4.
Interpreting Regression
LO5
Interpret the results of regression output.
F
V
Y
= Intercept
Y =
Slope
F
+ V
X
X
5.
Interpreting Regression, Continued. . .
Correlation
coefficient
“R” measures the linear relationship between
variables. The closer R is to 1.0 the closer the
points are to the regression line. The closer R is to
zero, the poorer the regression line.
Coefficient of
determination
“R2” The square of the correlation coefficient. The
proportion of the variation in the dependent
variable (Y) explained by the independent
variable(s)(X).
T-Statistic
The t-statistic is the value of the estimated
coefficient, b, divided by its standard error.
Generally, if it is over 2, then it is considered
significant. If significant, the cost is NOT totally
fixed.
6.
Interpreting Regression, Continued. . .
Correlation Coefficient
Coefficient of Determination
T-Statistic
.91
A linear relationship does exists between repair hours and overhead costs.
.828
82.8% of the changes in overhead costs can be explained by changes
in repair-hours.
10.7 & 7.9
Both have t-statistics that are greater than 2, so the cost is not totally
fixed.
Estimate 3C’s overhead with 520 repair hours.
7.
Multiple Regression
Multiple Regression:
When more than one predictor (x)
is in the model.
Is repair-hours the only activity that drives
overhead costs at 3C?
Predictors:
X1: Repair-hours
X2: Parts Cost
Equation:
TC = VC(X1) + VC(X2) + FC
9.
Multiple Regression Output
Interpret results and estimate total costs with 520 repair hours and
$3500 parts costs
10.
Statistical Cost Estimation Using Regression Analysis
Statistical procedure to determine the
relationship between variables.
High-Low Method
Regression
Uses two data points.
Uses all the data points.
3C Overhead
11.
Implementation Problems
LO6
Identify potential problems with regression data.
1. Curvilinear costs
2. Outliers
3. Spurious relations
Curvilinear costs
4. Assumptions
Identify relevant range
Analyze relevant range
Relevant
Range
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