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Nikolay [14]
3 years ago
7

Find the minimum value of C = 6x + 7y Subject to the following constraints: x ≥ 0 y ≥ 0 4x + 3y = 24 x + 3y > 15​

Mathematics
1 answer:
SIZIF [17.4K]3 years ago
4 0

Answer:

Step-by-step explanation:

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Brainliest if correct
Bad White [126]

Answer:

(-2,11) (0,3) (2,-5) (4,-13)

Step-by-step explanation:

used desmos calculator

8 0
2 years ago
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(6 to the power of 3 times 3 to the power of 3) to the power of 2
frosja888 [35]

Answer:

True

Step-by-step explanation:

(6³ * 3³)² = 18⁶

Multiply inside the parenthesis.

(18³)² = 18⁶

When you put powers to powers, they multiply, eg:

(x³)⁴ = x³°⁴ = x¹²

18⁶ = 18⁶

True.

4 0
3 years ago
Below is the frequency distribution of the weights (in kg) of some sample of teen-agers.
julia-pushkina [17]

Usually, the terms “data” and “information” are used interchangeably. However, there is a subtle difference between the two.

In a nutshell, data can be a number, symbol, character, word, codes, graphs, etc. On the other hand, information is data put into context. Information is utilised by humans in some significant way (such as to make decisions, forecasts etc).

A basic example of information would be a computer. A computer uses programming scripts, formulas, or software applications to turn data into information.

Let us have a detailed look at the difference between data and information in a tabular column below

3 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
Help! Mathematics answer? Will give brainliest and some good points, Algebra I
skad [1K]
The answer is B.

Hope it helps!
8 0
3 years ago
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