MANGT 515 Prospective Students
Sample Syllabus
Sample Lesson
Introduction
2.1 Reasons for Forecasting in Project Management
2.2 Forecasting Methods for Projects
2.2.1 Qualitative Forecasting Methods
2.2.2 Quantitative Forecasting Methods
Time Series Analysis
Short Term Forecasting Methods
Naïve Approach
Simple Averages
Moving Averages
Exponential Smoothing
Example of Exponential Smoothing
Trend-Adjusted Exponential Smoothing
Example of Trend-Adjusted Exponential Smoothing
2.3 Intermediate Term Forecasting
Example of Linear Regression
Evaluating the "Fit" of the Regression Line
2.3.2 The Correlation Coefficient (r)
2.3.3. The Standard Error of the Estimate (syx)
2.3.4 Limitations in Forecasting Using Linear Regression
2.3.4 Limitations in Forecasting Using Linear Regression (continued)
2.4 Forecasting the Project End Conditions
2.5 S-curve Forecasting
2.6 Technological Forecasting*

2.3.2 The Correlation Coefficient (r)

This statistic measures the strength of the relationship or association between x and y and takes on a value between -1 and +1. The closer the value of r to +1 or -1, the stronger the relationship is between the variables. The formula for r is given by: