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Gelneren [198K]
3 years ago
15

craig are some blueberries. he used 2/5 of the blueberries to make muffins. He ate 1/3 of the remainder. what fraction of the bl

ueberries did he eat? -
Mathematics
2 answers:
sveticcg [70]3 years ago
8 0

<h2><u>PLEASE MARK BRAINLIEST!</u></h2>

Answer:

Facts:

  • Craig used 2/5 of the blueberries to make muffins
  • Craig ate 1/3 of the blueberries left after baking the muffins

Step-by-step explanation:

You need to have like fractions to solve this problem.

\frac{2}{5} = \frac{6}{15}

\frac{15}{15} - \frac{6}{15} = \frac{9}{15}

\frac{9}{15} × \frac{1}{3} = \frac{3}{15}

\frac{3}{15} = \frac{1}{5}

<h3>\frac{1}{5} is your answer.</h3>

<em>I hope this helps!</em>

Lerok [7]3 years ago
5 0
He ate 1/5 of the blueberries
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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

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