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School of Statistics

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Degrees

Available Programs

Data Analytics for Beginners

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Data Analysis with SPSS

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M.Phil Statistics

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About the School

At the School of Statistics, students not only gain knowledge of the field but more importantly understand the principles and importance of life-long learning. The programs; BS(Hons.) , ADP (Statistics), M. Phil in Statistics, are designed in a way that enable the students to think critically about applications of statistics in various fields and analyze various types of data and interpret the results effectively.

The curricula of this school is based on a solid foundation with balanced coverage of theory and practice and undergo frequent updates to keep the content advanced and updated. Moreover, we are committed to train our students to become Data Scientists and highly qualified professionals who are hardworking, responsible, and good team players, with the skills and capabilities to contribute to the development of local and regional societies, and to be able to survive in the increasingly competitive global market.

Head`s Message

Message

I warmly welcome you to School of Statistics where we provide our students the latest theoretical developments in the subject and efficiently train them in application of statistical techniques. School of Statistics is rich in research and provides guidance in diverse areas of the subject along with providing expertise in the statistical softwares like SPSS, Stata, R, SAS, Python and many more. As a Head of the School, I assure you that School of Statistics will provide excellent educational and professional services to you in particular and to society in general, so join us to be a leading Statistician.

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Objectives

  • To efficiently provide knowledge & train students in statistical methods, including;
  • Statistical modeling and the omnipresent role of variability
  • The efficient design of studies and construction of effective sampling plans
  • Exploratory data analysis
  • Formal inference process
  • Have a background in probability, statistical theory, and statistical modeling
  • To help students in synthesizing & applying knowledge of common inferential methods; understanding the limitations of the procedures and the appropriate scope of conclusions.
  • To offer mastery of several standard statistical software packages and facilities with data management strategies.
  • To grant a focused concentration in an area of application outside the discipline of statistics.

Salient Features

  • MUL's liberal arts education prepares undergraduate students to face contemporary challenges based on competency-based learning.
  • State-of-the-art robotic library for easy access to all research material, books, and digital resources.
  • Well-equipped computer labs have modernized computers with advanced statistical languages and software.
  • Highly qualified faculty having research expertise in Statistical Linear Models, Statistical Quality Control, Experimental Design, Time series & Forecasting, Applied Statistics, Bayesian Statistics, Meta-Analysis, Sampling Techniques, Probability Distributions, Multivariate Analysis, Statistical Analysis using Artificial Intelligence, Neural Networking & Machine Learning.
  • A friendly and academically supportive atmosphere, where group teaching and personal attention are readily available for all students.
  • Hands-on experience in Statistics with advanced Statistical softwares.
  • Nine weeks of compulsory internship.
  • Modern teaching pedagogy includes case studies, classroom activities, video tutorials, group discussions, and instructional assignments.

Publications

Title Volume.# Issue.# Pages Year
Approaches of Generation Z towards AI Integration in Pakistani Classrooms.
Journal Name: Traditional Journal of Law and Social Sciences
 Volume # 4 Issue # 1 Page # 52-56 Year of Publication: 2025
Memory Type Floating Control Charts For Monitoring Process Coefficinet of Vartation
Journal Name: Contemporary Journal of Social Science Review
Volume # 3 Issue # 1 Page # 782-793 Year of Publication: 2025
An exponentiated XLindley Distribution with Properties, Inference and Applications
Journal Name: Heliyon
 Volume # 10 Issue # 3 Year of Publication: 2024
The prevalence of diabetes in Afghanistan: a systematic review and meta-analysis
Journal Name: BMC Public Health
 Volume # 21 Issue # 941 Year of Publication: 2021
Core Predictors of Debt Specialization: A New Insight to Optimal Capital Structure
Journal Name: Mathematics
 Volume # 9 Issue # 9 Page # 975 Year of Publication: 2021
Estimation of finite population distribution function with dual use of auxiliary information under non-response
Journal Name: PLOS ONE
 Volume # 15 Issue # 12 Page # 1-24 Year of Publication: 2020
Prevalence of diabetes and pre-diabetes in Bangladesh: a systematic review and meta-analysis
Journal Name: BMJ Open
 Volume # 10 Issue # 9 Page # 1-10 Year of Publication: 2020
An ensemble procedure for pattern recognition of regional drought
Year of Publication: 2025
A novel generalized combinative procedure for Multi-Scalar standardized drought Indices-The long average weighted joint aggregative criterion
Year of Publication: 2025
On the more generalized non‐parametric framework for the propagation of uncertainty in drought monitoring
Year of Publication: 2025
A framework to identify homogeneous drought characterization regions
Year of Publication: 2025
A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes
Year of Publication: 2025
Kumaraswamy Half-Logistic Distribution, Properties & Applications
Journal Name: Journal of Statistics Applications & Probability
 Volume # 6 Issue # 3 Page # 597-609 Year of Publication: 2017
A Novel Multi-Scalar Drought Index for Monitoring Drought: the Standardized Precipitation Temperature Index
Year of Publication: 2025
Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model
Year of Publication: 2025
On Exploring Order Effect and Ties in Paired Preferences with Bayesian Analysis
Journal Name: Journal of Statistics
 Volume # 25 Page # 24-31
Robust Assessing the Lifetime Performance of Products with Inverse Gaussian Distribution in Bayesian and Classical Setup
Journal Name: Mathematical Problems in Engineering
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Introduction
Objectives & Salient Features
Head`s Message
Offered Programs
Publications