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Sonbull [250]
3 years ago
12

Please help!! How can I use the values of the discriminant to describe the solutions?

Mathematics
1 answer:
alexdok [17]3 years ago
4 0
I think you are referring to the (square root of b^2 - 4ac) in the quadratic formula.
When b^2 - 4ac is less than zero, the solutions to the quadratic equation will be complex (iamginary).
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If you wanted to graph the point (2.5, 1.5) what would you<br>do?​
Harrizon [31]

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3 years ago
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8. It takes Olivia 3 | yards of ribbon
Leokris [45]

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21 yards

Step-by-step explanation:

3 yards for 1 mum

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3 years ago
Coach Kirby is forming dodgeball teams from students in her physical education class.
Cloud [144]

Answer:

6x<48

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ik its this one because theres 48 players and 6 teams both are even numbers so if you did 48÷6x you would get 8 but theres fewer than 48 kids

6 0
2 years ago
What is the equation of the best-fitting regression line? Find this in two ways. First, using the regression analysis part of th
Lemur [1.5K]

Step-by-step explanation:

Regression analysis is used to infer about the relationship between two or more variables.

The line of best fit is a straight line representing the regression equation on a scatter plot. The may pass through either some point or all points or none of the points.

<u>Method 1:</u>

Using regression analysis the line of best fit is: y=\alpha +\beta x+e

Here <em>α </em>= intercept, <em>β</em> = slope and <em>e</em> = error.

The formula to compute the intercept is:

\alpha =\bar y-\beta \bar x

Here<em> </em>\bar y and \bar x are mean of the <em>y</em> and <em>x</em> values respectively.

\bar y=\frac{\sum y_{i}}{n} \\\bar x=\frac{\sum x_{i}}{n}

The formula to compute the slope is:

\beta =\frac{\sum (x-\bar x)(y - \bar y)}{\sum (x=\bar x)^{2}}

And the formula to compute the error is:

e=y-\alpha -\beta x

<u>Method 2:</u>

The regression line can be determined using the descriptive statistics mean, standard deviation and correlation.

The equation of the line of best fit is:

(y-\bar y)=r\frac{\sigma_{x}}{\sigma_{y}} (x-\bar x)

Here <em>r</em> = correlation coefficient = r=\frac{Cov (x,y)}{\sqrt{\sigma^{2}_{x}\sigma^{2}_{y}} }

\sigma_{x} and \sigma_{y} are standard deviation of <em>x</em> and <em>y</em> respectively.

\sigma_{x}=\frac{1}{n}\sum (x-\bar x)^{2} \\\sigma_{y}=\frac{1}{n}\sum (y-\bar y)^{2}

3 0
3 years ago
I need helpppppppp I don’t like remainders :/
den301095 [7]

Answer:

The correct answer is 67 9/11

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