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Danny Boy (Ireland)
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Arirang (Korea) |
Sakura (Japan) |
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Cielito Lindo (Mexico)
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Greensleeves (England)
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La Marseillaise (France) |
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Guantanamera (Cuba)
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Sweet Mother (Nigeria) |
Volare (Italy) |
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Jambo Bwana (Kenya)
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Nkosi Sikelelei Afrika (South Africa) |
La Garota de Ipanema (Brazil) |
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Asedayo Ya Me Ne Dya (Ghana)
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Ngoromera (Zimbabwe) |
Arroro Mi Nino (Latin America) |
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Prokarekareana (New Zealand)
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Waltzing Matilda (Australia) |
Var Vindar Friska/ Sweden |
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Shalom Chaverim (Israel)
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Im Argau (Switzerland) |
Loch Lomond, Auld Lang Sein (Scotland) |
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Mo Li Hua, Si Ji Ge Yue Liang Dai Biao Wo
China |
Saudade
Cape Verde Islands |
Berber Algeria
Idir, Adrar Inu
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Category Archives: Matrices
Physics: Case study Matrix – Flight
| The Angle of Attack | Action/Reaction | Pressure Differential |
|---|---|---|
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The Hand Outside the Car Window
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The Balloon letting air out |
Ping Pong ball and Hair Dryer |
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Little Angles matter: Tilt of earth, etc.
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The Astronaut/Skater |
Bernouilli’s Principle |
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The Ailerons
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The Engines |
Flying Upside Down Shape of Wing
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Quantitative Literacy
Do the numbers really mean what they seem to?
Race Gender Class
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Unadjusted number that suggests discrimination or injustice.
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Blacks 12% of pop 40% of prisoners Or 50% of those stopped and frisked |
Women make $.77 on the dollar |
Top 1% of households make 20% of income -suggests injustice |
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What other factors might account for the differential?
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Could crime rate differentials account for the differentials?
Could the war on drugs itself not racism be the real problem? Could family structure inequality be a factor? |
Could preference for flexible hours, or lower paying care-giving professions account for most of the difffential? |
Should the numbers be adjusted for hours worked? Workers per household? Age? Productivity? So what?
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Facts and questions |
Women are 50% of pop only 5% of prisoners.
Sexism? |
Adjusting for these Harvard economist Claudia Goldin
finds the gap virtually non-existent.
Is she wrong? |
The top 1% pay 40% of income taxes – twice their share of income.
Is that fair? Who decides? How? Is meritocracy bad? Is the real problem equality of opportunity not inequality of income? |
Visual Literacy
| Photography | Drawing | Painting |
|---|---|---|
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Composition:
Rule of Three
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The Picture Plane (Durer, Van Gogh) |
The Color Wheel |
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Lighting:
Time of Day, Fog
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The Upside Down Drawing
And
Negative Spaces
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Different paints Different textures |
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Aperture
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The Basic Unit |
Light and Shadow |
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Portfolio: Portraits, still lives, landscapes
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Portfolio Still life, landscape, portrait |
Portfolio Still life, landscape, Portrait |
Statistical Literacy
#1 #2 #3
| Descriptive Statistics:Pictures are worth a thousand numbers as well as a thousand words. |
Why a histogram is better than a mean or median or even a five number summary of a set of data. | Why a scatter plot is better than an Rsquared or a
Regression Equation in summarizing the relationship between two sets of data. |
Judgment is key to adjusting the axes of the histograms and scatter plots to maximize the quality of information |
| The average American has one testicle and one ovary. | Gathered data is not always good data | Correlations are not causation | Most important may be ignored by the analyst |
| Has the data been massaged? Are the outliers there? | The most important facts may not be quantifiable. | Problem sets should be prioritized by civic or personal relevance. | Failure to do so is a recipe for amnesia, boredom, and poor performance. |
| Inferential Statistics:
All about randomness, probability, and sample size
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Randomness is key to getting a good sample | The bigger the sample size the closer and more confident you can be in generalizing. | Roughly: a random sample of 100: 95% confident, plus or minus 10%.
Sample of 1200” 95% confident Plus or minus 3% |
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Beware the file drawer problem!
Beware Type 1 and Type 2 Errors |
Probability is the key to statistical experiments.
Has the experiment been reproduced? How many times? |
Perfect analogy is to the jury system. As the jury should assume innocent, so the statistician assumes no effect
(null hypothesis) |
Then calculates odds of getting actual result from chance alone. If extremely rare then, rejects the null hypothesis |
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Data omission and factor omission are likely when issue has a partisan dimension.
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P values are arbitrary. P values should be stated a priori. P values should be thought about. |
Chi square calculations can be completely misleading.
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Simpson’s Paradox is a warning to make sure all the data has been disclosed.
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| Finding Right Metric key | Best hitter: is batting average the right number?
Is Z-score better than absolute? |
Finance: absolute or relative performance? risk-adjusted or not,
But how? Sharpe? |
Justice: do women make $.77 on the dollar? What does this mean? Are you sure? |
Statistical Literacy -2
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Level One |
The uncertain can often be predicted with amazing certainty. | The laws of chance lead often to extremely counter-intuitive results. | Data can be misleading and decisions based on them false. |
| Quantification can lead to the double illusion of importance and objectivity, | The most important factors may not be quantifiable. | Most complex problems require non-quantitive judgment. | |
| Statistical wizardry is no substitute for substantive knowledge. | Experiments should be reproduced multiple times. | The bigger the sample the lower the standard deviation. | |
| Level Two | 1111 is a good sample size –
which is not a function of the population – the tasting soup analogy |
P values are arbitrary but should be decided on before experiments are conducted. | For what is a p value of 5% a good decision rule? Guilt or innocence? |
| The inevitability of Type 1 and Type 2 errors | Studies should be based on random samples.
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Experiments should be double blind and controlled. | |
| Regression to the man, the Placebo effect, and the Hawthorne effect can be big | Adjusting data is often necessary but can be extremely misleading. | CPI adjustment is critical but fails to account for quality improvement. | |
| Extrapolation is almost irresistible: budgets, stocks,
Climate. |
Partisan bias can distort data collection, experimental design. | Only 40% of social science experiments are ever repeated.
Is this science? |