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denis-greek [22]
3 years ago
9

V = LWH Solve for W.

Mathematics
2 answers:
kvv77 [185]3 years ago
8 0
W = v / lh
Volume / length(height) 

If that's what you're asking for lol
inessss [21]3 years ago
4 0
If you <span>are trying to solve for w, all you have to do is get l(length) and h(height) over to the left side to solve for w. By doing so, you just divide since you want to do the opposite and you will arrive at a final answer of w = v / lh.</span>
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Ellen bought 3 greeting cards for $3 each.
Ne4ueva [31]

Answer:

(3×3)+2=11

$11

Step-by-step explanation:


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3 years ago
Someone please answer !!!!
Goryan [66]
The answer is C

By the way, that expression means "n choose k". Hope I helped!
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In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti
kipiarov [429]

Answer:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

Step-by-step explanation:

Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".

Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"

The linear model is given by the following equation Y=mx +b, where y is the dependent variable, x the independent variable, m the slope and b the intercept.

For this case we have the following info given:

\sum_{i=1}^n (x_i -\bar x)^2 =98775

\sum_{i=1}^n (y_i -\bar y)^2 =19.4

\bar x =26.36

\bar y =0.5188

n= 40  represent the sample size

\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y) =796.94

The total sum of squares is given by this formula:

SST= \sum_{i=1}^n (y_i -\bar y)^2 =19.4

And from the formulas for a simple regression, we need to calculate the slope for the regression like this:

m=\frac{\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)}{\sum_{i=1}^n (x_i -\bar x)^2}

And if we replace the values given we have:

m=\frac{796.94}{98775}=0.008068

We have another useful equation in order to find the sum of squares for the regression, given by:

SSR=m* \sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)=0.008068*796.94=6.430

And we know this equivalence:

SST= SSR+SSE

And solving for the sum of squares for the error we have:

SSE=SST-SSR=19.4-6.430=12.97

So then we have the final solutions:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

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kobusy [5.1K]

Answer:

it's the first one girl! :)

Step-by-step explanation:

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The answer is dividing each bottle size by its cost I took the test and I got it right <span>
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