Udemy – Introduction to Statistics for nonmathematicians
English | Tutorial | Size: 1.59 GB
Introduction to Statistics for nonmathematicians published by Udemy Academy. From Basic Principles to Multiple Regression. In this course, with the basic concepts of statistics (quantity, variables and constants, samples and populations, randomness and representation, parameters and statistics, units of analysis, databases), measurement scales (nominal, ordinal, interval and scale) ratio) and types of variables (continuous and discrete, parametric and non-parametric), how to convert one type of scale to another, basic description of data (mean, median, mode, range, variance, standard deviation). Theoretical and real distributions, standardization (Z scores), normal distribution and its characteristics, hypothesis testing, null hypothesis probability, Z test, t test and t distribution, confidence intervals, independent samples t test, t test of measurements repeated measures, analysis of variance (ANOVA) for independent samples and repeated measures, Pearson correlation, simple and multiple linear regression.
In all presentations, the focus shifts from the mathematical aspects to the fundamental principles behind each statistical method, so that each topic is very easy to understand. The rhythm of the presentation is well calibrated to facilitate the understanding of each new concept. Each section is followed by several exercises in which you can consolidate your newly acquired knowledge. In addition, for each section you will receive tables with critical values for your statistical indicators and a document with all the solved exercises to check if you are doing it right. Good luck on your way to becoming an expert in statistics!
What you will learn in the Introduction to Statistics for nonmathematicians course:
To understand and use correctly the basic concepts of statistics
To perform the descriptive analysis of data
To estimate statistical parameters in the population
To perform comparisons between two or more groups using t tests and ANOVA
To perform comparisons between two or more sets of repeated measures using t tests and ANOVA
To understand and perform correlation analysis of data
To build simple and multiple predictive models using linear regression
Who is this course suitable for:
Students in different domains such as psychology, sociology, business, data science, economics, marketing, anthropology, medicine, engineering and others
Future marketing analysts, business intelligence analysts, data analysts, or data scientists
Professionals in different domains who want to acquire new competences that will take them to a superior level in their career.
Entrepreneurs and managers who want to upgrade their ability to interpret data in such way that risks and opportunities become more obvious
Course specifications
Publisher: Udemy
Instructor: Sebastian Pintea
Language: English
Training level: Introductory
Number of courses: 39
Training duration: 4 hours and 3 minutes
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