A rectangle has vertices W(2,3), X(2,6), Y(7,6), and Z(7,3).

Mathematics

1 answer:

KiRa [710]2 years ago

4 0

Answer:

Step-by-step explanation:

A rectangle has 4 sides.

2 of them are lengths and 3 of them are widths.

We can simply use coordinate geometry (without graphing) to find side lengths of the rectangle. We will use Distance Formula.

We can find all the 4 lengths by using Distance Formula from points:

W and X

X and Y

Y and Z

W and Z

Note, that we don't need to find all 4 of them individually, because 2 are lengths (same) and 2 are widths (same). Thus we can find

Distance of WX, which would be same as distance of YZ

also

Distance of XY which would be same as distance of WZ

Note: Distance Formula is $D=\sqrt{(y_2-y_1)^2+(x_2-x_1)^2}$ where D is the distance, x_1, y_1 is the first coordinate points and x_2,y_2 is the second coordinate points

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pickupchik [31]

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Given nothing and that we need to explain standard deviation. margin of error, correlation coefficient .

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In statistics, the standard deviation may be a measure of the number of variation or dispersion of a group of values. an occasional variance indicates that the values tend to be near the mean of the set, while a high variance indicates that the values are detached over a wider range.

Formula: $\sqrt{(x-x bar)^{2}/N }$

where x bar is mean and N is size of population.

Margin of error

The margin of error may be a statistic expressing the quantity of sampling error within the results of a survey. The larger the margin of error, the less confidence one should have that a poll result would reflect the results of a survey of the complete population.

Formula for M=z*s/ $\sqrt{n}$

here z is z value of Z score , s is variance , n is that the sample size.

Correlation coefficient

In statistics, the Pearson parametric statistic ― also called Pearson's r, the Pearson product-moment parametric statistic, the bivariate correlation, or colloquially simply because the coefficient of correlation ― could be a measure of linear correlation between two sets of information.

Formula=∑ $(x_{i} -x bar)(y_{i} -y bar)/\sqrt{}$ ∑ $(x_{i} -x bar)^{2}$ ∑ $(y_{i}-ybar) ^{2}$