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kvasek [131]
3 years ago
6

Which graph represents-3x - 4y ≤ 2?

Mathematics
1 answer:
den301095 [7]3 years ago
8 0

Answer:

B

Step-by-step explanation:

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A recipe calls for 3/4 cups of sugar for every 2 cups of flour. How much sugar per cup of flour?
nexus9112 [7]

Answer:

8/3 or 2 2/3

Step-by-step explanation:

5 0
3 years ago
Select the correct comparison.
Delicious77 [7]

The true comparison is the typical value is greater in set A. The spread is greater in set B.

<h3>What is the true comparison?</h3>

Spread is used to measure the variability of a data set. Range can be used to measure spread. Range is the difference between the largest number and the smallest number in the dataset.

  • Spread of set A =  7 - 5 = 2
  • Spread of set B = 8 - 1 = 7

The median can be used to measure the typical value of the dataset. Median is the number at the center of the data set.

  • Median of set A = 6
  • Median of set B = 4

To learn more about median, please check: brainly.com/question/14746682

#SPJ1

8 0
2 years ago
The large rectangle below represents one whole.
svlad2 [7]

Step-by-step explanation:

there's no picture???

6 0
3 years ago
How many solutions does the following equation have?
Genrish500 [490]

Answer:

1 solution

Step-by-step explanation:

-6y + 13 + 9y = 8y - 3

=> 3y - 8y = -3 - 13

=> -5y = -16

=> y = 16/5

so y= 16/5 or 3.20

4 0
4 years ago
The least-squares regression line is:
babunello [35]

Answer:

a. the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.

Step-by-step explanation:

If we have N points (x_1, y_1),. ...(x_N, y_N) and we want to adjust a model y = ax+b

We can define the error associated to this like that:

E(a,b) = \sum_{n=1}^N [y_n -(ax_n +b)]^2

So as we can see here we are adding the square distances between the real and the adjusted values in order to minimize the error for this reason the correct answer is:

a. the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.

For this case we need to calculate the slope with the following formula:

a=\frac{S_{xy}}{S_{xx}}

Where:

S_{xy}=\sum_{i=1}^n x_i y_i -\frac{(\sum_{i=1}^n x_i)(\sum_{i=1}^n y_i)}{n}

S_{xx}=\sum_{i=1}^n x^2_i -\frac{(\sum_{i=1}^n x_i)^2}{n}

And we can find the intercept using this:

b=\bar y -m \bar x

7 0
4 years ago
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