Kerbal Space Program 101 - Tutorial For Beginners - Construction, Piloting, Orbiting. Introverts by Jade. English Jade - Learn English (engVid) Are you learning English in a way that suits your personality and makes you an effective learner? When you learn in a way that fits your personality, you will enjoy your studies more and progress faster. Find out the different ways introverts and extroverts like to learn. If you don't know if you're an introvert or extrovert, and what that means exactly -- I explain all you need to know.
Once you know more about your personality and whether you are mostly introverted or extroverted, you can find out what your language-learning strengths and weaknesses are. Remember -- it's not good or bad to be an extrovert or introvert. We're just different! Take the quiz on this lesson here: to both my channels:English Jade: Joddle: TRANSCRIPT:Hi everyone. But, first of all, for today's lesson, I've got a confession to make. Panel Data Models - Econometrics Academy. Panel data models provide information on individual behavior, both across individuals and over time. The data and models have both cross-sectional and time-series dimensions. Panel data can be balanced when all individuals are observed in all time periods or unbalanced when individuals are not observed in all time periods. Examples include estimating the effect of education on income, with data across time and individuals; and estimating the effects of income on savings, with data across years and countries.
Panel data models: topics covered Panel data characteristics, panel data typesVariation types (overall, within, and between variation)Panel data models (pooled model, fixed effects model, and random effects model)Estimator properties (consistency and efficiency)Estimators (pooled OLS, between, fixed effects, first differences, random effects)Tests for choosing between models (Breusch-Pagan LM test, Hausman test)
Lagrange multiplier. Figure 1: Find x and y to maximize f(x, y) subject to a constraint (shown in red) g(x, y) = c. Figure 2: Contour map of Figure 1. The red line shows the constraint g(x, y) = c. The blue lines are contours of f(x, y). The point where the red line tangentially touches a blue contour is our solution. Since d1 > d2, the solution is a maximization of f(x, y). For instance (see Figure 1), consider the optimization problem maximize f(x, y) subject to g(x, y) = c. We need both f and g to have continuous first partial derivatives.
Where the λ term may be either added or subtracted. Introduction[edit] One of the most common problems in calculus is that of finding maxima or minima (in general, "extrema") of a function, but it is often difficult to find a closed form for the function being extremized. Consider the two-dimensional problem introduced above: subject to g(x, y) = c We can visualize contours of f given by f(x, y) = d for various values of d, and the contour of g given by g(x, y) = c. for some λ. Statistics with R: Box-Cox transformation of the response in a linear regression model part 2. Dynamic Periodic Table. L'histoire de l'Europe - Part 2/4. Big History Project. Free online courses/MOOC aggregator - Class Central. Electrostatics (part 2) | Electricity and magnetism. On "super-professors" and the MOOC pushback. As with all new things that receive a lot of (arguably "too much") positive press, the backlash necessarily ensues.
So it is now with MOOCs. Early, simplistic pushback came in the form of noting the lack of the student-professor interaction possible in a live classroom (e.g., this NY Times article and associated NPR interview). As statistics came out on the early MOOCs, attention focused heavily on poor completion/ high "failure" rates (e.g., this Money magazine article). The most recent negativity is different and warrants more attention, highlighted recently in a letter by San Jose State University professors to a MOOC-teacher. Specifically, there's an assertion by several in academia now that professors (or, as negative writers like to hyperbolize, "super-professors") who teach MOOC courses are providing a tool that reckless universities are using to dismantle departments, reduce costs by hiring cheaper, non-expert teachers, etc.
I'll present my thoughts on each of these, in turn. Lak12 - home. Understanding Education through Big Data. The seduction of ‘Big Data’ lies in its promise of greater knowledge. The large amounts of data created as a by-product of our digital interactions, and the increased computing capacity to analyse it offer the possibility of knowing more about ourselves and the world around us. It promises to make the world less mysterious and more predictable. This is not the first time that new technologies of data have changed our view of the world. In the nineteenth century, statistical ‘objective knowledge’ supplanted the personal knowledge of upper-class educated gentlemen as the main way in which governments came to know about those they governed.
In our own time we seem to be facing a new revolution in which the basis of how we come to ‘know’ something – our epistemological foundations – is becoming reliant on big data analysis. Learning Analytics It is not new that educational institutions collect and analyse data for predicting and intervening in children’s educational performance.
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