Archive for April 15th, 2007

From Mrs. Chiang’s Szechwan Cookbook: Szechwan Home Cooking.

Grand Duke Chicken

1/2 c. peanuts

1 whole chicken breast

Marinade
4 t. dark soy sauce
1/2 t. sugar
1 t. each: sesame oil and sherry
1 egg white
1 T. cornstarch
2 green onions

3 bell peppers (or try Hungarian wax peppers)
10 cloves garlic
1/2 inch ginger
5 chilis
1/2 t. salt

1 T. dark soy sauce
oil

Cut the chicken into medium sized cubes and toss into a large bowl. Cut the green onions into pieces about the same size as the chicken and add them to the bowl. Mix the remaining ingredients, pour over the chicken and green onions, mix well, and marinate for at least an hour.

Mince the garlic and ginger, then stem the chilis and tear into pieces. Stem and seed the bell peppers, then cut into cubes about the same size as the chicken. Put a wok over very high heat until smoking hot. Add a tablespoon or so of oil, swirl to coat the wok, then add the peppers and flip over high heat for about 45 seconds. Add the salt. and flip for another 45 seconds. Remove the peppers and reserve.

Add 1/4 c. oil, then add the garlic, ginger, and chilis. Stir and cook over high heat for 30 seconds. Stir the chicken and add all at once, stir and flip rapidly as you cook it over high heat for about a minute, until it goes white. Add the peppers and the tablespoon of soy sauce, and cook for another minute. Mix in the peanuts (I always drizzle with a bit of sesame oil) and serve with rice.

Dragon Lady published the weather map for her area, so here’s our weather map:

Note that I have friends who are weather junkies and I make fun of them for continually looking at real-time weather maps, so I’m opening myself up to abuse. Also note that yesterday, they were predicting inches of snow (as much as a foot on one forecast). Also note that despite the rain, here on the ridge, it’s only drizzling.

Of course.

The AFT publishes the mean teacher salaries by state, so I thought I’d analyze the data and see if there were any relationship between the mean teacher salaries and the percentage proficient or above in math and reading. Just glancing at the data leads one to suspect there may not be. Here are the three states that pay the highest teacher salaries and the three that pay the lowest:

State
2003-04 Mean Salary
% US Mean
Proficient or Above Math
Proficient or Above Reading
Connecticut $56,516 121.3% 34.6 33.6
California $56,444 121.1% 21.8 20.5
New York $55,181 118.4% 30.8 33.5
North Dakota $35,411 76.0% 34.6 36.5
Oklahoma $35,061 75.2% 20.6 25.3
South Dakota $33,236 71.3% 36.5 35.1

Note that South Dakota pays the lowest mean teacher salaries, yet has the highest percentage of students proficient or above in math, and in reading, is second only to North Dakota, which pays the third lowest mean teacher salaries. Also note the difference between the percentage proficient or above in math and reading between California, which pays the second highest teacher salaries, and Connecticut and New York, which pay the highest and third highest teacher salaries, respectively.

But let’s run regressions on the data to see if there is a statistically significant relationship between mean teacher salary and percentage proficient or above in both skills. First, math:

Regression: 2003-04 Mean Teacher Salary, % Proficient+ Math
Multiple R 0.23431406
R Square 0.05490308
Adjusted R Square 0.03521356
Standard Error 6.99589092
Observations 50
ANOVA df SS MS F Significance F
Regression 1 136.473288 136.473288 2.78844187 0.10145623
Residual 48 2349.23951 48.9424898
Total 49 2485.7128      
  Coefficients Standard Error t Stat P-value  
Intercept 17.3262997 6.93479542 2.49845867 0.01595078
2003-04 Mean Salary 0.00026116 0.0001564 1.66986283 0.10145623  

First, note that the correlation coefficient for the two variables is only 0.23431406, and that the variation in mean teacher salary only accounts for 5.49% of the variation in the percentage proficient or above in math. But the p-value for the regression is 0.10145623, and because it is greater than 0.05, these data support no statistically significant relationship between the mean teacher salary and the percentage of students proficient or above in math.

Now, reading proficiency:

Regression: 2003-04 Mean Teacher Salary, % Proficient+ Reading
Multiple R 0.16902722
R Square 0.0285702
Adjusted R Square 0.00833208
Standard Error 6.1429964
Observations 50
ANOVA
  df SS MS F Significance F
Regression 1 53.2725707 53.2725707 1.41170234 0.24061977
Residual 48 1811.34743 37.7364048
Total 49 1864.62      
  Coefficients Standard Error t Stat P-value  
Intercept 22.9389444 6.08934927 3.76706005 0.00045145
2003-04 Mean Salary 0.00016317 0.00013733 1.1881508 0.24061977  

Here, the correlation coefficient is even lower (0.16902722), and teacher salary accounts for only 2.86% of the variation in the percentage of students proficient or above in reading. The p-value for the regression is greater than 0.05 (0.24061977), so these data also do not support any statistically significant relationship between teacher salary and the percentage of students proficient or above in reading.

There could be a relationship, of course, that’s hidden by aggregating the data by state. If we had, say, the mean teacher salaries and percentage of students proficient or above in math and reading data aggregated by school district, we might see a statistically significant relationship that is concealed by aggregating the data by state. But these data cannot support the argument that increasing teacher salaries will increase the quality of education.

There are, of course, other arguments for raising teacher salaries. But it’s a good idea not to use an argument that can easily be demonstrated to be false.