About
Statistics
Algebra
Trigonometry and Vectors
Calculus (1st Year)
Advanced Math
Statistics
- Discrete Vs. Continuous
- Survey Design Issues: Causation
- Standard Deviation
- Statistical Charts
- Basic Probability Definitions
- Multiplication Principle for Probabilities, Independent Events
- Conditional Probability
- Independence of Two Events
- Expected Value and Standard Deviation
- Discrete Probability
- The Z-Score
- Uniform Distribution
- The Normal Curve and the Central Limit Theorem
- Confidence Intervals
- Hypothesis Testing
- Regression Line
- Regression Analysis
- Chi Squared Test
- ANOVA
Course Topic: Discrete Vs. Continuous
Video Link: https://www.youtube.com/watch?v=d39tTuUbDVw&index=2&list=PL8A25592E6D32C753
Time: 35:34 to 36:22
University: India Institute of Technology
Course: Artificial Intelligence
Professor Name: Sudeshna Sarkar
Teaching Ideas: This video explains the difference between discrete and continuous environments. This is the same as the discrete and continuous variables that are examined in the statistics class. This is a different point of view of the topic that might provoke interest.
Course Topic: Survey Design Issues: Causation
Video Link: http://oyc.yale.edu/psychology/psyc-110/lecture-13
Time: 58:28 to 63:29
University: Yale
Course: Introduction to Psychology
Professor Name: Paul Bloom
Teaching Ideas: This video goes through several examples explaining why statistically conclusive results do not imply causation. The professor gives the examples of family meals vs. drug use, reading to the child vs. the child becoming a good reader and being abused as a child and growing up to be an abuser. This is a perfect presentation of the dangers of faulty interpretation.
Video Link: http://oyc.yale.edu/psychology/psyc-110/lecture-20
Time: 0:58 to 4:23
University: Yale
Course: Introduction to Psychology
Professor Name: Paul Bloom
Teaching Ideas: This video speaks about the challenges of determining if therapy is a successful. The professor speaks about regression towards the mean and then finishes with the idea of a control study. He states that based on the control study results we know therapy works. This is a practical reinforcement of why one must use a randomized control study to conclude cause and effect.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-4
Time: 36:21 to 38:23
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video compares epidemiology (observational) and intervention (control) studies. The professor defines them in the context of food consumption and states that the control study is more powerful.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-12
Time: 36:21 to 51:47
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video shows a graph of BMI vs. mortality rate. The professor notes that there is a correlation, but causation cannot be concluded. He notes that it could be the case that poverty causes both. The left piece of the scatterplot has a negative correlation which does not mean low BMI causes death. The professor notes that smoking causes both.
Course Topic: Standard Deviation
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-8
Time: 24:42 to 25:26
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video shows the graph in the growth in obesity for several countries. The y-axis corresponds to the percent above two standard deviations from the mean weight. The graph will require some explanation since the professor does not mention anything about the standard deviation, but it is shown on the graph.
Course Topic: Statistical Charts
Video Link: https://www.youtube.com/watch?v=oADF0Bit1Yw&t=3579s
Time: 1:57 to 6:03
University: UCLA
Course: Families and Couples
Professor Name: Benjamin Carney
Teaching Ideas: This video shows two statistical charts. The first is a bar chart that show that poverty issues are a factor in divorce rates. The second is a line graph that shows the years of marriage vs. the percent that have resulted in divorce for three poverty levels. This is an excellent example of using different graphs to display information.
Course Topic: Basic Probability Definitions
Video Link: https://www.youtube.com/watch?v=5K1to94YQtU&list=PL8A25592E6D32C753&index=28
Time: 30:29 to 35:01
University: India Institute of Technology
Course: Artificial Intelligence
Professor Name: Anupam Basu
Teaching Ideas: This video goes over the definition of probability and mutually exclusive events. This come right from a standard lecture in a statistics event. It can reinforce what is done in class.
Video Link: https://www.youtube.com/watch?v=5K1to94YQtU&list=PL8A25592E6D32C753&index=28
Time: 41:29 to 43:44
University: India Institute of Technology
Course: Artificial Intelligence
Professor Name: Anupam Basu
Teaching Ideas: This video goes over rule of complements for probability. It just states the rules and gives a mathy example and nothing about artificial intelligence, so using is is just to show that the same rules are discussed in other courses.
Course Topic: Multiplication Principle for Probabilities, Independent Events
Video Link: https://www.youtube.com/watch?v=CNr_7gPhYtY&list=PL2CD836B66D3CEBED&index=17
Time: 45:45 - 47:49
University: UC Berkeley
Course: Biology 1B (2nd Semester Biology)
Professor Name: John Huelsenbeck
Teaching Ideas: This video uses the multiplication principle for independent probabilities to determine the probability of a genetic match with two alleles. The professor goes on to talk about how this is used in criminal trials. This can be used as a nice introduction to probability theory.
Video Link: https://www.youtube.com/watch?v=5K1to94YQtU&list=PL8A25592E6D32C753&index=28
Time: 34:56 to 39:38
University: India Institute of Technology
Course: Artificial Intelligence
Professor Name: Anupam Basu
Teaching Ideas: This video goes over the definition of independence of two events. The professor shows the definition on a PowerPoint and gives an example. This is pretty standard, but it might be interesting to students since it is coming from another class from another country.
Course Topic: Conditional Probability
Video Link: https://www.youtube.com/watch?v=5K1to94YQtU&list=PL8A25592E6D32C753&index=28
Time: 43:44 to 46:14
University: India Institute of Technology
Course: Artificial Intelligence
Professor Name: Anupam Basu
Teaching Ideas: This video goes over the definition of conditional probability. No examples are given, but the definition coincides with what is done in a statistics class and can reinforce the students understanding. If you go until 49:43, an example is given.
Course Topic: Independence of Two Events
Video Link: http://oyc.yale.edu/chemistry/chem-125a/lecture-10
Time: 45:15 - 48:02
University: Yale
Course: Freshman Organic Chemistry I
Professor Name: Michael McBride
Teaching Ideas: This video applies the uses coins stuck together to demonstrate that two events are not always independent. The professor uses it to demonstrate that the orbitals of multiple electrons are dependent since electrons repel one another. This is nice lesson in resisting the temptation to always multiply probabilities together.
Course Topic: Expected Value and Standard Deviation
Video Link: http://oyc.yale.edu/economics/econ-251/lecture-13
Time: 67:52 to 71:09
University: Yale
Course: Financial Theory
Professor Name: John Geanakoplos
Teaching Ideas: This video goes over the expected value (he calls it expected payoff) and the variance and standard deviation. The professor describes these just as they do in math classes. This is a nice video that shows the most important topic from statistics is used in finance.
Video Link: http://oyc.yale.edu/economics/econ-251/lecture-14
Time: 1:58 to 3:24
University: Yale
Course: Financial Theory
Professor Name: John Geanakoplos
Teaching Ideas: This video shows the professor calculating the expected value, variance and standard deviation by hand for the situation where the sample space is {-1,1} each with probability 1/2. He refers to an investment where you either win or lose $1. This is a simple example that shows the hand computation.
Video Link: http://oyc.yale.edu/economics/econ-251/lecture-20
Time: 7:15 to 7:48
University: Yale
Course: Financial Theory
Professor Name: John Geanakoplos
Teaching Ideas: This video shows the professor calculating the expected value for a bet on the Yankees winning a game. This is quickly done and gives a fun example of the topic of expected values for a discrete probability distribution function.
Video Link: http://oyc.yale.edu/economics/econ-159/lecture-9
Time: 6:10 to 9:59
University: Yale
Course: Game Theory
Professor Name: Ben Polak
Teaching Ideas: This video uses looks at the expected payoff (value) for a game based on a payoff matrix and probabilities of each strategy. The computations are done by hand and are easy to follow.
Video Link: http://oyc.yale.edu/economics/econ-159/lecture-9
Time: 25:49 to 28:13
University: UC Berkeley
Course: Environmental Economics and Policy
Professor Name: David Zetland
Teaching Ideas: This video gives a hypothetical example of the expected utility of going to a football game with a 10% chance of rain. It is a simple example that can be shown when introducing expected value.
Course Topic: Discrete Probability
Video Link: https://youtu.be/xxw-ekvWcFY?list=PLkOvqP5rUuNF5G_ooxxmrPJN8-U7Kmtm1
Time: 28:55 - 35:13
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video is an application of discrete probability to make genetic counseling decisions. The specific mathematics is all at the level of elementary statistics class, but it uses several ideas and formulas. Only the very best students will be able to solve the problem without plenty of explanation from their instructor. Students will need help to follow the reasoning, but the application if incredibly relevant: There is a deadly disease caused by a homozygous gene. 1% of the population dies of this disease. John's brother died of the disease and Jill knows nothing about her family history. If John and Jill have a child, what is the probability that the child will die of the disease?
Course Topic: The Z-Score
Video Link: http://oyc.yale.edu/economics/econ-252-11/lecture-4
Time: 75:33 to 76:40
University: Yale
Course: Financial Markets
Professor Name: Robert J Shiller (Nobel Laureate)
Teaching Ideas: This video defines the Sharpe Ratio for a Portfolio which is basically just the z-score for that portfolio. The professor never used the word Z-Score, so a teaching strategy is to ask the students who have just watched the clip what statistic the Sharpe Ratio corresponds to. The students paying attention should be able to answer "Z-Score". The professor continues the explanation until 77:38 by saying exactly what statistics instructor say about the Z-Score, that the raw value is meaningless without knowing the mean and the standard deviation.
Course Topic: Uniform Distribution
Video Link: http://oyc.yale.edu/economics/econ-251/lecture-16
Time: 67:17 to 69:20 or 70:00 and then again to hear the solution from 72:49 to 73:16. Then at 73:52 to
University: Yale
Course: Financial Theory
Professor Name: John Geanakoplos
Teaching Ideas: This video poses the question if a man meets 1000 women who rate uniformly on [0,1]. After meeting each woman the man can either marry her or move on to the next and never go back. What is the man's optimal strategy? Although this does not give any math that is done in the statistics class that uses the uniform distribution, it is both funny and relevant to college students and "answers" the question why so many married men are seduced by the "other woman". Also the same theory works for how to decide which restaurant to stop at when driving down Main Street in a town you are unfamiliar with. The final part does the mathematics which is pretty simple and could be worth showing if there is time.
Video Link: http://oyc.yale.edu/physics/phys-201/lecture-20
Time: 65:41 to 68:25
University: Yale
Course: Fundamentals of Physics II
Professor Name: Ramamurti Shankar
Teaching Ideas: This video looks at the uniform density function that can model the position of an electron. The professor uses an integral, but calculates it as just the area of the shown rectangle. This is a tough but important example of the uniform distribution.
Video Link: https://www.youtube.com/watch?v=9HWBOHQRZIU&list=PL2Q_sOQgsm24ybtnVq75-TgZd86lMjm9m&index=13
Time: 41:41 to 45:01
University: Harvard
Course: Introduction to Computer Science
Professor Name: D. J. Malan
Teaching Ideas: This video presents the birthday problem: given n people in a room find the probability that at least two will share a birthday. The professor states that he is assuming a uniform distribution. He uses the rule of complements and the multiplication rule for probability. At the end he notes that this concept is essential for understanding how computers work. This would be an instructive example of probability.
Course Topic: The Normal Curve and the Central Limit Theorem
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-3
Time: 13:00 - 15:43
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video begins by explaining the Central Limit Theorem and the Normal Curve. Then it describes how it can be used to look at the rate of evolution of the Galapagos finches. This perfectly ties in the content from the statistics course to evolutionary biology and can be used as an effective introduction to the topic.
Video Link: http://oyc.yale.edu/economics/econ-252-11/lecture-2
Time: 59:02 to 67:25
University: Yale
Course: Financial Markets
Professor Name: Robert J Shiller (Nobel Laureate)
Teaching Ideas: This video explains that with a normal distribution, values far from the mean should occur very infrequently. He gives the example of the daily stock market change since 1928. The graph looks normal, but there are three tremendously large outliers in 1929 that correspond to the stock market crash and the one day rebound. If the distribution had been normal this basically couldn't have occurred. This is a long but interesting application of the normal distribution that can be used to emphasize that the normal distribution rarely occurs for populations that do not correspond to averages and these strong outliers kill normality.
Video Link: http://oyc.yale.edu/economics/econ-252-11/lecture-3
Time: 8:19 to 11:39
University: Yale
Course: Financial Markets
Professor Name: Robert J Shiller (Nobel Laureate)
Teaching Ideas: This video presents the Central Limit Theorem and then goes on to explain its limitations for finance due to the fact that to apply the Central Limit Theorem the variance must be finite. In finance the variance may be infinite due to the large outliers that occur. This video can be used to show that the Central Limit Theorem is not just a math concept. Other fields such as finance and physics make great use of it. Since the video gives a full nonmathematical presentation of the theorem it can reemphasize what the elementary statistics instructor presents.
Video Link: http://oyc.yale.edu/economics/econ-252-11/lecture-5
Time: 9:09 to 10:37
University: Yale
Course: Financial Markets
Professor Name: Robert J Shiller (Nobel Laureate)
Teaching Ideas: This video presents the formula for the standard deviation of a binomial experiment. The professor relates it to the insurance industry. He makes a mistake and his statement is not perfect, but even Nobel Laureates make mistakes. This short clip can be a nice motivator to explain why having many trials in a binomial experiment is preferred.
Video Link: http://oyc.yale.edu/economics/econ-252-11/lecture-7
Time: 7:05 to 10:01
University: Yale
Course: Financial Markets
Professor Name: Robert J Shiller (Nobel Laureate)
Teaching Ideas: This video shows how an investment firm took advantage of the normal distribution and basically sold off the right tail and beefed up its left tail to make more money most of the time. Unfortunately, when the left tail hits, the is catastrophic loss. This is a nice example of using the normal distribution in the real world before understanding the Central Limit Theorem.
Video Link: http://oyc.yale.edu/economics/econ-251/lecture-13
Time: 66:13 to 67:27
University: Yale
Course: Financial Theory
Professor Name: John Geanakoplos
Teaching Ideas: This video looks at the normal distribution and the professor notes at the end that "fat tails" makes the stock market not normal. He goes over the idea of the normal distribution especially that there are not many outliers. This is a quick application that shows the dangers of the normal distribution used in finance.
Video Link: http://oyc.yale.edu/economics/econ-251/lecture-24
Time: 70:13 to 71:00
University: Yale
Course: Financial Theory
Professor Name: John Geanakoplos
Teaching Ideas: This video shows that the graph of the daily returns of the S&P 500 is very close to normally distributed in 1972. This is a very clear example of a distribution following the normal distribution. One thing to note is that over a longer time, it is not normal since outliers are stronger now and the normal distribution is no longer a good model. This could be from the fact that we know that it should be normal so it isn't.
Video Link: http://oyc.yale.edu/psychology/psyc-110/lecture-13
Time: 23:40 to 25:21
University: Yale
Course: Introduction to Psychology
Professor Name: Paul Bloom
Teaching Ideas: This video explains how the scoring of IQ scores work. The professor speaks of the Normal distribution and presents the empirical rule in the context of IQ scores. This is a nice reinforcement of what is done in a statistics class.
Course Topic: Confidence Intervals
Video Link: http://oyc.yale.edu/economics/econ-252-11/lecture-11
Time: 41:20 to 47:00
University: Yale
Course: Financial Markets
Professor Name: Robert J Shiller (Nobel Laureate)
Teaching Ideas: In this video, the professor first goes over what a 90% confidence interval, gives an example, and then asks the class to guess at a 90% confidence intervals for the world population (in 2011), the weight of the world in metric tons and how many languages there are in the world. This is a great way to explain what a confidence interval is and convince them that confidence intervals are used in other classes. If you just want to show this for the first question, you can stop at 43:43. Much later, he gives the answers, but an instructor can save time and just have the class do the exercise for the first question for the current date, show the correct answer via Google, then then see what percent got it right. Later at 50:10 he explains that people are over confident and fewer then 90% will get their question containing the population mean.
Video Link:
Time: 48:07 to 53:58
University: MIT
Course: The Challenge of World Poverey
Professor Name: Esther Duflo
Teaching Ideas: In this video, the professor discusses an experiment that was done by charging different prices for mosquito nets in Africa. She shows the 95% confidence intervals for each price mark and by comparing these confidence intervals can see whether or not giving free or cheaper mosquito nets results in higher use. The purpose is to look at the best practices in stopping malaria in poverty ridden parts of the world. This is a very meaningful use of confidence intervals.
Course Topic: Hypothesis Testing
Video Link: https://www.youtube.com/watch?v=CNr_7gPhYtY&list=PL2CD836B66D3CEBED&index=17
Time: 37:58 - 39:21
University: UC Berkeley
Course: Biology 1B (2nd Semester Biology)
Professor Name: John Huelsenbeck
Teaching Ideas: This video uses does a great job in explaining what hypothesis testing is all about in the context of seeing if a population follows the Hardy Weinberg Laws of population. It will make a wonderful introduction to hypothesis testing. It doesn't go over any specifics such as Type 1 and 2 errors, p-values, etc.
Video Link: http://oyc.yale.edu/chemistry/chem-125a/lecture-30
Time: 37:30 - 38:33
University: Yale
Course: Freshman Organic Chemistry I
Professor Name: Michael McBride
Teaching Ideas: This video uses the p-value and hypothesis testing to test the claim that an acid reflux medication is effective compared to the over the counter meds. The guest presenter explains that there were two studies done that showed statistical significance and one that showed no difference. This reinforces the idea that a low p-value is not a guarantee. This would be a good time to discuss type one and type two errors and their repercussions.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-4
Time: 48:46 to 51:37
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video looks at three different types of heart disease and compare the risk for those who have high body fat above the waist to those who have low body fat above the waist. The professor notes that the p-value is less than 0.001 for each of these three and states that the results are statistically significant. This is a relevant reinforcement and good explanation of the p-value.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-12
Time: 57:26 to 61:28
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video first shows that the fact that green tea in Japan reduces the risk of heart disease with a very small p-value. Next he points out that although there is statistical significance, there is little practical significance. This is a great example that explains the difference between these two concepts.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-20
Time: 34:41 to 35:56
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell (But Dr. Marlene Schwartz is the guest speaker doing the talking)
Teaching Ideas: This video describes a before an after study where junk food at school was replaced by healthy food. A p-value is shown, but there test statistic is "F" but the speaker does not elaborate on what test was used. This can be an opportunity to ask students what statistical tool might have been used (probably ANOVA).
Video Link: https://www.youtube.com/watch?v=fIB5AE4SRN4&index=9&list=PL0A0E275BC354C934
Time: 1:24:20 to 1:25:49
University: Missouri University of Science and Technology
Course: Engineering Geology and Geotechnics
Professor Name: David Rogers
Teaching Ideas: This video shows the number of storms of different levels and when they occurred. The professor notes that it is very statistically significant that the large storms are becoming more frequent. The p-value is not calculated, but it is clear that statistics has been used to show that in this period of global warming large storms occur more frequently.
Course Topic: Regression Line
Video Link: https://www.youtube.com/watch?v=2z0nrWrcHSA&list=PL2CD836B66D3CEBED
Time: 7:23 - 9:59
University: UC Berkeley
Course: Biology 1B (2nd Semester Biology)
Professor Name: Alan Shabel
Teaching Ideas: This video shows two regression lines. The first is for number of cones produced by the tree vs. the width of the tree rings. The second is the fecundity vs. the probability of survival. This can be either used in a statistics class or a beginning algebra class. The content is basic enough that everybody should be able to understand it.
Video Link: http://oyc.yale.edu/astronomy/astr-160/lecture-16
Time: 12:40 - 14:08
University: Yale
Course: Frontiers and Controversies in Astrophysics
Professor Name: Charles Bailyn
Teaching Ideas: This video looks at the relationship between the redshift and the distance between galaxies. The professor says that the data are strongly correlated to a line and gives the famous Hubble equation that results. This can either be shown in an elementary statistics class to talk about regression analysis or a beginning algebra class to talk about equations of lines that go through the origin.
Video Link: http://oyc.yale.edu/chemistry/chem-125b/lecture-4
Time: 25:12 - 26:56
University: Yale
Course: Freshman Organic Chemistry II
Professor Name: Michael McBride
Teaching Ideas: This video uses a regression line to understand the relationship between Muliken's Electronegativity, the energy required to pull an electron away from a charged atom, and Pauling's Electronegativity, a measure of the difference between cross atom bonds and the average of the same atom bonds. This may be too technical for an elementary statistics class, but in any class, the slope and y-intercept can be discussed.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-1
Time: 19:25 - 20:41
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video shows two scatter plots of trait vs. reproductive success. The first has a strong positive correlation and the second has a weak correlation. He then states that the first is the driver of evolution and the second is the driver of being able to use DNA sequences to infer history. This is a great way to show students the power of regression analysis.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-4
Time: 16:15 - 17:36
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video shows how to use a scatter plot and a regression line to predict the time at which two species shared a common ancestor. This is a straightforward use of regression analysis and clearly demonstrates how the regression line can be used to make predictions.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-11
Time: 13:52 - 14:58
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video displays the scatterplot and regression line to relate the observed age vs. predicted age when an animal will have its first offspring. The predicted age is based on a mathematical model that maximizes theoretical success based on Darwinism. The professor gives the correlation r = 0.93 and is careful to note that this does not imply causation. This can be used in the section of the statistics class where correlation is presented.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-12
Time: 33:50 - 35:15
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video shows the social rank of the mother vs. the lifetime reproductive success for both male and female red deer on the same axes. There is a correlation for the sons but not for the daughter. This gives a nice comparison on what a correlated scatter plot looks like vs. one that is not correlated.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-16
Time: 28:55 - 33:55
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video shows the scatterplot of adult life expectancy vs. male fidelity. The regression line is presented showing a strong correlation that indicates that fidelity is linearly associated with living longer. The correlation is not presented, but students may appreciate that at least for the species of Procellariforms, cheating on your mate does not tend to pay off.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-21
Time: 12:40 - 13:30
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video shows the scatterplot of type 1 diabetes and worm infections. There is a clear negative correlation. The value of r is not shown, but the professor does refer to the negative correlation.
Video Link: http://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122/lecture-25
Time: 11:20 - 12:34
University: Yale
Course: The Nature of Evolution: Selection, Inheritance and History
Professor Name: Stephen C. Stearns
Teaching Ideas: This video shows the scatterplot of temperature and rate of development of insects. The correlation is very strong and positive. The line is shown but r is not. This is an easy to understand example of the regression line used in biology.
Video Link: http://oyc.yale.edu/geology-and-geophysics/gg-140/lecture-24
Time: 15:28 - 17:26
University: Yale
Course: The Atmosphere, the Ocean, and Environmental Change
Professor Name: Ronald Smith
Teaching Ideas: This video displays the connected scatter plot of the amount of global sea ice above the mean vs. year. There is a clear trend downward. The regression line is shown, but the correlation is not. This is a profound example of how regression analysis is used to understand trends.
Video Link: http://oyc.yale.edu/molecular-cellular-and-developmental-biology/mcdb-150/lecture-9
Time: 57:30 - 58:40
University: Yale
Course: Global Problems of Population Growth
Professor Name: Robert Wyman
Teaching Ideas: This video displays a scatterplot of fertility drop vs. how Catholic people were in their voting patterns. There is a clear correlation. The scatterplot and regression line is shown, but the correlation is not discussed. The result is not surprising, but it is can serve as a reminder that one must collect data before making inferences.
Video Link: http://oyc.yale.edu/molecular-cellular-and-developmental-biology/mcdb-150/lecture-13
Time: 12:04- 13:56
University: Yale
Course: Global Problems of Population Growth
Professor Name: Robert Wyman
Teaching Ideas: This video displays a scatterplot of the number of children women want vs. the number of children they have. The video shows both the regression line and R2. The professor compares this line with the line y = x which contains the points that corresponds to the what it would be if women had what they wanted. The difference is around 1. This is also the y-intercept. this video is a nice example of an application of regression analysis and shows an application of the y-intercept.
Video Link: http://oyc.yale.edu/molecular-cellular-and-developmental-biology/mcdb-150/lecture-14
Time: 12:10- 13:05
University: Yale
Course: Global Problems of Population Growth
Professor Name: Robert Wyman
Teaching Ideas: This video displays a scatterplot of the use of contraception and the fertility per country. There is a clear negative correlation. The regression line is drawn, but if the correlation is shown it is too blurry to see. The relationship seems obvious, but until the data is shown it is just a guess.
Video Link: http://oyc.yale.edu/molecular-cellular-and-developmental-biology/mcdb-150/lecture-18
Time: 47:28- 49:00
University: Yale
Course: Global Problems of Population Growth
Professor Name: Robert Wyman
Teaching Ideas: This video displays a scatterplot of the population growth rate and the growth of per capita GDP. The correlation is surprisingly zero. This is a good example of how something everyone expects to be true turns out not to be based on the data. This helped change US policy on family planning. Six minutes later the professor explains that there is a 20 year delay on the economic benefits due to the fact that the children must first become working age.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-13
Time: 57:42 to 58:52
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video shows the scatterplot of Miss America beauty pageant year vs. the winner's BMI. There is a clear negative correlation. The correlation is not presented , but the graph has great impact. An instructor can also explain why this can not be used for long term forecasting.
Video Link: https://www.youtube.com/watch?v=K1sqYL0dVqg&list=PL0A0E275BC354C934&index=7
Time: 47:02 to 49:09
University: Missouri University of Science and Technology
Course: Engineering Geology and Geotechnics
Professor Name: David Rogers
Teaching Ideas: This video explains why colluvium landslides occur. The professor shows a scatterplot and regression line for the strength plot of the hillside. The correlation is not given, but it is clear from the scatterplot that it is strongly correlated. The professors explains that this strain caused the landslides of the Wrightwood area. This is a relevant example that will interest geology majors.
Video Link: https://www.youtube.com/watch?v=fIB5AE4SRN4&index=9&list=PL0A0E275BC354C934
Time: 57:15 to 59:39
University: Missouri University of Science and Technology
Course: Engineering Geology and Geotechnics
Professor Name: David Rogers
Teaching Ideas: This video shows three scatterplots and their corresponding regression lines that give analyses of width, depth, and velocity vs. discharge for sediments on a log scale. The professor explains that the models are not that great with weaker correlations although the actual correlation values are not shown. The professor explains that because of the weak correlations, we have to be careful about trusting the predictions. This is a solid example of how the regression line can and cannot be used to make a prediction.
Video Link: https://www.youtube.com/watch?v=fIB5AE4SRN4&index=9&list=PL0A0E275BC354C934
Time: 1:13:26 to 1:15:59
University: Missouri University of Science and Technology
Course: Engineering Geology and Geotechnics
Professor Name: David Rogers
Teaching Ideas: This video shows a scatterplots and their corresponding regression lines that give analyses of recurrence of storms vs. discharge on a log scale. The professor explains that although the correlation is strong, we have to be careful about using it to predict the future do to the fact that global warming is occurring. This is a nice example of why we always have to be careful about using data to predict the future, since there is always an assumption that there will be no major changes to what is causing the variables.
Video Link: https://www.youtube.com/watch?v=HmstcE_1ZXU&index=14&list=PL0A0E275BC354C934
Time: 1:36:52 to 1:38:08
University: Missouri University of Science and Technology
Course: Engineering Geology and Geotechnics
Professor Name: David Rogers
Teaching Ideas: This video shows the scatterplot and regression line that plots the magnitude of an earthquake vs. its frequency on a reverse logarithmic scale. The professor explains that according to the data, there is an earthquake that is overdue in the Missouri area. This is an interesting use of the regression line that students will relate to.
Course Topic: Regression Analysis
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-12
Time: 55:52 to 58:47
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video displays a complete study on the relationship between green tea consumption and cardiovascular rate and cancer rate. The fine print shows the p-value and confidence intervals for the regression analysis. The professor does not go into the statistics, but it is there on the slide. If you watch through 60:20, the professor explains that it is statistically significant but not practically significant.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-15
Time: 10:59 to 12:30
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell
Teaching Ideas: This video compare the correlations between income and obesity and income and diabetes. The professor briefly explains what correlation means and then explains why the correlation might be larger for diabetes. This is a relevant example that will help students understand that there is a difference between an R-Squared of 0.60 vs. 0.87.
Video Link: http://oyc.yale.edu/psychology/psyc-123/lecture-20
Time: 34:41 to 51:08
University: Yale
Course: The Psychology, Biology and Politics of Food
Professor Name: Kelly Brownell (But Dr. Marlene Schwartz is the guest speaker doing the talking)
Teaching Ideas: This video shows the three correlations between school policy quality and and individually population density, percent at free and reduced lunch, and Democrat/Republican ratio. The p-values are show and the professor explains what this means. This is a nice short clip that can motivate regression analysis.
Time: 35:36 to 36:29
University: MIT
Course: Principles of Chemical Science
Professor Name: Catherine Drennan
Teaching Ideas: This video explains the importance of using scatter plots and regression analysis to decide what model is correct. In particular the professor shows data plotted on both the (t, ln[A]) and the (t, 1/[A]) axes. The first is clearly nonlinear and the second is clearly linear. Not actual calculations are done, but a picture is worth a thousand words.
Time: 43:20 to 49:55 (you can begin at 46:01 to shorten the clip and skip the explanation of the situation at the beginning)
University: MIT
Course: Artificial Intelligence
Professor Name: Patrick Winston
Teaching Ideas: This video goes over the scatterplots of sleep vs. performance in the military and in college. The professor ends the clip with an example of diet soda vs. being fat and notes that a dog or a cat might come to a conclusion that diet soda causes weight gain while a college student can see that being fat causes high consumption of diet soda. This is a nice example of being careful about correlation and causation.
Course Topic: Chi Squared Test
Video Link: https://www.youtube.com/watch?v=z_fOdaNTppI&list=PL0A0E275BC354C934&index=8
Time: 0:05 to 3:50
University: Missouri University of Science and Technology
Course: Engineering Geology and Geotechnics
Professor Name: David Rogers
Teaching Ideas: This video demonstrates the use of the Chi Square Goodness of Fit Test in order to show that the geologic data given is not Normal. The professor shows how this is done by hand. He carries out some but not all the steps and does not fully complete the calculations. This is a direct use of the Chi Square test in geology.
Course Topic: ANOVA
Video Link: https://www.youtube.com/watch?v=oADF0Bit1Yw&t=3579s
Time: 38:42 to 43:27
University: UCLA
Course: Families and Couples
Professor Name: Benjamin Carney
Teaching Ideas: This video shows a study that was done to see if there is evidence to support the claim that income is a factor in what people think about marriage and divorce. There is a p-value give, but the professor does not explicitly state that he used ANOVA. The instructor can explain that this is what was used and why.