Sta2020s - Study guides, Study notes & Summaries

This document contains the full lecture notes for the STA2020S course at the University of Cape Town (UCT) as of 2022. The notes cover all the modules covered in the course.

ALL THE NOTES YOU WILL NEED TO PASS THE TIME SERIES SECTION IN THE EXAM. I put my blood sweat and tears into these notes. Second-year second-semester applied statistical science notes. Notes made from the lecture slides, textbook and lecturer talking points thus making them in-depth and comprehensive. INCLUDES QUESTIONS AND EXAMPLES OF FOR EVERY TYPE OF METHOD.

ALL THE NOTES YOU WILL NEED TO PASS THE NON-PARAMETRIC SECTION IN THE EXAM. I put my blood sweat and tears into these notes. Second-year second-semester applied statistical science notes. Notes made from the lecture slides, textbook and lecturer talking points thus making them in-depth and comprehensive. INCLUDES QUESTIONS AND EXAMPLES OF FOR EVERY TYPE OF METHOD.

ALL THE NOTES YOU WILL NEED TO PASS THE ANOVA SECTION IN THE EXAM. I put my blood sweat and tears into these notes. Second-year second-semester applied statistical science notes. Notes made from the lecture slides, textbook and lecturer talking points thus making them in-depth and comprehensive. INCLUDES QUESTIONS AND EXAMPLES OF FOR EVERY TYPE OF METHOD.

ALL THE NOTES YOU WILL NEED TO PASS THE SIMPLE LINEAR, MULTIPLE LINEAR REGRESSION & LOGISTIC REGRESSION SECTION IN THE EXAM. I put my blood sweat and tears into these notes. Second-year second-semester applied statistical science notes. Notes made from the lecture slides, textbook and lecturer talking points thus making them in-depth and comprehensive. INCLUDES QUESTIONS AND EXAMPLES OF FOR EVERY TYPE OF METHOD.

This is a complete and comprehensive summary of the time series section of the STA2020 - Applied Statistics course. It contains everything you need to know to fully understand and do well in this section. Ideal for test/exam preparation! The sub-sections covered are autocorrelation, stationarity, components of a non-stationary time series, moving average smoothing, classical decomposition, simple forecasting methods, transformations, diagnostic checks for residuals, forecasting accuracy, pre...