Statistics 101
We constantly listen to information supported by “statistical data”. Every detail (and the opposite) is "scientifically proven." Without a basic knowledge of Statistics it is impossible to understand the information we receive to make a serious judgment of its veracity.
In this course essential concepts are studied to understand the information, for the management of the company, for the decision making based on the information: How to represent the information? What do the graphs say? What do we mean? How to interpret the information contained in the data to know what decisions to make? The course will be focused on learning through solving problems that cover different areas of the participants' performance.
What will you learn?
Topic 1. Descriptive Statistics
1.1 How to represent the information. How to read the information. What the tables and graphs say
1.2 Measures of central tendency. Measures of dispersion. What is an outlier?
Topic 2 Estimation of the population mean and proportion
2.1 Estimation of the population mean and proportion
2.2 Hypothesis tests on the mean and the population proportion
Topic 3 Comparison of means and proportions
3.1 Comparison of Means and Proportions
3.2 Analysis of Variance (ANOVA)
Topic 4 Regression and Correlation
4.1 Measurement of linear relationship between two variables
4.2 Linear Regression
4.3 Multicollinearity 4.4 Detection of multivariate outliers
Differentiator
The knowledge and techniques that are studied are introduced from the approach and resolution of problems related to the different and diverse areas of performance of the participants.
For the introduction of each concept, the following steps are followed:

Statement of a problem

Analysis of the variables involved: information they contain, types, distributions.

Presentation of statistical concepts

Application of the concept to the problem. Discussion of the solution

Getting Results Using Python, R, IBM SPSS Modeler or personalized

Other examples