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NISA [10]
3 years ago
11

Mr. small has 5 crates of oranges which total of 182 1/2 lb. each crate contains an equal weight and no crate can exceed 35 lbs.

when shipped.
A. are the crates within the limit of 35 lb shipped each


B. how many pounds are they above or below the weight of the 35. lb each​
Mathematics
1 answer:
In-s [12.5K]3 years ago
6 0

Answer:

real n1qa

Step-by-step explanation:

real n1qa i dont know the question but hello mate u a real n1qa

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What the range of 80,84,86,87,90,91,92,94,100
inessss [21]

Answer:

20

pls mark brainliest if possible

Step-by-step explanation:

the range is the difference between the values of the largest and smallest values. You can figure this out with subtraction.

Largest number: 100

Smallest number: 80

Range: 20

6 0
3 years ago
What is the classification of the square root of 36
Makovka662 [10]
The square root is 6 because 6*6=36
5 0
3 years ago
Read 2 more answers
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

6 0
3 years ago
Which statements are true about these lines? Select three options.
shepuryov [24]
RS is perpendicular to MN and PQ.


We can use the slopes of these lines to determine the answer.
Slope is given by the formula
m=.

Using the coordinates for M and N, we have:
m=.

Since PQ is parallel to MN, its slope will be as well, since parallel lines have the same slope.

Using the coordinates for points T and V in the slope formula, we have
m=.

This is not parallel to MN or PQ, since the slopes are not the same.
We can also say that it is not perpendicular to these lines; perpendicular lines have slopes that are negative reciprocals (they are opposite signs and are flipped). This is not true of TV either.

Using the coordinates for R and S in the slope formula, we have
m=. Comparing this to the slope of RS, it is flipped and the sign is opposite; they are negative reciprocals, so they are perpendicular.
8 0
3 years ago
Read 2 more answers
Which sequence of transformations would maintain congruent ?
MakcuM [25]
218they are the annnns erv
7 0
3 years ago
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