Parikshit Kumar Statistics Econometrics Time Series
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I tutor undergraduate Statistics with a simple and logical teaching style designed to remove fear of numbers. I break down complex concepts into easy steps and focus on exam-oriented preparation as well as conceptual clarity. Whether you need help with basics or advanced problem-solving, I adapt my pace to your learning style.
Ideal for B.Sc.,BBA, B.A., B.Com., Economics, Data Science, and Engineering students. 1.WA 84347.

I provide structured and concept-oriented tutoring in undergraduate-level Statistics, focusing on building strong fundamentals and problem-solving skills. My teaching approach emphasizes clear explanations, step-by-step derivations, and practical examples, helping students understand both theory and applications. I assist with coursework, assignments, exam preparation, and statistical interpretation using real-world data. 2. WA 21143

Topics covered include:
Descriptive Statistics, Probability Theory, Random Variables, Distributions.
Estimation, Hypothesis Testing, Correlation & Regression.
Sampling Techniques and Design of Experiments.
Statistical Quality Control.
Introductory Econometrics.
Advanced Econometrics.
Time Series Analysis.

Subjects

Statistics Bachelors/Undergraduate
Econometrics (Advanced) Bachelors/Undergraduate-Masters/Postgraduate

Experience

Intern (Aug, 2025–Present) at Central Water Commission, Delhi

Education

BSc Statistics Hons. (Jun, 2021–Jul, 2024) from Ramjas college, Delhi University–scored CGPA 8.689

Fee details

₹10,000–15,000/month (US$105.25–157.88/month)

Courses offered

Advanced Econometrics
- US$25000
- Duration: 2 Months
- Delivery mode: Flexible as per the student
- Group size: Individual
- Instruction language: English
- Certificate provided: No
This course provides an in-depth exploration of modern econometric methods used for analyzing complex economic data. It builds on foundational concepts to develop advanced analytical and statistical techniques for empirical research. Topics include generalized least squares (GLS), instrumental variables (IV) estimation, panel data models (fixed and random effects), limited dependent variable models (logit, probit), time series analysis (ARIMA, cointegration, and volatility models), and causal inference methods such as difference-in-differences and propensity score matching.
Emphasis is placed on both theoretical understanding and practical implementation using statistical software. Students will learn to handle issues such as heteroskedasticity, autocorrelation, endogeneity, and model specification. By the end of the course, students will be equipped to conduct independent empirical research and critically evaluate econometric studies in academic and policy contexts.