Kam Hamidieh

Kam Hamidieh
  • Lecturer in Statistics

Contact Information

  • office Address:

    447 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Teaching

Current Courses

  • STAT102 - Intro Business Stat

    Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

    STAT102001 ( Syllabus )

    STAT102002 ( Syllabus )

    STAT102003 ( Syllabus )

Past Courses

  • STAT101 - INTRO BUSINESS STAT

    Data summaries and descriptive statistics; introduction to a statistical computer package; Probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; statistical inference of univariate data; Statistical inference for bivariate data: inference for intrinsically linear simple regression models. This course will have a business focus, but is not inappropriate for students in the college. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT102 - INTRO BUSINESS STAT

    Continuation of STAT 101. A thorough treatment of multiple regression, model selection, analysis of variance, linear logistic regression; introduction to time series. Business applications. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT422 - PREDICTIVE ANALYTICS

    This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT430 - PROBABILITY

    Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

  • STAT722 - PREDICTIVE ANALYTICS

    This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It delves into classification methodologies such as logistic regression. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. The methodologies can all be implemented in either the JMP or R software packages.

Knowledge@Wharton

Will Consumers Spend or Save This Holiday Season?

Retail sales will see modest growth over a holiday season marked by the uneven spending habits of consumers affected by the pandemic, according to experts.

Knowledge @ Wharton - 2020/10/20
How Data Science Can Win the Debate on Police Reform

Rooting out racial bias in law enforcement starts with better data, according to Wharton’s Dean Knox and Princeton’s Jonathan Mummolo. Their research is bringing hard science to the emotional debate on police reform.

Knowledge @ Wharton - 2020/10/20
When Should Schools Reopen?

A Penn Wharton Budget Model analysis provides policymakers with a framework to weigh the cost of COVID-19 infections in a community against students’ loss of future income due to lower quality education.

Knowledge @ Wharton - 2020/10/20