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# steps on performing simple regression

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A uranium mining company has recorded its monthly profit and the average price of uranium for each month over a period of 24 months. The recorded data is in the attached file.

It is assumed that a simple linear regression will determine the relationship between the average monthly uranium price and monthly profit. The following values have been calculated:

SSxx= 707.1967 SSxy= 8435.9392 SSyy= 114003.8333
SSE= 13374.02264 x= 46.4496 y= 55.9167

Using the calculations given above:

i) Derive the simple linear regression for this data and explain the equation in
words.

ii) Assuming that the model is correct calculate the predicted monthly profit next
month if the average uranium price is assumed to be \$59.

iii) Calculate the value r2 and explain what this value represents.

Please do not use excel functions - I would like to see clear workings and commentary for calculations

https://brainmass.com/statistics/linear-regression/steps-performing-simple-regression-571278

#### Solution Preview

i) Derive the simple linear regression for this data and explain the equation in words. ...

#### Solution Summary

The solution gives detailed steps on conducting simple regression. All formula and calculations are shown and explained.

\$2.19

## Performing a Simple Regression: Rapid Engine Company

The Rapid Engine Company is a multi-national manufacturer os small gasoline and Diesel motors. Rapid hs estimated the following cost experience for a new 3.5hp engine over a sample of 122 observations.

COST = \$8,500 + \$32 OUTPUT

Predictor Coef Stdv t Ratio
_______________________________________________________
Constant 8,500 5,000 1.7
OUTPUT 32 8 4.0
__
SEE = \$2,500, R2 75% R2 74.5%
Where COST is the dependant variable and OUTPUT is the independent variable
A.- Fully interpret these simple regression results using all the statistical information supplied
B.- Describe this cost category as a fixed or variable based upon the simple regression results described above.
C.- How might Rapid improve the value of this regression as a sales predictor.

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