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3 years ago

14

Subtract 1/6 a+3 from 1/3 a−5 .

1 answer:

steposvetlana [31]3 years ago

7 0

$\frac{1}{6} a + 3 - \frac{1}{3}a - 5 = - \frac{a}{6} - 2$

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I hope that helps you out!!

Any more questions, please feel free to ask me and I will gladly help you out!!

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the answer should be letter A

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In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti

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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:

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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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3 years ago

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4 years ago

Read 2 more answers

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Answer:

a) Sample correlation coefficient, r = 0.7411

bi) test statistic, t = 4.102

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Step-by-step explanation:

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$r = \frac{S_{xy} }{\sqrt{S_{xx} S_{yy} }} }$

$S_{xx} = 2,648,130.357\\S_{yy} = 36.7376,\\S_{xy} = 7408.225$

$r = \frac{7408.225}{\sqrt{2648130.357*36.7376} }$

r = 0.7511

b)

i) formula for the test statistic is given by the formula:

$t = \frac{r\sqrt{n-1} }{\sqrt{1 - r^{2} } }$

sample size, n = 4

$t = \frac{0.7511\sqrt{14-1} }{\sqrt{1 - 0.7511^{2} } }$

t = 4.102

ii) Degree of freedom, df = n -2

df = 14 -2

df = 12

The P-value is calculate from the degree of freedom and the test statistic using excel

P-value =(=TDIST(t,df,tail))

P-value = (=TDIST(4.1,12,1)

P-value = 0.000736

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3 years ago