The answer is true to all of them.
Check the picture below.
so we can say that two sides are "4" each in length, since opposite sides are equal, let's find how long the slanted sides are.
![~~~~~~~~~~~~\textit{distance between 2 points} \\\\ (\stackrel{x_1}{-4}~,~\stackrel{y_1}{2})\qquad (\stackrel{x_2}{3}~,~\stackrel{y_2}{5})\qquad \qquad d = \sqrt{( x_2- x_1)^2 + ( y_2- y_1)^2} \\\\\\ d=\sqrt{[3 - (-4)]^2 + [5 - 2]^2}\implies d=\sqrt{(3+4)^2+3^2} \\\\\\ d=\sqrt{49+9}\implies d=\sqrt{58} \\\\[-0.35em] ~\dotfill\\\\ \stackrel{\textit{\Large Perimeter}}{4~~ + ~~4~~ + ~~\sqrt{58}~~ + ~~\sqrt{58}\implies 8+2\sqrt{58}}](https://tex.z-dn.net/?f=~~~~~~~~~~~~%5Ctextit%7Bdistance%20between%202%20points%7D%20%5C%5C%5C%5C%20%28%5Cstackrel%7Bx_1%7D%7B-4%7D~%2C~%5Cstackrel%7By_1%7D%7B2%7D%29%5Cqquad%20%28%5Cstackrel%7Bx_2%7D%7B3%7D~%2C~%5Cstackrel%7By_2%7D%7B5%7D%29%5Cqquad%20%5Cqquad%20d%20%3D%20%5Csqrt%7B%28%20x_2-%20x_1%29%5E2%20%2B%20%28%20y_2-%20y_1%29%5E2%7D%20%5C%5C%5C%5C%5C%5C%20d%3D%5Csqrt%7B%5B3%20-%20%28-4%29%5D%5E2%20%2B%20%5B5%20-%202%5D%5E2%7D%5Cimplies%20d%3D%5Csqrt%7B%283%2B4%29%5E2%2B3%5E2%7D%20%5C%5C%5C%5C%5C%5C%20d%3D%5Csqrt%7B49%2B9%7D%5Cimplies%20d%3D%5Csqrt%7B58%7D%20%5C%5C%5C%5C%5B-0.35em%5D%20~%5Cdotfill%5C%5C%5C%5C%20%5Cstackrel%7B%5Ctextit%7B%5CLarge%20Perimeter%7D%7D%7B4~~%20%2B%20~~4~~%20%2B%20~~%5Csqrt%7B58%7D~~%20%2B%20~~%5Csqrt%7B58%7D%5Cimplies%208%2B2%5Csqrt%7B58%7D%7D)
Answer:
its option c kskskwoodid djekeo e ekekwl skewered dokekwowkwkw ekekekkr
Answer:
a.is approximately normal because of the central limit theorem.
Step-by-step explanation:
The central limit theorem states that if we have a population with mean μ and standard deviation σ and we take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed.
For any distribution if the number of samples n ≥ 30, the sample distribution will be approximately normal.
Since in our question, the sample of observations is 50, n = 50.
Since 50 > 30, then <u>our sample distribution will be approximately normal because of the central limit theorem.</u>
So, a is the answer.
