Statistical Thinking for Data Analysis
Statistical Thinking for Data Analysis, available at $99.99, has an average rating of 4.65, with 42 lectures, based on 32 reviews, and has 293 subscribers.
You will learn about How to assess the quality of study design and how it affects data quality How to reason with variation in data using data visualizations and summary statistics How to reason with the variation in sample statistics using sampling distributions A conceptual understanding of statistical inference: confidence intervals and hypothesis testing Introduction to linear regression Introduction to R and RStudio analyzing real-world data This course is ideal for individuals who are Students who want to gain a foundational understanding of statistics. or Professionals, like doctors or journalists, who need a better understanding of statistical inference for assessing the quality of research or Professors preparing to teach introductory statistics courses using my book or Anyone who wants to gain the critical and statistical thinking skills to question the quality of statistical information presented in the media or in journal articles It is particularly useful for Students who want to gain a foundational understanding of statistics. or Professionals, like doctors or journalists, who need a better understanding of statistical inference for assessing the quality of research or Professors preparing to teach introductory statistics courses using my book or Anyone who wants to gain the critical and statistical thinking skills to question the quality of statistical information presented in the media or in journal articles.
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Summary
Title: Statistical Thinking for Data Analysis
Price: $99.99
Average Rating: 4.65
Number of Lectures: 42
Number of Published Lectures: 42
Number of Curriculum Items: 42
Number of Published Curriculum Objects: 42
Original Price: $99.99
Quality Status: approved
Status: Live
What You Will Learn
- How to assess the quality of study design and how it affects data quality
- How to reason with variation in data using data visualizations and summary statistics
- How to reason with the variation in sample statistics using sampling distributions
- A conceptual understanding of statistical inference: confidence intervals and hypothesis testing
- Introduction to linear regression
- Introduction to R and RStudio analyzing real-world data
Who Should Attend
- Students who want to gain a foundational understanding of statistics.
- Professionals, like doctors or journalists, who need a better understanding of statistical inference for assessing the quality of research
- Professors preparing to teach introductory statistics courses using my book
- Anyone who wants to gain the critical and statistical thinking skills to question the quality of statistical information presented in the media or in journal articles
Target Audiences
- Students who want to gain a foundational understanding of statistics.
- Professionals, like doctors or journalists, who need a better understanding of statistical inference for assessing the quality of research
- Professors preparing to teach introductory statistics courses using my book
- Anyone who wants to gain the critical and statistical thinking skills to question the quality of statistical information presented in the media or in journal articles
Course Description
This is a university level course taught by a professor with 15 years of teaching experience at Columbia University and other institutions including New York University, Yale University, and Berkeley.
It is a clear and concise version of my Columbia University course titled “Statistical Thinking for Data Science” (google title for details). It is the companion course to the third edition of my book “Statistical Thinking through Media Examples” published by Cognella. You can preview and purchase my book from their website (google book title) .
My course and book provides you with the statistical intuition and understanding you need, through media, research, and real-world data examples. I communicate the statistical concepts in my course and book in a language you will understand with a minimum of calculations. You will learn to go beyond the news headlines to critique the quality of the research for yourself, including polls and surveys. You will learn how to conduct statistical analysis and how to interpret the results.
With a solid foundational understanding of statistics, you can build on that foundation with confidence. You can become a statistical analyst or data scientist with the statistical intuition to properly conduct research and interpret the results. Join me and start building that necessary foundation today!
Course Curriculum
Chapter 1: Overview of Course Content
Lecture 1: Section 1: Preview
Chapter 2: Populations and Samples
Lecture 1: Data, Types of Variables, Datasets
Lecture 2: Examples – The MMR Vaccine – Autism Link, COVID Testing in NYC Population
Lecture 3: R-Labs – Dataframes and Subsetting
Chapter 3: Types of Studies
Lecture 1: Randomized Experiments – Migraine Study Example
Lecture 2: Randomized Experiment – Hydroxychloroquine and COVID Study Example
Lecture 3: Observational Studies – Hydroxychloroquine and COVID Study Example
Chapter 4: Measuring Uncertainty with Probability
Lecture 1: What is Probability?
Lecture 2: Conditional Probability – Hospital Example
Lecture 3: Conditional Probability – Blood Pressure – Cholesterol Example
Lecture 4: Bayes Rule – Medical Testing for Disease Example
Chapter 5: Visualizing and Summarizing Sample Data
Lecture 1: Histograms, Means and Standard Deviations – Climate Change and College Football
Lecture 2: The Normal Distribution and Z-Scores
Lecture 3: The Normal Distribution and Z-Scores – Blood Pressure Example
Lecture 4: R-Labs – Summary Statistics
Lecture 5: R-Labs – Data Visualizations
Chapter 6: Visualizing and Summarizing Sample Statistics
Lecture 1: Sample Proportions – Election Polling and Coin-Toss Example
Lecture 2: Sample Means – Height Example
Lecture 3: Sample Means – Pregnancy Duration Example
Chapter 7: Confidence Intervals
Lecture 1: Confidence Intervals: Population Mean – Heart Disease Example
Lecture 2: Population Proportion – National Youth Fitness Survey and Election Polling
Lecture 3: Comparing Means – National Youth Fitness Survey Example
Chapter 8: Hypothesis Testing
Lecture 1: Population Proportion – Coin-Toss Example
Lecture 2: Population Mean – Height and Heart Disease Data Examples
Lecture 3: One-Sided versus Two-Sided Alternatives – Height and Heart Disease Data Examples
Lecture 4: Hypothesis Testing : What Does the P-value Really Mean?
Lecture 5: Comparing Means – National Youth Fitness Survey Example
Lecture 6: Comparing Means – College Football Study Example
Lecture 7: Comparing Proportions – Analysis of 2×2 Tables – National Youth Fitness Survey
Lecture 8: Comparing Proportions – Analysis of 2×2 Tables – The Power-Pose Study Example
Lecture 9: Types of Error and Power of the Test
Lecture 10: Types of Error and Power of the Test – Mask Study and Hydroxychloroquine Example
Lecture 11: R-Labs – Comparing Means
Lecture 12: R-Labs – Comparing Proportions
Chapter 9: Linear Regression
Lecture 1: Correlation – National Youth Fitness Survey Data Example
Lecture 2: The Line of Best Fit – COVID-19 and National Youth Fitness Survey Data Example
Lecture 3: Understanding How the Line of Best Fit is Chosen
Lecture 4: Statistical Inference for the Line of Best Fit – National Youth Fitness Survey
Lecture 5: Multiple Linear Regression – National Youth Fitness Survey Data Example
Lecture 6: R-Labs – Simple Linear Regression
Chapter 10: Integrity in Research
Lecture 1: The Extra Sensory Perception and Power-Pose Studies
Lecture 2: The Vioxx Study – Oxycontin and The Opioid Epidemic
Instructors
-
Anthony Donoghue
Instructor
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 1 votes
- 3 stars: 1 votes
- 4 stars: 12 votes
- 5 stars: 18 votes
Frequently Asked Questions
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