Who should enroll in this program?
This program is designed for data analysts, production engineers, financial analysts, information specialists, and technical supervisors
What will I learn?
How to collect data, how to analyze and summarize data and how to interpret the results. The techniques learned in this certificate can be applied to quality control, production design and analysis, telecommunications, financial analysis, and risk analysis. This certificate will help the data analysts in conducting appropriate statistical analyses of their data and help the technical supervisors in understanding the results of statistical analyses conducted by other people.
- Role and purpose of applied statistics. Data visualization and use of statistical software used in course. Descriptive statistics, summary measures for quantitative and qualitative data, data displays. Modeling random behavior: elementary probability and some simple probability distribution models. Normal distribution. Computational statistical inference: confidence intervals and tests for means, variances, and proportions. Linear regression analysis and inference. Control charts for statistical quality control. Introduction to design of experiments and ANOVA, simple factorial design and their analysis.
- Regression models and the least squares criterion. Simple and multiple linear regression. Regression diagnostics. Confidence intervals and tests of parameters, regression and analysis of variance. Variable selection and model building. Dummy variables and transformations, growth models. Other regression models such as logistic regression. Using statistical software for regression analysis.
- Role of sample surveys. Sampling from finite populations. Sampling designs, the Horowitz-Thompson estimator of the population mean. Different sampling methods, simple random sampling, stratified sampling, ratio and regression estimates, cluster sampling, systematic sampling.
- Statistically designed experiments and their importance in data analysis, industrial experiments. Role of randomization. Fixed and random effect models and ANOVA, block design, latin square design, factorial and fractional factorial designs and their analysis.
- Communicating with scientists in other disciplines. Statistical tools for consulting. Using statistical software such as JMP, SAS, and S-plus. Case studies which illustrate using statistical methodology and tools are presented by the instructor and guest speakers from academia and industry.
- Time series models, smoothing, trend and removal of seasonality. Naive forecasting models, stationarity and ARMA models. Estimation and forecasting for ARMA models. Estimation, model selection, and forecasting of nonseasonal and seasonal ARIMA models.
Why study Applied Statistical Methods at NJIT?
The graduate certificate’s narrow focus allows you to dig deep into this specific topic, and start applying your knowledge sooner. Earn this certificate on our NJIT Newark campus. And you’ll learn from NJIT’s distinguished professors and instructors.
Applicants must have an undergraduate degree from an accredited institution with at least 12 credits in mathematics, including calculus. Students who do not meet these requirements may be admitted if they satisfy the university's requirements for admission. An undergraduate GPA of at least 3.0 on a 4.0 scale or equivalent is normally required.
Related Degree Programs
All credits for the Applied Statistical Methods Certificate relates in its entirety to NJIT MS in Applied Statistics.
Check the course descriptions for more information. Some courses have prerequisites and must be taken in order.
Faculty Advisor: Sunil Dhar
For more information about this graduate certificate, click here.
Please check the Registrar's website for full course listings and course profiles.