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says:
May 25, 2016 at 1:42 pm

I hope the professors on the list see the humor in using dummy variables for professors.

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says:
May 26, 2016 at 3:03 am

I think they’d call them ‘indicator variables’ instead

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says:
May 27, 2016 at 2:30 am

If they’re making a cross-class comparison, then yes, essays might not be a useful measurement because of changing the professor, but that doesn’t necessarily mean that essays are useless for cross-student, within-class comparisons. I’d still be wary of essays as a measurement technique just because if they can’t be validated across professors, then that is evidence that the measurement is not valid within a class as well.

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says:
May 25, 2016 at 11:18 am

Somebody can undoubtedly do a better job of explanation, but my take is that the professor grades the essays and sees prior test scores, so it is possible that there is a bias due to that. So, the professor’s identity is included in the model and has a significant effect on the scores. Hence, the unreliability of essay exams as measures of student achievement!

Of course, there is no other reason why the professor might make a difference and it is not possible that the difference would emerge on essay questions but not on multiple choice or short answers – which is why I’m glad I am nearing the end and not the beginning of my career. All this time I thought I made a difference and that essay exams were the best way to see that. How foolish of me.

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says:
May 25, 2016 at 12:08 pm

I’ll be the devil’s advocate here and say, a contrario, the effects are not small, the sample sizes are not small either (700 total, >200 in each of three subgroups), and there is no evidence of fishing for significance (specification searching) by testing multitudes of possible effects and reporting only that which is significant. It seems to me a straightforward and well done experimental design based on a simple and plausible idea. If this isn’t good enough, I don’t know what is.

But I will grant you that they probably over-interpret the results. Sadly, this is how research gets published. The Dragnet approach (“Just the facts ma’am”) doesn’t lead to publications. You need a story, and that often means over-interpreting.

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It’s officially summer, but the weather is still calling for a warm and cozy meal! This one-pot meal is easy to cook, easy to clean up afterwards, and makes enough for leftovers. Serve with a mixed green salad and you’re done! One Pot Cheesy Mexican Lentils, Black Beans and Rice 1/2 of a yellow onion, diced 1 clove of garlic minced 1/2 cup uncooked long grain brown rice 1 cup canned lentils, rinsed and drained 2-3 cups low sodium vegetable stock 1 teaspoon chili powder 1/2 teaspoon ground cumin 1/4 teaspoon ground chipotle powder (optional) 15 ounce can black beans, rinsed and drained 1/2 cup fire roasted tomatoes, drained 4 ounce can diced green chiles 1 small zucchini, shredded 1/2 cup defrosted frozen corn (or use canned) 1 cup red enchilada sauce 3/4 cup shredded cheese (sharp cheddar) Cilantro for garnish (optional) Salt and pepper, to taste Plain Greek yogurt (optional, for topping) Heat a large pot or saucepan over medium high heat and drizzle with olive oil. Add the onion and garlic. Sauté for a couple minutes then add in the brown rice. Add in the vegetable stock, stir and bring to a boil. Once boiling, cover and reduce the heat to low. Simmer for 30-40 minutes, until rice is tender. If there is any excess liquid, drain it. Season the rice with salt, pepper, chili powder, cumin and chipotle powder. Add in all of the remaining ingredients except 1/4 cup of the cheese and stir together. Heat the mixture until the cheese is melted. Top individual bowls with a sprinkle of cheese and a large dollop of plain Greek yogurt. Recipe adapted from: reciperunner.com/one-pot-cheesy-mexican-lentils-black-beans-and-rice/
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Some functions with branch cut detection are available as builtins: see acb_sqrt_analytic() , Salvatore Ferragamo Joy Leopard Print Ballet Flats 007nnhNb7
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. It is not difficult to write custom functions of this type, using the following pattern:

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, acb_real_max() , acb_real_min() provide piecewise holomorphic functions that are useful for integrating piecewise-defined real functions.

For example, here we define a piecewise holomorphic extension of the function f ( z ) = z f ( z ) = z (for simplicity, without implementing derivatives):

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may be slightly better if it is known that z will be nonnegative on the path.)

See the demo program examples/integrals.c for more examples.

Computes a rigorous enclosure of the integral

where f is specified by ( func , param ), following a straight-line path between the complex numbers a and b . For finite results, a , b must be finite and f must be bounded on the path of integration. To compute improper integrals, the user should therefore truncate the path of integration manually (or make a regularizing change of variables, if possible). Returns ARB_CALC_SUCCESS if the integration converged to the target accuracy on all subintervals, and returns ARB_CALC_NO_CONVERGENCE otherwise.

By default, the integrand func will only be called with order = 0 or order = 1; that is, derivatives are not required.

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