Statistical information is truly everywhere. You can't look at a newspaper without seeing statistics on virtually every page. Meaning from Data: Statistics Made Clear is your introduction to a vitally important subject in today's data-driven society. In 24 half-hour lectures, which require no background in mathematics beyond basic algebra, you explore the principles and methods that underlie the study of statistics. Professor Michael Starbird puts his emphasis on the role of statistics in daily life: weather forecasts, business, and a host of other applications.
Meaning from Data: Statistics Made Clear
Learn how to understand statistical information and the role it plays in everything from political polls to stock performances in this course taught by a professor of Mathematics.
Rated 5 out of
5
by
WatchingU from
Statistics from a wise outsider
I have listened to others of Prof Starbird's classes, mainly on calculus but also probability. His easygoing lecture style draws the listener into the subject and makes learning painless. His choice of examples consistently shows the breadth of the subject and always includes a few surprises. For example, I had heard that Mendel's data were fudged, but had never seen a detailed explanation of how this was discovered -- by Ronald Fisher no less. (If Fisher comes after you, it's probably all over.) It is interesting to me that Starbird uses relatively few formulas in these lectures; it is hard to imagine statistics without formulas because that is the way it's usually taught, but it works here since the goal is to understand the concepts and not fall into memorization without learning. It is highly probable (actually we already know this) that the instructor is a mathematician rather than a statistician, and this helps the concepts stay ahead of the formulas. I liked it.
Date published: 2024-01-20
Rated 5 out of
5
by
TommyBVA from
Interesting and entertaining
The course is just what I was looking for; to gain a high level view of statistics and understand some of the common terminology and definitions, not to master even the basics of the subject. On top of that, I appreciated Professor Starbird's putting things in perspective, pointing out the strengths and limitations of statistical analysis.
I will quote, and heartily agree with, an earlier review an earlier reviewer who described Professor Starbird as a "personable professor with a friendly folksy style." That style actually greatly increased how much I enjoyed the course. I prefer a realistic presentation to one that has been highly scripted and edited to present someone's idea of perfection. Some of the more recent Wondrium Great Courses fall into this category and I find those presentations somewhat dull and lifeless, even as the information presented is interesting and informative. That being said, the use and presentation of the graphical information was well done and instrumental to following the course.
Date published: 2023-06-21
Rated 5 out of
5
by
Gwick from
Yes, It’s Everywhere
For me, this was an excellent introduction & thorough overview of Statistics in approx. 6 hours of lectures. Prof. Starbird does a wonderful job. I came away with a greater awareness of the use of Statisics in forming conclusions or judgments, as well its misuse. Since we see Statistics used daily, this seems well worth learning more about and I look forward to a future course that can spend some more time on the underlying math.
Date published: 2023-03-12
Rated 5 out of
5
by
Taggert from
Makes a complex topic understandable
Dr. Starbird does a great job of explaining data sets along with a variety of methods to analyze and present data in layman's terms. I am not quite finished with the course. So far, it is great.
Date published: 2021-10-30
Rated 4 out of
5
by
Mlyeates from
Educational examples
The first several lectures started off slowly & over repetitive with definitions. But the remainder of the course was quite good with many applied examples that helped me understand the useage & application of statistics.
Date published: 2021-09-30
Rated 5 out of
5
by
DHARMENDRA from
Simple & concise
I think statistics is a critical subject for anyone to make better decisions & not get fooled by misleading data
Date published: 2021-04-14
Rated 5 out of
5
by
William K from
Great teacher
As usual this man is a teacher and not just a lecturer .Of over 40 courses I have taken he is the best teacher. Even better than Sapolsky & Wolsonand and thats saying a lot.
Date published: 2020-12-14
Rated 5 out of
5
by
maturestudent from
Excellent introduction to the subject
I am a mature student studying for a science degree out of personal interest. I had bad experiences with maths a long time ago when at school. So I have been having to upgrade my maths. I have just completed a university course on statistics. I watched this course by Professor Starbird before starting my university course and watched it again on completion of my course. I found Professor Starbird's course to be an excellent introduction to the subject. I enjoyed his relaxed and quite humorous style of presentation. I thought he explained things well and made the subject very interesting with all his examples.The course prepared me well for my university course and I surprised myself by being awarded a distinction. So I would recommend the course for students and for those with a general interest in statistics.
Date published: 2020-11-01
Overview
About
Dr. Michael Starbird is Professor of Mathematics and University Distinguished Teaching Professor at The University of Texas at Austin, where he has been teaching since 1974. He received his B.A. from Pomona College in 1970 and his Ph.D. in Mathematics from the University of Wisconsin-Madison in 1974. Professor Starbird's textbook, The Heart of Mathematics: An Invitation to Effective Thinking, coauthored with Edward B. Burger, won a 2001 Robert W. Hamilton Book Award. Professors Starbird and Burger also collaborated on Coincidences, Chaos, and All That Math Jazz: Making Light of Weighty Ideas, published in 2005. Professor Starbird has won many teaching awards, including the Mathematical Association of America's 2007 Deborah and Franklin Tepper Haimo National Award for Distinguished College or University Teaching of Mathematics, which is the association's most prestigious teaching award. It is awarded nationally to 3 people from its membership of 27,000. Professor Starbird is interested in bringing authentic understanding of significant ideas in mathematics to people who are not necessarily mathematically oriented. He has developed and taught an acclaimed class that presents higher-level mathematics to liberal arts students.
Trailer
01: Describing Data and Inferring Meaning
The statistical study of data deals with two fundamental questions: How can we describe and understand a situation when we have all the pertinent data about it? How can we infer features of all the data when we know only some of the data?
33 min
02: Data and Distributions-Getting the Picture
The first three rules of statistics should be: Draw a picture, draw a picture, draw a picture. A visual representation of data reveals patterns and relationships, for example, the distribution of one variable, or an association between two variables.
32 min
03: Inference-How Close? How Confident?
The logic of statistical inference is to compare data that we collect to expectations about what the data would be if the world were random in some particular respect. Randomness and probability are the cornerstones of all methods for testing hypotheses.
33 min
04: Describing Dispersion or Measuring Spread
This lecture defines and explores standard deviation, which measures how widely data are spread from the mean. The various methods of measuring data dispersion have different properties that determine the best method to use.
30 min
05: Models of Distributions-Shapely Families
Any shaped curve can model a data set. This lecture looks at skewed and bimodal shapes, and describes other characteristically shaped classes of distributions, including exponential and Poisson. Each shape arises naturally in specific settings.
32 min
06: The Bell Curve
The most famous shape of distributions is the bell-shaped curve, also called a normal curve or a Gaussian distribution. This lecture explores its properties and why it arises so frequently-as in the central limit theorem, one of the core insights on which statistical inference is based.
32 min
07: Correlation and Regression-Moving Together
One way we attempt to understand the world is to identify cases of cause and effect. In statistics, the challenge is to describe and measure the relationship between two variables, for example, incoming SAT scores and college grade point averages.
33 min
08: Probability-Workhorse for Inference
Probability accomplishes the seemingly impossible feat of putting a useful, numerical value on the likelihood of random events. Our intuition about what to expect from randomness is often far from accurate. This lecture looks at several examples that place intuition and reality far apart.
32 min
09: Samples-The Few, The Chosen
Sampling is a technique for inferring features of a whole population from information about some of its members. A familiar example is a political poll. Interesting issues and problems arise in taking and using samples. Examples of potential pitfalls are explored.
30 min
10: Hypothesis Testing-Innocent Until
This lecture introduces a fundamental strategy of statistical inference called hypothesis testing. The method involves assessing whether observed data are consistent with a claim about the population in order to determine whether the claim might be false. Drug testing is a common application.
31 min
11: Confidence Intervals-How Close? How Sure?
Headlines at election time frequently trumpet statistics such as: "Candidate A will receive 59 percent of the vote, with a margin of error of plus or minus 3 percent." This lecture investigates what this "margin of error" statement means and why it is incomplete as written.
34 min
12: Design of Experiments-Thinking Ahead
When gathering data from which deductions can be drawn confidently, it's important to think ahead. Double-blind experiments and other strategies can help meet the goal of good experimental design.
31 min
13: Law-You're the Jury
Opening the second part of the course, which deals with applying statistics, this lecture focuses on two examples of courtroom drama: a hit-and-run accident and a gender-discrimination case. In both, the analysis of statistics aids in reaching a fair verdict.
29 min
14: Democracy and Arrow's Impossibility Theorem
An election assembles individual opinions into one societal decision. This lecture considers a surprising reality about elections: The outcome may have less to do with voters' preferences than with the voting method used, especially when three or more candidates are involved.
29 min
15: Election Problems and Engine Failure
The challenge of choosing an election winner can be thought of as taking voters' rank orderings of candidates and returning a societal rank ordering. A mathematically similar situation occurs when trying to determine what type of engine lasts longest among competing versions.
29 min
16: Sports-Who's Best of All Time?
Analyzing statistical data in sports is a sport of its own. This lecture asks, "Who is the best hitter in baseball history?" The question presents statistical challenges in comparing performances in different eras. Another mystery is also probed: "Is the 'hot hand' phenomenon real, or is it random?"
31 min
17: Risk-War and Insurance
A discussion of strategies for estimating the number of Mark V tanks produced by the Germans in World War II brings up the idea of expected value, a central concept in the risky business of buying and selling insurance.
30 min
18: Real Estate-Accounting for Value
Tax authorities often need to set valuations for every house in a tax district. The challenge is to use the data about recently sold houses to assess the values of all the houses. This classic example of statistical inference introduces the idea of multiple linear regression.
29 min
19: Misleading, Distorting, and Lying
Statistics can be used to deceive as well as enlighten. This lecture explores deceptive practices such as concealing lurking variables, using biased samples, focusing on rare events, reporting handpicked data, extrapolating trends unrealistically, and confusing correlation with causation.
30 min
20: Social Science-Parsing Personalities
This lecture addresses two topics that come up when applying statistics to social sciences: factor analysis, which seeks to identify underlying factors that explain correlation among a larger group of measured quantities, and possible limitations of hypothesis testing.
31 min
21: Quack Medicine, Good Hospitals, and Dieting
Medical treatments are commonly based on statistical studies. Aspects to consider in contemplating treatment include the characteristics of the study group and the difference between correlation and causation. Another statistical concept, regression to the mean, explains why quack medicines can appear to work.
31 min
22: Economics-"One" Way to Find Fraud
Economics relies on a wealth of statistical data, including income levels, the balance of trade, the deficit, the stock market, and the consumer price index. A surprising result of such data is that the leading digits of numbers do not occur with equal frequency, and that provides a statistical method for detecting fraud.
31 min
23: Science-Mendel's Too-Good Peas
Statistics is essential in sciences from weather forecasting to quantum physics. This lecture discusses the statistics-based research of Johannes Kepler, Edwin Hubble, and Gregor Mendel. In Mendel's case, statisticians have looked at his studies of the genetics of pea plants and discovered data that are too good to be true.
31 min
24: Statistics Everywhere
The importance of statistics will only increase as greater computer speed and capacity make dealing with ever-larger data sets possible. It has limits that need to be respected, but its potential for helping us find meaning in our data-driven world is enormous and growing.
31 min
