M.Phil Statistics


In continuation of the BS Statistics program, further higher training toward research is essential in Pakistan. The Best University in Multan is providing higher research facilities to the students of Southern Punjab in many disciplines in M.Phil. Statistics. ISP is the only institution in Southern Punjab providing this facility with highly qualified and trained faculty. Moreover, ISP is also providing facilities of research to the students in many vacant research areas, so that after completing their degrees they can get jobs.

Eligibility Criteria

  1. Sixteen years of schooling or 4 year education in relevant filed from HEC recognized DAI will be required for admission.
  2. The GAT-General (CAT-C) (www.nts.org.pk/gat/gat.asp) / ISP-Admit Test conducted by the National Testing Service with a minimum 50% cumulative score will be required at the time of admission.


For M.Phill statistics program NOC is granted by HEC Pakistan.

Career Prospects


Statisticians not only work in academia but also work in many other fields including the following:

  • Market researcher
  • Financial risk analyst
  • Operational researcher
  • Business analyst
  • Data scientist
  • Investment analyst and many others.


Program Educational Objectives (PEOs)

Acquire a sound knowledge and deeper understanding of Statistics and will be able to pursue their studies for Ph.D. in Statistics.

Do research in Statistics as well as related areas of other disciplines.

To read, discuss, write and present Statistics with proper reasoning.

To work both independently and collaboratively on statistical problems in their own as well as allied areas while demonstrating high ethical values.

Acquire a sound knowledge of latest statistical software, needed in their areas.

Pursue their professional career in universities, research organizations, Business and Finance, Govt. Departments as well as private organizations, etc.; and will be able to reach middle level management within five years.

Programs Learning Outcomes (PLOs)

To apply knowledge of Statistics and other related areas to the solutions of problems occurring in their own areas as well as related areas of other disciplines.

To identify research literature and formulate statistical problems for further analysis and investigations.

To create, select and apply appropriate methods and techniques for the solution of statistical problems.

To apply their knowledge of social, health, cultural and economic issues in their professional responsibilities.

To apply high ethical values and norms in their professional careers.

To work effectively as an individual or as a member of team in multi-disciplinary environment.

To communicate effectively on statistical ideas and techniques with Statistics community as well as society.

To engage in life-long learning in the context of latest advancements in their own and related areas.

Scheme of Studies for M.Phil Statistics Program

Total Credit Hours = 30

Advance Method in Statistical Quality Control
Applied Stochastic Model
Advance Design of Experiment
Time Series Analysis
Thesis (continued) / 2 Elective Courses
Elective Courses
Advanced Probability Theory
Advanced Statistical Inference I
Advanced Statistical Inference II
Linear Model and Regression Analysis I
Linear Model and Regression Analysis II
Advanced Multivariate Analysis
Regression Models for Count Data
Advanced Categorical Data Analysis
Logical Reasoning and Research Methods
Survey Sampling
Longitudinal Data Analysis
Survival Data Analysis
Spatial Data Analysis
Advanced Distribution Theory
Inference in Stochastic Processes
Advanced Bayesian Inference
Optimization Techniques
Ecological Statistics
Statistical Methods for Clinical Trials
Bayesian Inference
Financial Stochastic Models
Statistical Genetics
Generalized Linear Models
Decision Trees
Generalized Linear Mixed Models
Advanced Operations Research
Multilevel Modeling
Environmental Statistics
Applied Smoothing Techniques
Convergence in Probability
Structural Equation Models
Casual Inference
Measure Theory and Integration
Advanced Statistical Theory