
After successful completion of this part, the student is able to:
 apply the convolutions and the conditional expectations/variances to solve an elementary stochastic inventory problem
 apply regression theory to investigate dependence between variables in a simple data set


Probability theory: convolutions and conditional expectations/variances of continuous and discrete random variables
Regression theory in order to investigate dependence between multiple variables in a data set.



 Assumed previous knowledgeRandom variables mean, variance, and standard deviation. Continuous Probability distributions: normal and gamma. Discrete distributions. Statistics. Conditional probability, and conditional variance (Probability and statistics in Module 4). The conditional variance is explained in the same quarter. This is taken into account in the timing between SCM content and the statistics and math. 
Bachelor Industrial Engineering and Management 
  Required materialsRecommended materialsBookApplied Statistics and Probability for Engineers, by D. Montgomery and G. Runger. Fifth Edition. SI version.  ISBN13: 9780470053041 

 Instructional modesTestsStatistics Exam 1
 Statistics Exam 2


 