This course is intended to provide the basic and PRACTICAL knowledge of statistics for those who are involved in managing, analysing and presenting quantitative information.
Its intent is to cover, using a simplified but rigorous approach, the lack of knowledge that usually have those that have not attended a proper academic training during university but, nevertheless, require to manage, analyse and present quantitative information.
During the course, some statistical tools will be used to show how to translate into immediate practical application the concepts presented.
CFOs, R&D, financial analysts, and anyone who need to analyse measures and KPI.
Categorical data (nominal, ordinal)
Describing and summarizing
Scale (interval, ratio, continuous)
Summarizing continuous data
The Likert Scale
Sample and population
Independent (predictor) vs. dependent (outcome) variable
Operationalization – How to measure a concept or construct
Sample-statistic: estimating mean and standard deviation
Statistical Significance and Significance Testing
Test-statistic (estimate significance p<0.05)
Type of Errors
Measuring Effect Size
Concept of Power
Generalization, confidence and causality
The mechanic of inferential statistics
The normal distribution and the confidence interval
The SAMPLING DISTRIBUTION OF MEANS and the CENTRAL LIMIT THEOREM
Use of Test Statistic to compute the Significance Test
How big is the sample for a test-statistic?
Differences In the proportion of cases that fall into 2 different categories
Differences in the mean of two continuous variable
When to use
How a variable can predict another and how much of the variation of the variable can be explained by another
Identifying a small number of core theoretical variable
Correlation between 2 continuous variable or 1 continuous variable and 1 categorical dichotomous variable (Correlation and Simple Regression)
Using graphs to analyse data