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Quantitative Methods | Online Assignment Help

Ch. 4 Homework (SCM 386)

1. What is a simple linear regression?

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2. In this equation define what each of the variables are:

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3. In terms of slope, what is the differences between positive, negative and no linear relationship

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4. What is coefficient of determination (r2)?

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5. What is the meaning of a 0.8 coefficient of correlation?

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6. In a multiple regression, P values shouldn’t be used to eliminate more than one variable at a time. Why?

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7. What is the use of an adjusted r2 value in a multiple regression?

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Using the Excel Data Analysis add-on solve:

8. As HR manager you wonder how effective training is at reducing scrap. For the last 5 years you track training hrs. vs scrap level.

Year Training Scrap
2012 200 5000
2013 300 4900
2014 400 4300
2015 500 4200
2016 600 4000

What is the F value and at 95% significance does it support a linear relationship? If so, what is the equation? What % of the variation is explained by the model? What are the upper and lower values for the regression coefficients?

9. You are tracking production output and years’ experience.

Years’ experience output
1 2000
2 2012
3 1900
4 2020

At 95% significance, does the F value support a linear relationship between years’ experience and output?

10. As the production manager, you have been putting more time in preventative maintenance & operator training. You want to quantify what this has meant for output.

Year PM hrs Training Ouput
2011 300 200 20000
2012 330 210 20200
2013 500 260 21000
2014 510 280 21050
2015 600 330 22000
At 95% significance, what is the F value?

What is the equation?

What is the correlation coefficient? Is this high or low?

What % of the variability in output is explained by the linear regression model?

Which variable (PM hrs or training) has a higher impact on output?
What is the sum of residual values? What does this sum tell you about the model?

11. Using the VizDataEffectivelyPractice File complete Dotplot (Ch 3 Effective Data Visualization) and SmallMultiples (Ch 3 Effective Data Visualization)

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