Statistical Inference
This module provides learners with an in-depth understanding of the statistical distribution and hypothesis testing in a practical approach to getting things done.
Statistical distributions include Binomial, Poisson, Normal, Log-Normal, Exponential, t, F, and Chi-Square. Parametric and non-parametric tests used in research problems are covered in this unit.
The module will help learners to formulate research hypotheses, select appropriate tests of hypotheses, write primarily R programs to perform hypothesis testing, and to draw inferences using the output generated. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analysing data.
Core Reading List:
Statistical Inference For Everyone
Copyright Year: 2017
Brian Blais, Bryant University
- Accreditation: Swiss Private Course
- Total workload: 150 hours
- Requires extra purchases (outside texts, etc.): Yes, purchases required
- ID verification: Required
- Admission requirements: Application required
- Minimum education requirement for students: Undergraduate
Candidates who apply for this course must have a recognised undergraduate degree or equivalent. Candidates without a degree but with other relevant qualifications and/or work experience can also be considered.
English language competency at an IELTS 6.5 (or equivalent) is required of all applicants whose first language is not English. Where students can demonstrate previous substantial studies or work experience in English, this requirement can be waived.