Which of the following best describes a single fixed point that is the same
distance from the parabola as the directrix is from the parabola?
A. Center
B. Focus
C. Vertex
D. Locus

1.3.25 question suppose 40​% of all voters voted for a particular candidate. to simulate exit polls regarding whether or not voters voted for this​ candidate, five random samples of size 1010 and five random samples of size 10001000 have been generated using technology using a population proportion of 0.400.40​, with the accompanying results. complete parts a through c below. click the icon to view the simulation results. a. observe how the sample proportions of successes vary around 0.400.40. simulation compared to sample prop simulation compared to sample prop 1 ▾ 6 ▾ greater than 0.40 less than 0.40 equal to 0.40 2 ▾ less than 0.40 greater than 0.40 equal to 0.40 7 ▾ greater than 0.40 less than 0.40 equal to 0.40 3 ▾ greater than 0.40 less than 0.40 equal to 0.40 8 ▾ equal to 0.40 greater than 0.40 less than 0.40 4 ▾ greater than 0.40 equal to 0.40 less than 0.40 9 ▾ less than 0.40 greater than 0.40 equal to 0.40 5 ▾ equal to 0.40 less than 0.40 greater than 0.40 10 ▾ equal to 0.40 greater than 0.40 less than 0.40 click to select your answer(s) and then click check answer. 2 parts remaining clear all check answer

When you dilate a figure with a scale factor of 1/2, what will the dilated figure look like?                                                                                           a: the dilated figure will have side lengths that are half the length of the original side lengths.                                                                          b: the angles in the dilated figure will be half the measure of the original figure.                                                                                c: the dilated figure will have side lengths twice the length of the original.

Consider the linear model for a two-stage nested design with b nested in a as given below. yijk=\small \mu + \small \taui + \small \betaj(i) + \small \varepsilon(ij)k , for i=1,; j= ; k=1, assumption: \small \varepsilon(ij)k ~ iid n (0, \small \sigma2) ; \small \taui ~ iid n(0, \small \sigmat2 ); \tiny \sum_{j=1}^{b} \small \betaj(i) =0; \small \varepsilon(ij)k and \small \taui are independent. using only the given information, derive the least square estimator of \small \betaj(i) using the appropriate constraints (sum to zero constraints) and derive e(msb(a) ).