Well, let's take a peek at the graph of that line, hmmm let's pick two points, heck, those tow on the extremes anyway, and those are (-5,2) and (5,-1), alrite.. now, let's do some checking on that.
![\bf \stackrel{\textit{point-slope form}}{y-{{ y_1}}={{ m}}(x-{{ x_1}})}\implies y-2=-\cfrac{3}{10}[x-(-5)] \\\\\\ y-2=-\cfrac{3}{10}(x+5)\implies y-2=-\cfrac{3}{10}x-\cfrac{3}{2}\implies y=-\cfrac{3}{10}x-\cfrac{3}{2}+2 \\\\\\ y=-\cfrac{3}{10}x+\cfrac{1}{2}](https://tex.z-dn.net/?f=%5Cbf%20%5Cstackrel%7B%5Ctextit%7Bpoint-slope%20form%7D%7D%7By-%7B%7B%20y_1%7D%7D%3D%7B%7B%20m%7D%7D%28x-%7B%7B%20x_1%7D%7D%29%7D%5Cimplies%20y-2%3D-%5Ccfrac%7B3%7D%7B10%7D%5Bx-%28-5%29%5D%0A%5C%5C%5C%5C%5C%5C%0Ay-2%3D-%5Ccfrac%7B3%7D%7B10%7D%28x%2B5%29%5Cimplies%20y-2%3D-%5Ccfrac%7B3%7D%7B10%7Dx-%5Ccfrac%7B3%7D%7B2%7D%5Cimplies%20y%3D-%5Ccfrac%7B3%7D%7B10%7Dx-%5Ccfrac%7B3%7D%7B2%7D%2B2%0A%5C%5C%5C%5C%5C%5C%0Ay%3D-%5Ccfrac%7B3%7D%7B10%7Dx%2B%5Ccfrac%7B1%7D%7B2%7D)
I WILL UPVOTE!!:p:p:p<3
1 answer:
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Answer: Normal approximation can be used for discrete sampling distributions, such as Binomial distribution and Poisson distribution if certain conditions are met.
Step-by-step explanation: We will give conditions under which the Binomial and Poisson distribitions, which are discrete, can be approximated by the Normal distribution. This procedure is called normal approximation.
1. Binomial distribution: Let the sampling distribution be the binomial distribution , where
is the number of trials and
is the probability of success. It can be approximated by the Normal distribution with the mean of
and the variance of
, denoted by
if the following condition is met:
2. Poisson distribution: Let the sampling distribution be the Poisson distribution where
is its mean. It can be approximated by the Normal distribution with the mean
and the variance
, denoted by
when
is large enough, say
(however, different sources may give different lower value for
but the greater it is, the better the approximation).