Explanation
Introduction to Statistics for non-mathematicians published by Udemy Academy. From Basic Principles to Multiple Regressions. In this course, with the basic concepts of statistics (quantity, variables and constants, samples and populations, classification and representation, parameters and statistics, units of analysis, data storage), measurement (nominal, ordinal, interval and scale) ratio ) and the types of variables (continuous and discrete, parametric and non-parametric), how to change one type of scale to another, a basic description of the data (mean, median, shape, range, variance, standard deviation). Theoretical and real distributions, standard deviation (Z scores), normal distribution and its characteristics, hypothesis test, probability hypothesis, Z test, t test and t distribution, confidence interval, independent samples t test, t test for repeated measures, analysis of variance (ANOVA) for independent samples and repeated measures, Pearson correlation, simple multiple linear regression.
In all presentations, the focus shifts from the mathematical aspects to the basic principles behind each statistical model, so that each topic is very easy to understand. The tone of the presentation is well organized to facilitate the understanding of each new concept. Each section is followed by several exercises where you can reinforce your newfound knowledge. In addition, in each section you will find tables with the key values of your statistical indicators and a document with all the solved exercises to make sure you are correct. Good luck on your way to becoming a statistician!
What you will learn in the Introduction to Statistics non-math course:
- To understand and use correctly the basic concepts of statistics
- To perform a descriptive analysis of the data
- To estimate the statistical parameters of the population
- To make comparisons between two or more groups using t tests and ANOVA.
- To make comparisons between two or more repeated measurements using t tests and ANOVA
- To understand and perform data correlation analysis
- To build simple multiple prediction models using linear regression
Who this course is suitable for:
- Students in various fields such as psychology, sociology, business, data science, economics, marketing, anthropology, health, engineering and others
- Future marketing analysts, business intelligence analysts, data analysts, or data scientists
- Professionals in various fields who want to acquire new skills that will take them to a higher level of their profession.
- Entrepreneurs and managers who want to improve their ability to interpret data as risks and opportunities become apparent.
Course guidelines
- Publisher: Udemy
- Teacher: Sebastian Pintea
- Language: English
- Level of training: Introduction
- Number of courses: 39
- Training duration: 4 hours and 3 minutes
course topics
Course requirements
No special experience is required
Basic knowledge of mathematical operations
Pictures
Introduction to Statistics in non-statistics video
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Quality: 720p
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File size
1.65GB