Simple Regression Equation: Data Analysis
P1.B.3 Forecasting Techniques
y = a + bx
y = total cost (dependent) x = cost driver (independent) a = fixed costs (y-intercept) b = variable cost per unit (slope)
a = [(sum y)(sum x2)-(sum x)(sum xy)] / n(sum x2)-(sum x)2
b = [n(sum xy)-(sumx)(sum y)] / n(sum x2)-(sum x)2
What is Multiple Regression?
P1.B.3 Forecasting Techniques
Expected Value of Random Variables: Decision Theory
P1.B.3 Forecasting Techniques
Sum of product of expected outcome & respective probabilities.
Cumulative Average - Time Learning Model: Model Building
P1.B.3 Forecasting Techniques
Statistical Reliability of Each Independent Variable
t-value: relationship between x and y
t-value lower than 2 suggests there is little to no statistical relationship between the two variables
Regression Analysis Benefits & Limitations
P1.B.3 Forecasting Techniques
Benefits
Limitations
Learning Curve Analysis Benefits & Limitations
P1.B.3 Forecasting Techniques
Benefits
Limitations
Expected Value Technique Benefits & Limitations
P1.B.3 Forecasting Techniques
Benefits
Limitations