statistic
Example 4: Sample, Parameter, Population, and Statistic
SECTION 2:
3
About Samples: Cases, Variables, Measurements, Part 1
14:14
Data
How Do We Get Data?
Types of Variables
4
About Samples: Cases, Variables, Measurements, Part 2
17:56
Types of Measurements
Types of Measurements (Scales)
Example 1: Cases, Variables, Measurements
Example 2: Which Scale of Measurement is Used?
Example 3: What Kind of a Scale of Measurement is This?
Example 4: Discrete vs. Continuous Variables.
SECTION 3:
Visualizing Distributions
5
Introduction to Excel, Part 1
07:14
Before Visualizing Distribution
Excel: Organization
Excel + Data
6
Frequency Distributions in Excel, Part 1
18:22
Preview
Raw Data to Frequency Tables
Example 1: Number of Births
Example 2: Age Distribution
Example 3: Height Distribution
Example 4: Height Distribution of Males
7
Frequency Distributions in Excel, Part 2
13:47
Raw Data to Frequency Tables
Example 1: Number of Births
Example 2: Age Distribution
Example 3: Height Distribution
Example 4: Height Distribution of Males
8
Frequency Distributions in Excel, Part 3
06:36
Raw Data to Frequency Tables
Example 1: Number of Births
Example 2: Age Distribution
Example 3: Height Distribution
Example 4: Height Distribution of Males
9
Frequency Distributions and Features, Part 1
13:53
Example #1
Example #2
Example #3
Example #4a
Example #4b
10
Frequency Distributions and Features, Part 2
11:26
Summary
Sketch Problem 2: Life Expectancy
Sketch Problem 3: Telephone Numbers
Sketch Problem 4: Length of Time Used to Complete a Final Exam
11
Dotplots and Histograms in Excel, Part 1
15:24
Previously
Dotplots
Dotplots: Pros and Cons
Histograms
12
Dotplots and Histograms in Excel, Part 2
16:11
Histograms
13
Dotplots and Histograms in Excel, Part 3
10:59
Histograms: Pros and Cons
Frequency vs. Relative Frequency
Example 1: Dotplots vs. Histograms
Example 2: Age of Pennies Dotplot
Example 3: Histogram of Mammal Speeds
Example 4: Histogram of Life Expectancy
14
Stemplots
12:17
What Sets Stemplots Apart?
Example 1: What Do Stemplots Look Like?
Example 2: Back-to-Back Stemplots
Example 4: Quiz Grade & Afterschool Tutoring Stemplot
15
Bar Graphs, Part 1
14:01
Review of Frequency Distributions
Example 1: Bar Graph
Do Shapes, Center, and Spread of Distributions Apply to Bar Graphs?
16
Bar Graphs, Part 2
08:41
Example 2: Create a Frequency Visualization for Gender
Example 3: Cases, Variables, and Frequency Visualization
Example 4: What Kind of Graphs are Shown Below?
SECTION 4:
Summarizing Distributions
17
Central Tendency: Mean, Median, Mode, Part 1
15:04
Central Tendency 1
Central Tendency 2
Summation Symbol
Population vs. Sample
Excel Examples
Median vs. Mean
Example 1: Mean
Example 2: Using Summation Symbol
Example 3: Average Calorie Count
Example 4: Creating an Example Set
18
Central Tendency: Mean, Median, Mode, Part 2
10:23
Central Tendency 1
Central Tendency 2
Summation Symbol
Population vs. Sample
Excel Examples
Median vs. Mean
Example 1: Mean
Example 2: Using Summation Symbol
Example 3: Average Calorie Count
Example 4: Creating an Example Set
19
Central Tendency: Mean, Median, Mode, Part 3
13:15
Central Tendency 1
Central Tendency 2
Summation Symbol
Population vs. Sample
Excel Examples
Median vs. Mean
Example 1: Mean
Example 2: Using Summation Symbol
Example 3: Average Calorie Count
Example 4: Creating an Example Set
20
Central Tendency: Mean, Median, Mode, Part 4
09:35
Central Tendency 1
Central Tendency 2
Summation Symbol
Population vs. Sample
Excel Examples
Median vs. Mean
Example 1: Mean
Example 2: Using Summation Symbol
Example 3: Average Calorie Count
Example 4: Creating an Example Set
21
Variability, Part 1
12:25
Range, Quartiles and Interquartile Range
Interquartile Range Example
Variance and Standard Deviation
Sum of Squares (SS)
Population vs. Sample SD
Population vs. Sample
Example 1: Find the Mean and Standard Deviation of the Variable Friends in the Excel File
Example 2: Find the Mean and Standard Deviation of the Tagged Photos in the Excel File
Example 3: Sum of Squares
Example 4: Standard Deviation
22
Variability, Part 2
14:53
Range, Quartiles and Interquartile Range
Interquartile Range Example
Variance and Standard Deviation
Sum of Squares (SS)
Population vs. Sample SD
Population vs. Sample
Example 1: Find the Mean and Standard Deviation of the Variable Friends in the Excel File
Example 2: Find the Mean and Standard Deviation of the Tagged Photos in the Excel File
Example 3: Sum of Squares
Example 4: Standard Deviation
23
Variability, Part 3
15:15
Range, Quartiles and Interquartile Range
Interquartile Range Example
Variance and Standard Deviation
Sum of Squares (SS)
Population vs. Sample SD
Population vs. Sample
Example 1: Find the Mean and Standard Deviation of the Variable Friends in the Excel File
Example 2: Find the Mean and Standard Deviation of the Tagged Photos in the Excel File
Example 3: Sum of Squares
Example 4: Standard Deviation
24
Five Number Summary & Boxplots, Part 1
17:02
Summarizing Distributions
Boxplot: Visualizing 5 Number Summary
Boxplots on Excel
When are Boxplots Useful?
How to Determine Outlier Status
Modified Boxplot
Example 1: Percentage Values & Lower and Upper Whisker
Example 2: Boxplot
Example 3: Estimating IQR From Boxplot
Example 4: Boxplot and Missing Whisker
25
Five Number Summary & Boxplots, Part 2
17:58
Summarizing Distributions
Boxplot: Visualizing 5 Number Summary
Boxplots on Excel
When are Boxplots Useful?
How to Determine Outlier Status
Modified Boxplot
Example 1: Percentage Values & Lower and Upper Whisker
Example 2: Boxplot
Example 3: Estimating IQR From Boxplot
Example 4: Boxplot and Missing Whisker
26
Five Number Summary & Boxplots, Part 3
14:08
Summarizing Distributions
Boxplot: Visualizing 5 Number Summary
Boxplots on Excel
When are Boxplots Useful?
How to Determine Outlier Status
Modified Boxplot
Example 1: Percentage Values & Lower and Upper Whisker
Example 2: Boxplot
Example 3: Estimating IQR From Boxplot
Example 4: Boxplot and Missing Whisker
27
Five Number Summary & Boxplots, Part 4
07:59
Summarizing Distributions
Boxplot: Visualizing 5 Number Summary
Boxplots on Excel
When are Boxplots Useful?
How to Determine Outlier Status
Modified Boxplot
Example 1: Percentage Values & Lower and Upper Whisker
Example 2: Boxplot
Example 3: Estimating IQR From Boxplot
Example 4: Boxplot and Missing Whisker
28
Shape: Calculating Skewness & Kurtosis, Part 1
08:55
Skewness Concept
Calculating Skewness
Interpreting Skewness
Kurtosis Concept
Calculating Kurtosis
Interpreting Kurtosis
Example 1: Shape of Distribution
Example 2: Shape of Distribution
Example 3:Shape of Distribution
Example 4: Kurtosis
29
Shape: Calculating Skewness & Kurtosis, Part 2
11:31
Skewness Concept
Calculating Skewness
Interpreting Skewness
Kurtosis Concept
Calculating Kurtosis
Interpreting Kurtosis
Example 1: Shape of Distribution
Example 2: Shape of Distribution
Example 3:Shape of Distribution
Example 4: Kurtosis
30
Shape: Calculating Skewness & Kurtosis, Part 3
11:37
Skewness Concept
Calculating Skewness
Interpreting Skewness
Kurtosis Concept
Calculating Kurtosis
Interpreting Kurtosis
Example 1: Shape of Distribution
Example 2: Shape of Distribution
Example 3:Shape of Distribution
Example 4: Kurtosis
31
Normal Distribution, Part 1
19:45
What is a Normal Distribution
Possible Range of Probabilities
What is a Normal Distribution
"Same" Shape: Illusion of Different Shape!
Types of Problems
Shape Analogy
Example 1: The Standard Normal Distribution and Z-Scores
Example 2: The Standard Normal Distribution and Z-Scores
Example 3: Sketching and Normal Distribution
Example 4: Sketching and Normal Distribution
32
Normal Distribution, Part 2
14:35
What is a Normal Distribution
Possible Range of Probabilities
What is a Normal Distribution
"Same" Shape: Illusion of Different Shape!
Types of Problems
Shape Analogy
Example 1: The Standard Normal Distribution and Z-Scores
Example 2: The Standard Normal Distribution and Z-Scores
Example 3: Sketching and Normal Distribution
Example 4: Sketching and Normal Distribution
33
Standard Normal Distributions & Z-Scores, Part 1
16:41
Preview
A Family of Distributions
Normal Distribution vs. Standard Normal Distribution
Z-Score, Raw Score, Mean, & SD
Weird Z-Scores
Excel
Types of Problems
Shape Analogy
Example 1: Deaths Due to Heart Disease vs. Deaths Due to Cancer
Example 2: Heights of Male College Students
Example 3: Mean and Standard Deviation
Example 4: Finding Percentage of Values in a Standard Normal Distribution
34
Standard Normal Distributions & Z-Scores, Part 2
11:39
A Family of Distributions
Normal Distribution vs. Standard Normal Distribution
Z-Score, Raw Score, Mean, & SD
Weird Z-Scores
Excel
Types of Problems
Shape Analogy
Example 1: Deaths Due to Heart Disease vs. Deaths Due to Cancer
Example 2: Heights of Male College Students
Example 3: Mean and Standard Deviation
Example 4: Finding Percentage of Values in a Standard Normal Distribution
35
Standard Normal Distributions & Z-Scores, Part 3
13:17
A Family of Distributions
Normal Distribution vs. Standard Normal Distribution
Z-Score, Raw Score, Mean, & SD
Weird Z-Scores
Excel
Types of Problems
Shape Analogy
Example 1: Deaths Due to Heart Disease vs. Deaths Due to Cancer
Example 2: Heights of Male College Students
Example 3: Mean and Standard Deviation
Example 4: Finding Percentage of Values in a Standard Normal Distribution
36
Normal Distribution: PDF vs. CDF, Part 1
18:33
Frequency vs. Cumulative Frequency
Frequency vs. Cumulative Frequency
Calculus in Brief
PDF
Integral of PDF = CDF
Example 1: Cumulative Frequency Graph
Example 2: Mean, Standard Deviation, and Probability
Example 3: Mean and Standard Deviation
Example 4: Age of Cars
37
Normal Distribution: PDF vs. CDF, Part 2
17:15
Frequency vs. Cumulative Frequency
Frequency vs. Cumulative Frequency
Calculus in Brief
PDF
Integral of PDF = CDF
Example 1: Cumulative Frequency Graph
Example 2: Mean, Standard Deviation, and Probability
Example 3: Mean and Standard Deviation
Example 4: Age of Cars
38
Normal Distribution: PDF vs. CDF, Part 3
19:51
Frequency vs. Cumulative Frequency
Frequency vs. Cumulative Frequency
Calculus in Brief
PDF
Integral of PDF = CDF
Example 1: Cumulative Frequency Graph
Example 2: Mean, Standard Deviation, and Probability
Example 3: Mean and Standard Deviation
Example 4: Age of Cars
SECTION 5:
Linear Regression
39
Scatterplots, Part 1
18:28
Previous Visualizations
Compare & Contrast
Summary Values
Example Scatterplot
Positive and Negative Association
Linearity, Strength, and Consistency
Summarizing a Scatterplot
Example 1: Gapminder.org, Income x Life Expectancy
Example 2: Gapminder.org, Income x Infant Mortality
Example 3: Trend and Strength of Variables
Example 4: Trend, Strength and Shape for Scatterplots
40
Scatterplots, Part 2
17:41
Previous Visualizations
Compare & Contrast
Summary Values
Example Scatterplot
Positive and Negative Association
Linearity, Strength, and Consistency
Summarizing a Scatterplot
Example 1: Gapminder.org, Income x Life Expectancy
Example 2: Gapminder.org, Income x Infant Mortality
Example 3: Trend and Strength of Variables
Example 4: Trend, Strength and Shape for Scatterplots
41
Scatterplots, Part 3
11:04
Previous Visualizations
Compare & Contrast
Summary Values
Example Scatterplot
Positive and Negative Association
Linearity, Strength, and Consistency
Summarizing a Scatterplot
Example 1: Gapminder.org, Income x Life Expectancy
Example 2: Gapminder.org, Income x Infant Mortality
Example 3: Trend and Strength of Variables
Example 4: Trend, Strength and Shape for Scatterplots
42
Regression, Part 1
14:48
Linear Equations
Rough Line
Regression - A "Center" Line
Goal of Regression
Prediction
Error in Prediction
Example 1: Residual
Example 2: Large and Negative Residual
Example 3: Positive Residual
Example 4: Interpret Regression Line & Extrapolate
43
Regression, Part 2
17:05
Linear Equations
Rough Line
Regression - A "Center" Line
Goal of Regression
Prediction
Error in Prediction
Example 1: Residual
Example 2: Large and Negative Residual
Example 3: Positive Residual
Example 4: Interpret Regression Line & Extrapolate
44
Least Squares Regression, Part 1
17:16
Best Fit
Sum of Squared Errors (SSE)
Why Squared?
Quantitative Properties of Regression Line
So How do we Find Such a Line?
How Do We Find Slope (b1)
Hoe Do We Find Intercept
Example 1: Which of These Equations Fit the Above Data Best?
Example 2: Find the Regression Line for These Data Points and Interpret It
Example 3: Summarize the Scatterplot and Find the Regression Line.
Example 4: Examine the Mean of Residuals
45
Least Squares Regression, Part 2
17:13
Best Fit
Sum of Squared Errors (SSE)
Why Squared?
Quantitative Properties of Regression Line
So How do we Find Such a Line?
How Do We Find Slope (b1)
Hoe Do We Find Intercept
Example 1: Which of These Equations Fit the Above Data Best?
Example 2: Find the Regression Line for These Data Points and Interpret It
Example 3: Summarize the Scatterplot and Find the Regression Line.
Example 4: Examine the Mean of Residuals
46
Least Squares Regression, Part 3
09:20
Best Fit
Sum of Squared Errors (SSE)
Why Squared?
Quantitative Properties of Regression Line
So How do we Find Such a Line?
How Do We Find Slope (b1)
Hoe Do We Find Intercept
Example 1: Which of These Equations Fit the Above Data Best?
Example 2: Find the Regression Line for These Data Points and Interpret It
Example 3: Summarize the Scatterplot and Find the Regression Line.
Example 4: Examine the Mean of Residuals
47
Least Squares Regression, Part 4
12:36
Best Fit
Sum of Squared Errors (SSE)
Why Squared?
Quantitative Properties of Regression Line
So How do we Find Such a Line?
How Do We Find Slope (b1)
Hoe Do We Find Intercept
Example 1: Which of These Equations Fit the Above Data Best?
Example 2: Find the Regression Line for These Data Points and Interpret It
Example 3: Summarize the Scatterplot and Find the Regression Line.
Example 4: Examine the Mean of Residuals
48
Correlation, Part 1
15:06
Summarizing a Scatterplot Quantitatively
Correlation Coefficient ( r )
Trees vs. Forest
Calculating r
Relationship between Correlation and Slope
Example 1: Find the Correlation between Grams of Fat and Cost
Example 2: Relationship between r and b1
Example 3: Find the Regression Line
Example 4: Find the Correlation Coefficient for this Set of Data
49
Correlation, Part 2
15:18
Summarizing a Scatterplot Quantitatively
Correlation Coefficient ( r )
Trees vs. Forest
Calculating r
Relationship between Correlation and Slope
Example 1: Find the Correlation between Grams of Fat and Cost
Example 2: Relationship between r and b1
Example 3: Find the Regression Line
Example 4: Find the Correlation Coefficient for this Set of Data
50
Correlation, Part 3
13:24
Summarizing a Scatterplot Quantitatively
Correlation Coefficient ( r )
Trees vs. Forest
Calculating r
Relationship between Correlation and Slope
Example 1: Find the Correlation between Grams of Fat and Cost
Example 2: Relationship between r and b1
Example 3: Find the Regression Line
Example 4: Find the Correlation Coefficient for this Set of Data
51
Correlation: r vs. r-squared, Part 1
18:33
R-squared
Parsing Sum of Squared (Parsing Variability)
What is SST and SSE?
r-squared
If the Correlation is Strong…
If the Correlation is Weak…
Example 1: Find r-squared for this Set of Data
Example 2: What Does it Mean that the Simple Linear Regression is a "Model" of Variance?
Example 3: Why Does r-squared Only Range from 0 to 1
Example 4: Find the r-squared for This Set of Data
52
Correlation: r vs. r-squared, Part 2
18:51
R-squared
Parsing Sum of Squared (Parsing Variability)
What is SST and SSE?
r-squared
If the Correlation is Strong…
If the Correlation is Weak…
Example 1: Find r-squared for this Set of Data
Example 2: What Does it Mean that the Simple Linear Regression is a "Model" of Variance?
Example 3: Why Does r-squared Only Range from 0 to 1
Example 4: Find the r-squared for This Set of Data
53
Correlation: r vs. r-squared, Part 3
15:20
R-squared
Parsing Sum of Squared (Parsing Variability)
What is SST and SSE?
r-squared
If the Correlation is Strong…
If the Correlation is Weak…
Example 1: Find r-squared for this Set of Data
Example 2: What Does it Mean that the Simple Linear Regression is a "Model" of Variance?
Example 3: Why Does r-squared Only Range from 0 to 1
Example 4: Find the r-squared for This Set of Data
54
Transformations of Data, Part 1
10:35
Why Transform?
Shape-preserving vs. Shape-changing Transformations
Common Shape-Preserving Transformations
Common Shape-Changing Transformations
Change Just One Variable? Both?
Example 1: Create, Graph, and Transform the Data Set
Example 2: Create, Graph, and Transform the Data Set
Example 3: What Kind of Model would You Choose for this Data?
Example 4: Transformation of Data
55
Transformations of Data, Part 2
16:24
Why Transform?
Shape-preserving vs. Shape-changing Transformations
Common Shape-Preserving Transformations
Common Shape-Changing Transformations
Change Just One Variable? Both?
Example 1: Create, Graph, and Transform the Data Set
Example 2: Create, Graph, and Transform the Data Set
Example 3: What Kind of Model would You Choose for this Data?
Example 4: Transformation of Data
SECTION 6:
Collecting Data in an Experiment
56
Sampling & Bias, Part 1
13:06
Descriptive vs. Inferential Statistics
To tackle Generalization…
Defining Samples and Populations
Why Use Sampling?
Goal of Sampling: Avoiding Bias
Sampling Bias: Bias from Bas Sampling Methods
Response Bias: Bias from "Bad" Data Collection Methods
Example 1: What Kind of Biases?
Example 2: What Biases Might Arise?
Example 3: What Kind of Biases?
Example 4: What Kind of Biases?
57
Sampling & Bias, Part 2
14:52
Descriptive vs. Inferential Statistics
To tackle Generalization…
Defining Samples and Populations
Why Use Sampling?
Goal of Sampling: Avoiding Bias
Sampling Bias: Bias from Bas Sampling Methods
Response Bias: Bias from "Bad" Data Collection Methods
Example 1: What Kind of Biases?
Example 2: What Biases Might Arise?
Example 3: What Kind of Biases?
Example 4: What Kind of Biases?
58
Sampling & Bias, Part 3
12:30
Descriptive vs. Inferential Statistics
To tackle Generalization…
Defining Samples and Populations
Why Use Sampling?
Goal of Sampling: Avoiding Bias
Sampling Bias: Bias from Bas Sampling Methods
Response Bias: Bias from "Bad" Data Collection Methods
Example 1: What Kind of Biases?
Example 2: What Biases Might Arise?
Example 3: What Kind of Biases?
Example 4: What Kind of Biases?
59
Sampling & Bias, Part 4
14:05
Descriptive vs. Inferential Statistics
To tackle Generalization…
Defining Samples and Populations
Why Use Sampling?
Goal of Sampling: Avoiding Bias
Sampling Bias: Bias from Bas Sampling Methods
Response Bias: Bias from "Bad" Data Collection Methods
Example 1: What Kind of Biases?
Example 2: What Biases Might Arise?
Example 3: What Kind of Biases?
Example 4: What Kind of Biases?
60
Sampling Methods
14:16
Biased vs. Unbiased Sampling Methods
Probability Sampling Methods
Example 2: Describe How to Take a Two-Stage Sample from this Book
Example 3: Sampling Methods
Example 4: Cluster Sample Plan
61
Research Design, Part 1
17:53
Descriptive vs. Inferential Statistics
Variables and Relationships
Not Every Type of Study is an Experiment…
Category III
Why CAN'T the Other Strategies Determine Causation?
What Makes Experiments Special?
Methods of Control
Experiment Terminology
Blind
How Categories Relate to Statistics
Example 1: Research Design
Example 2: Research Design
Example 3: Research Design
Example 4: Research Design
62
Research Design, Part 2
16:14
Descriptive vs. Inferential Statistics
Variables and Relationships
Not Every Type of Study is an Experiment…
Category III
Why CAN'T the Other Strategies Determine Causation?
What Makes Experiments Special?
Methods of Control
Experiment Terminology
Blind
How Categories Relate to Statistics
Example 1: Research Design
Example 2: Research Design
Example 3: Research Design
Example 4: Research Design
63
Research Design, Part 3
19:28
Descriptive vs. Inferential Statistics
Variables and Relationships
Not Every Type of Study is an Experiment…
Category III
Why CAN'T the Other Strategies Determine Causation?
What Makes Experiments Special?
Methods of Control
Experiment Terminology
Blind
How Categories Relate to Statistics
Example 1: Research Design
Example 2: Research Design
Example 3: Research Design
Example 4: Research Design
64
Between and Within Treatment Variability, Part 1
15:45
Experimental Designs
Two Types of Variability
Updated Goal of Experimental Design
Example: Drugs and Driving
Different Types of Random Assignment
Randomized Block Design
Between-subject Variable vs. Within-subject Variable
Example 1: Design a Completely Random, Matched Pair, and Repeated Measures Experiment
Example 2: Block Design
Example 3: Completely Randomized Designs
Example 4: Completely Random, Matched Pairs, or Repeated Measures Experiments?
65
Between and Within Treatment Variability, Part 2
10:28
Experimental Designs
Two Types of Variability
Updated Goal of Experimental Design
Example: Drugs and Driving
Different Types of Random Assignment
Randomized Block Design
Between-subject Variable vs. Within-subject Variable
Example 1: Design a Completely Random, Matched Pair, and Repeated Measures Experiment
Example 2: Block Design
Example 3: Completely Randomized Designs
Example 4: Completely Random, Matched Pairs, or Repeated Measures Experiments?
66
Between and Within Treatment Variability, Part 3
15:12
Experimental Designs
Two Types of Variability
Updated Goal of Experimental Design
Example: Drugs and Driving
Different Types of Random Assignment
Randomized Block Design
Between-subject Variable vs. Within-subject Variable
Example 1: Design a Completely Random, Matched Pair, and Repeated Measures Experiment
Example 2: Block Design
Example 3: Completely Randomized Designs
Example 4: Completely Random, Matched Pairs, or Repeated Measures Experiments?
SECTION 7:
Review of Probability Axioms
67
Sample Spaces, Part 1
15:06
Why is Probability Involved in Statistics
Taste Test with Coffee Drinkers
Creating a Probability Model
D'Alembert vs. Necker
Problem with D'Alembert's Model
Covering Entire Sample Space
Where Do Probabilities Come From?
Checking whether Model Matches Real World
Example 1: Law of Large Numbers
Example 2: Possible Outcomes
Example 3: Brands of Coffee and Taste
Example 4: How Many Different Treatments are there?
68
Sample Spaces, Part 2
12:38
Why is Probability Involved in Statistics
Taste Test with Coffee Drinkers
Creating a Probability Model
D'Alembert vs. Necker
Problem with D'Alembert's Model
Covering Entire Sample Space
Where Do Probabilities Come From?
Checking whether Model Matches Real World
Example 1: Law of Large Numbers
Example 2: Possible Outcomes
Example 3: Brands of Coffee and Taste
Example 4: How Many Different Treatments are there?
69
Sample Spaces, Part 3
10:04
Why is Probability Involved in Statistics
Taste Test with Coffee Drinkers
Creating a Probability Model
D'Alembert vs. Necker
Problem with D'Alembert's Model
Covering Entire Sample Space
Where Do Probabilities Come From?
Checking whether Model Matches Real World
Example 1: Law of Large Numbers
Example 2: Possible Outcomes
Example 3: Brands of Coffee and Taste
Example 4: How Many Different Treatments are there?
70
Addition Rule for Disjoint Events, Part 1
10:48
Disjoint Events
Meaning of "or"
Example 1: Which of These are Mutually Exclusive?
Example 2: What is the Probability that You will Have a Combination of One Heads and Two Tails?
Example 3: Engagement Party
Example 4: Home Owner's Insurance
71
Addition Rule for Disjoint Events, Part 2
09:34
Disjoint Events
Meaning of "or"
Example 1: Which of These are Mutually Exclusive?
Example 2: What is the Probability that You will Have a Combination of One Heads and Two Tails?
Example 3: Engagement Party
Example 4: Home Owner's Insurance
72
Conditional Probability, Part 1
15:00
"or" vs. "and" vs. Conditional Probability
"and" vs. Conditional Probability
Tree Diagram
Defining Conditional Probability
Common Contexts for Conditional Probability
Example 1: Drug and Disease
Example 2: Marbles and Conditional Probability
Example 3: Cards and Conditional Probability
Example 4: Votes and Conditional Probability
73
Conditional Probability, Part 2
15:51
"or" vs. "and" vs. Conditional Probability
"and" vs. Conditional Probability
Tree Diagram
Defining Conditional Probability
Common Contexts for Conditional Probability
Example 1: Drug and Disease
Example 2: Marbles and Conditional Probability
Example 3: Cards and Conditional Probability
Example 4: Votes and Conditional Probability
74
Conditional Probability, Part 3
15:03
"or" vs. "and" vs. Conditional Probability
"and" vs. Conditional Probability
Tree Diagram
Defining Conditional Probability
Common Contexts for Conditional Probability
Example 1: Drug and Disease
Example 2: Marbles and Conditional Probability
Example 3: Cards and Conditional Probability
Example 4: Votes and Conditional Probability
75
Conditional Probability, Part 4
11:18
"or" vs. "and" vs. Conditional Probability
"and" vs. Conditional Probability
Tree Diagram
Defining Conditional Probability
Common Contexts for Conditional Probability
Example 1: Drug and Disease
Example 2: Marbles and Conditional Probability
Example 3: Cards and Conditional Probability
Example 4: Votes and Conditional Probability
76
Independent Events, Part 1
11:10
Preview
Independent Events & Conditional Probability
Non-independent and Independent Events
Defining Independent Events
Multiplication Rule
Example 1: Which of These Pairs of Events are Independent?
Example 2: Health Insurance and Probability
Example 3: Independent Events
Example 4: Independent Events
77
Independent Events, Part 2
13:07
Independent Events & Conditional Probability
Non-independent and Independent Events
Defining Independent Events
Multiplication Rule
Example 1: Which of These Pairs of Events are Independent?
Example 2: Health Insurance and Probability
Example 3: Independent Events
Example 4: Independent Events
SECTION 8:
Probability Distributions
78
Introduction to Probability Distributions, Part 1
17:42
Sampling vs. Probability
Missing
Insight: Probability Distributions
From Sample Spaces to Probability Distributions
The Random Variable
Expected Value
Example 1: Probability Distributions
Example 2: Probability Distributions
Example 3: Probability Distributions
Example 4: Probability Distributions
79
Introduction to Probability Distributions, Part 2
17:44
Sampling vs. Probability
Missing
Insight: Probability Distributions
From Sample Spaces to Probability Distributions
The Random Variable
Expected Value
Example 1: Probability Distributions
Example 2: Probability Distributions
Example 3: Probability Distributions
Example 4: Probability Distributions
80
Introduction to Probability Distributions, Part 3
11:49
Sampling vs. Probability
Missing
Insight: Probability Distributions
From Sample Spaces to Probability Distributions
The Random Variable
Expected Value
Example 1: Probability Distributions
Example 2: Probability Distributions
Example 3: Probability Distributions
Example 4: Probability Distributions
81
Introduction to Probability Distributions, Part 4
09:22
Sampling vs. Probability
Missing
Insight: Probability Distributions
From Sample Spaces to Probability Distributions
The Random Variable
Expected Value
Example 1: Probability Distributions
Example 2: Probability Distributions
Example 3: Probability Distributions
Example 4: Probability Distributions
82
Expected Value & Variance of Probability Distributions, Part 1
19:19
Discrete vs. Continuous Random Variables
Mean and Variance Review
Example Situation
Some Special Cases…
Linear Transformations
n Independent Values of X
Compare These Two Situations
Two Random Variables, X and Y
Example 1: Expected Value & Variance of Probability Distributions
Example 2: Expected Values & Standard Deviation
Example 3: Expected Winnings and Standard Deviation
83
Expected Value & Variance of Probability Distributions, Part 2
16:13
Discrete vs. Continuous Random Variables
Mean and Variance Review
Example Situation
Some Special Cases…
Linear Transformations
n Independent Values of X
Compare These Two Situations
Two Random Variables, X and Y
Example 1: Expected Value & Variance of Probability Distributions
Example 2: Expected Values & Standard Deviation
Example 3: Expected Winnings and Standard Deviation
84
Expected Value & Variance of Probability Distributions, Part 3
18:05
Discrete vs. Continuous Random Variables
Mean and Variance Review
Example Situation
Some Special Cases…
Linear Transformations
n Independent Values of X
Compare These Two Situations
Two Random Variables, X and Y
Example 1: Expected Value & Variance of Probability Distributions
Example 2: Expected Values & Standard Deviation
Example 3: Expected Winnings and Standard Deviation
85
Binomial Distribution, Part 1
19:34
Discrete Probability Distributions
Binomial Distribution
Multiplicative Rule Review
How Many Outcomes with k "Successes"
P (X=k)
Expected Value and Standard Deviation in a Binomial Distribution
Example 1: Coin Toss
Example 3: Types of Blood and Probability
Example 4: Expected Number and Standard Deviation
86
Binomial Distribution, Part 2
18:25
Discrete Probability Distributions
Binomial Distribution
Multiplicative Rule Review
How Many Outcomes with k "Successes"
P (X=k)
Expected Value and Standard Deviation in a Binomial Distribution
Example 1: Coin Toss
Example 3: Types of Blood and Probability
Example 4: Expected Number and Standard Deviation
87
Binomial Distribution, Part 3
17:02
Discrete Probability Distributions
Binomial Distribution
Multiplicative Rule Review
How Many Outcomes with k "Successes"
P (X=k)
Expected Value and Standard Deviation in a Binomial Distribution
Example 1: Coin Toss
Example 3: Types of Blood and Probability
Example 4: Expected Number and Standard Deviation
SECTION 9:
Sampling Distributions of Statistics
88
Introduction to Sampling Distributions, Part 1
14:12
Probability Distributions vs. Sampling Distributions
Same Logic
Simulating Samples
Connecting Sampling and Research Methods with Sampling Distributions
Simulating a Sampling Distribution
Logic of Sampling Distributions
General Method of Simulating Sampling Distributions
Questions that Remain
Example 1: Mean and Standard Error of Sampling Distribution
Example 2: What is the Best Way to Describe Sampling Distributions?
Example 3: Matching Sampling Distributions
Example 4: Mean and Standard Error of Sampling Distribution
89
Introduction to Sampling Distributions, Part 2
16:45
Probability Distributions vs. Sampling Distributions
Same Logic
Simulating Samples
Connecting Sampling and Research Methods with Sampling Distributions
Simulating a Sampling Distribution
Logic of Sampling Distributions
General Method of Simulating Sampling Distributions
Questions that Remain
Example 1: Mean and Standard Error of Sampling Distribution
Example 2: What is the Best Way to Describe Sampling Distributions?
Example 3: Matching Sampling Distributions
Example 4: Mean and Standard Error of Sampling Distribution
90
Introduction to Sampling Distributions, Part 3
17:16
Probability Distributions vs. Sampling Distributions
Same Logic
Simulating Samples
Connecting Sampling and Research Methods with Sampling Distributions
Simulating a Sampling Distribution
Logic of Sampling Distributions
General Method of Simulating Sampling Distributions
Questions that Remain
Example 1: Mean and Standard Error of Sampling Distribution
Example 2: What is the Best Way to Describe Sampling Distributions?
Example 3: Matching Sampling Distributions
Example 4: Mean and Standard Error of Sampling Distribution
91
Sampling Distribution of the Mean, Part 1
15:49
Special Case of General Method for Simulating a Sampling Distribution
Using Simulations to See Principles behind Shape of SDoM
Using Simulations to See Principles behind Center (Mean) of SDoM
Using Simulations to See Principles behind Standard Deviation of SDoM
Central Limit Theorem
Comparing Population, Sample, and SDoM
Example 1: Mean Batting Average
Example 2: Mean Sampling Distribution and Standard Error
Example 3: Sampling Distribution of the Mean
92
Sampling Distribution of the Mean, Part 2
11:22
Special Case of General Method for Simulating a Sampling Distribution
Using Simulations to See Principles behind Shape of SDoM
Using Simulations to See Principles behind Center (Mean) of SDoM
Using Simulations to See Principles behind Standard Deviation of SDoM
Central Limit Theorem
Comparing Population, Sample, and SDoM
Example 1: Mean Batting Average
Example 2: Mean Sampling Distribution and Standard Error
Example 3: Sampling Distribution of the Mean
93
Sampling Distribution of the Mean, Part 3
15:55
Special Case of General Method for Simulating a Sampling Distribution
Using Simulations to See Principles behind Shape of SDoM
Using Simulations to See Principles behind Center (Mean) of SDoM
Using Simulations to See Principles behind Standard Deviation of SDoM
Central Limit Theorem
Comparing Population, Sample, and SDoM
Example 1: Mean Batting Average
Example 2: Mean Sampling Distribution and Standard Error
Example 3: Sampling Distribution of the Mean
94
Sampling Distribution of the Mean, Part 4
12:15
Special Case of General Method for Simulating a Sampling Distribution
Using Simulations to See Principles behind Shape of SDoM
Using Simulations to See Principles behind Center (Mean) of SDoM
Using Simulations to See Principles behind Standard Deviation of SDoM
Central Limit Theorem
Comparing Population, Sample, and SDoM
Example 1: Mean Batting Average
Example 2: Mean Sampling Distribution and Standard Error
Example 3: Sampling Distribution of the Mean
95
Sampling Distribution of the Mean, Part 5
13:23
Special Case of General Method for Simulating a Sampling Distribution
Using Simulations to See Principles behind Shape of SDoM
Using Simulations to See Principles behind Center (Mean) of SDoM
Using Simulations to See Principles behind Standard Deviation of SDoM
Central Limit Theorem
Comparing Population, Sample, and SDoM
Example 1: Mean Batting Average
Example 2: Mean Sampling Distribution and Standard Error
Example 3: Sampling Distribution of the Mean
96
Sampling Distribution of Sample Proportions, Part 1
19:12
Intro to Sampling Distribution of Sample Proportions (SDoSP)
Notation
What's the Difference?
Binomial Distribution vs. Sampling Distribution of Sample Proportions
Example 1: Sampling Distribution of Sample Proportions
Example 2: Sampling Distribution of Sample Proportions
Example 3: Sampling Distribution of Sample Proportions
Example 4: Sampling Distribution of Sample Proportions
97
Sampling Distribution of Sample Proportions, Part 2
18:41
Intro to Sampling Distribution of Sample Proportions (SDoSP)
Notation
What's the Difference?
Binomial Distribution vs. Sampling Distribution of Sample Proportions
Example 1: Sampling Distribution of Sample Proportions
Example 2: Sampling Distribution of Sample Proportions
Example 3: Sampling Distribution of Sample Proportions
Example 4: Sampling Distribution of Sample Proportions
98
Sampling Distribution of Sample Proportions, Part 3
16:37
Intro to Sampling Distribution of Sample Proportions (SDoSP)
Notation
What's the Difference?
Binomial Distribution vs. Sampling Distribution of Sample Proportions
Example 1: Sampling Distribution of Sample Proportions
Example 2: Sampling Distribution of Sample Proportions
Example 3: Sampling Distribution of Sample Proportions
Example 4: Sampling Distribution of Sample Proportions
SECTION 10:
Inferential Statistics
99
Introduction to Confidence Intervals, Part 1
18:24
Inferential Statistics
Two Problems with This Picture…
Which Parameters are Known?
Confidence Interval - Goal
When We Don't Know
Example 1: Confidence Intervals
Example 2: Confidence Intervals
Example 3: Confidence Intervals
Example 4: Confidence Intervals
100
Introduction to Confidence Intervals, Part 2
11:19
Inferential Statistics
Two Problems with This Picture…
Which Parameters are Known?
Confidence Interval - Goal
When We Don't Know
Example 1: Confidence Intervals
Example 2: Confidence Intervals
Example 3: Confidence Intervals
Example 4: Confidence Intervals```

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