# Week 3 Chapter 3 - Estimating Linear Cost Functions & Regression Analysis

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 Author: honestkyle ID: 282920 Filename: Week 3 Chapter 3 - Estimating Linear Cost Functions & Regression Analysis Updated: 2014-09-20 14:18:09 Tags: ACC6902 Folders: ACC6902 Description: Estimating Linear Cost Functions & Cost Estimation/Statistical Issues - Regression Analysis Show Answers:

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1. What does "Y" represent in the Simple Linear Cost Function, y = a + bX?
Y = dependent variable (e.g., monthly maintenance cost)
2. What does "a" represent in the Simple Linear Cost Function, y = a + bX?
a = a fixed quantity that represents the value of Y when X = 0.
3. What does "b" represent in the Simple Linear Cost Function, y = a + bX?
b = the slope of the line (i.e., the variable cost rate)
4. What does "X" represent in the Simple Linear Cost Function, y = a + bX?
X = the cost driver/independent variable (e.g., # of machine hours)
5. In Regression Analysis,  is what?
• The coefficient of determination
• A measure of the explanatory power of the regression
• Relative measure of the "goodness of fit"
6. In Regression Analysis, what is the t-value?
A measure of the reliability of each of the independent variables.
7. In Regression Analysis, what is the standard error of the regression (SE)?
• An absolute measure of the "goodness of fit" of the regression line.
• Measure of the dispersion of the actual data points around the regression line.
8. A Regression with a high , means the value is close to what?
1.0
9. A Regression with a low , means the value is close to what?
0.0
10. What is the definition and equation for the Total Sum of Squares?
• The sum of the explained and unexplained squares for the regression line.
• TSS = ESS + RSS
11. In regards to the data points, how can the Standard Error be described?
It is the vertical distance between data points and the regression line.
12. What does it mean if ?
The accuracy or using the regression model is 75% better than using the mean.
13. What is the mean?
The measure of central tendency.
14. What is dispersion?
Variability in the data points.
15. What can be assumed when a data point is on the line?
Standard Error = 0
16. Regression is very sensitive to what?
Outliers
17. What are some rationales to be made if your prediction of the regression does not match the analysis?
• A linear equation may not be the right tool.
• The independent variable my not be the correct one.
18. Ordinary least squares regression produces what?
The line of best fit through any set of data, using the "least-squares" criterion.

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