Course Outline
Preparation of a database for analysis
- management of data collection
- operations on variables
- transforming the variables selected functions (logarithmic, exponential, etc.)
Parametric and nonparametric statistics, or how to fit a model to the data
- measuring scale
- distribution type
- outliers and influential observations (outliers)
- sample size
- central limit theorem
Study the differences between the characteristics of statistical
- tests based on the average and media
Analysis of correlation and similarities
- correlations
- principal component analysis
- cluster analysis
Prediction - single regression analysis and multivariate
- method of the least squares
- Linear Model
- instrumental variable regression models (dummy, effect, orthogonal coding)
Statistical Inference
Requirements
Knowledge of SPSS and the basis of statistics. Course participant should complete the training of SPSS Statistics Predictive Analytics Software.
Testimonials (7)
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
We were using road accident data for practicals
Maphahamiso Ralienyane - Road Safety Department
Course - Statistical Analysis using SPSS
Well thought out and high grade planning materials.
Andrew - Office of Projects Victoria - Department of Treasury & Finance
Course - Forecasting with R
Wasn't boring, the trainer could keep the attention, the topics were covered in depth.
Marta - Ministerstwo Zdrowia
Course - Advanced R Programming
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
At the end of the class, we had a great overview of the language, we were provided tools to continue learning and were provided suggestions on how to continue learning. We covered AI/ML information.