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andrey2020 [161]
2 years ago
11

Can someone pls help, I know this is kind of a lot but I’ve been stuck on this for a while now.

Mathematics
1 answer:
Greeley [361]2 years ago
3 0

9514 1404 393

Answer:

  (a, b, c) = (-0.425595, 11.7321, 2.16667)

  f(x) = -0.425595x² +11.7321x +2.16667

  f(1) ≈ 13.5

Step-by-step explanation:

A suitable tool makes short work of this. Most spreadsheets and graphing calculators will do quadratic regression. All you have to do is enter the data and make use of the appropriate built-in functions.

Desmos will do least-squares fitting of almost any function you want to use as a model. It tells you ...

  a = -0.425595

  b = 11.7321

  c = 2.16667

so

  f(x) = -0.425595x² +11.7321x +2.16667

and f(1) ≈ 13.5

_____

<em>Additional comment</em>

Note that a quadratic function doesn't model the data very well if you're trying to extrapolate to times outside the original domain.

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Nowe we can find the means for x and y like this:

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And then the best predicted value of y for x = 41 is:

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Step-by-step explanation:

For this case we assume the following dataset given:

x: 38,41,45,48,51,53,57,61,65

y: 116,120,123,131,142,145,148,150,152

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

m=\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}

So we can find the sums like this:

\sum_{i=1}^n x_i =459

\sum_{i=1}^n y_i =1227

\sum_{i=1}^n x^2_i =24059

\sum_{i=1}^n y^2_i =168843

\sum_{i=1}^n x_i y_i =63544

With these we can find the sums:

S_{xx}=\sum_{i=1}^n x^2_i -\frac{(\sum_{i=1}^n x_i)^2}{n}=24059-\frac{459^2}{9}=650

S_{xy}=\sum_{i=1}^n x_i y_i -\frac{(\sum_{i=1}^n x_i)(\sum_{i=1}^n y_i)}{n}=63544-\frac{459*1227}{9}=967

And the slope would be:

m=\frac{967}{650}=1.488

Nowe we can find the means for x and y like this:

\bar x= \frac{\sum x_i}{n}=\frac{459}{9}=51

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And we can find the intercept using this:

b=\bar y -m \bar x=136.33-(1.488*51)=60.442

So the line would be given by:

y=1.488 x +60.442

And then the best predicted value of y for x = 41 is:

y=1.488*41 +60.442 =121.45

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