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erma4kov [3.2K]
3 years ago
11

Please help determine the relationship of the following linear equations

Mathematics
1 answer:
maks197457 [2]3 years ago
4 0

Answer:

Neither

Step-by-step explanation:

step 1

Verify if the lines are parallel

we know that

If two lines are parallel, then their slopes are the same

In this problem the slope of f(x) =2/7 and the slope of g(x)=7/2

Compare

2/7≠7/2

therefore

The lines are not parallel lines

step 2

Verify if the lines are perpendicular

we know that

If two lines are perpendicular, then the product of their slopes is equal to -1

In this problem the slope of f(x) =2/7 and the slope of g(x)=7/2

Multiply

2/7*7/2=1

1≠-1

therefore

The lines are not perpendicular

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54.95

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Find the inverse of the equation
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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:

\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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12x84-(-7000)

1008+7000

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Easy

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