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Charra [1.4K]
2 years ago
14

DOES the given value make the inequalities true? x + 9 > 21, when x = 15?

Mathematics
2 answers:
Kobotan [32]2 years ago
7 0

Answer:

Yes.

Step-by-step explanation:

When x=15,

x+9=15+9=24 > 21.

r-ruslan [8.4K]2 years ago
6 0

Answer:

Yes this is true

Step-by-step explanation:

24 is greater than 21; which means this statement is always true.

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Help with these two figured and you shall be granted points. Good deal, half off.
STALIN [3.7K]

Answer:

5. C - 180ft²

6. D - 120m³

Step-by-step explanation:

5.

(6.5 x 11) = 71.5

(6 x 2.5) = 15

(6 x 11) = 66

(2.5 x 11) = 27.5

6.

((7.5 x 4)/2)(8) = 120

7 0
3 years ago
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
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Answer:

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Step-by-step explanation:

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64 \div 4 = 16

16 - 3 = 13

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3 years ago
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