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krok68 [10]
3 years ago
14

Can someone help plz

Mathematics
1 answer:
Misha Larkins [42]3 years ago
5 0

Answer: 5/4x

Step-by-step explanation:

To solve this you'll need to use the slope formula which is

Y2 - Y1 over

X1 - X2

You already have two points so that's what we'll use.

(4, 7) (12, 17)

17 - 7

12 - 4  = 10/8

10/8 can be simplified further to 5/4

You might be interested in
Table: Favorite Subject: Math 6, Science 3, English 4, History 7.
Klio2033 [76]

This is a ratio problem. First, we need to add the number of students polled.

7 + 4 + 3 + 6 = 20

Next, look at how many out of 20 students selected Science.

3 out of 20.

Say it this way:

3 is to 20 as ? is to 400.

Express it this way:

3/20 = x/400

Cross multiply to eliminate the denominators.

3 x 400 = 20 x  Now solve for x.

1200 = 20 x

x = 60

Check it!

Does 60/400 reduce to 3/20? Yes!

60 students are predicted to select science out of 400

4 0
3 years ago
Read 2 more answers
The equation shown below involves a perfect square trinomial. 4x2 + 20x + 25 = 7 Which of the following is a step in solving thi
fomenos
4 x^2 + 20 x + 25 = 7
Divide both sides by 4:
x^2 + 5 x + 25/4 = 7/4
Write the left-hand side as a square:
(x + 5/2)^2 = 7/4
Take the square root of both sides:
x + 5/2 = sqrt(7)/2 or x + 5/2 = -sqrt(7)/2
Subtract 5/2 from both sides:
x = sqrt(7)/2 - 5/2 or x + 5/2 = -sqrt(7)/2
Subtract 5/2 from both sides:
Answer:  x = sqrt(7)/2 - 5/2 or x = -5/2 - sqrt(7)/2
3 0
3 years ago
Find last year’s salary if, after a 3% pay raise, this year’s salary is $30,900
Liono4ka [1.6K]
Your answer is $30,000.
The way I have answered this is quite strange, but I'll do my best to explain it. So because we know that $30,900 is 3% than last year, we can call it 103%. This allows us to form a ratio and therefore find 100%.
30,900 : 103
÷ 103
300 : 1
× 100
30,000 : 100
Which means $30,000 is 100%, or 3% less than $30,900. I hope this helps! Let me know if it was confusing or anything :)
5 0
3 years ago
Read 2 more answers
52 is what percent of 260​
chubhunter [2.5K]

if we take 260 to be the 100%, what is 52 off of it as a percentage?

\bf \begin{array}{ccll} amount&\$\\ \cline{1-2} 260&100\\ 52&x \end{array}\implies \cfrac{260}{52}=\cfrac{100}{x}\implies 5=\cfrac{100}{x} \\\\\\ 5x=100\implies x = \cfrac{100}{5}\implies x=20

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
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