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Statistical Data Analysis with SPSS: a practical approach

Statistical Data Analysis with SPSS: a practical approach
Statistical Data Analysis with SPSS: a practical approach

Statistical Data Analysis with SPSS: a practical approach

From comparative studies to a lot of different predictive models

What you’ll learn

Statistical Data Analysis with SPSS: a practical approach

  • When you work with SPSS, you can do this easily.
  • To do descriptive analysis with SPSS.
  • To compare data using t-tests, ANOVA, ANCOVA, and multivariate ANOVA.
  • A lot of different models are going to be tested.

Requirements

  • Having some knowledge of statistics or the desire to learn quickly.
  • Must have Access to SPSS

Description

In this course, you will learn how to use one of the best statistical programs in the world, called SPSS (Statistical Package for Social Sciences).

It is true that SPSS is one of the best statistical programs for a lot of different types of research, like social sciences, business intelligence, psychology, forensic research, medical research, and many more.

This class will give you a research project that you can follow from the data that comes from a survey to a wide range of statistical tools that can help you get important information from this data.

Learn how to use SPSS to do the following things: define variables and introduce data, import data, transform and recode variables, reverse, and compute them. You will also learn how to do descriptive analysis, independent and repeated measures t-tests, Analysis of Variance (ANOVA), multivariate ANOVA analysis, ANCOVa analysis, correlations, and simple tests.

Each presentation comes with a set of exercises and databases that you can download to help you remember how to use SPSS correctly.

So, what are we going to do now? Let’s do this!

Who this course is for:

  • Students, professionals, entrepreneurs, and anyone else who wants to learn more about data.
  • The following are some of the types of people who study psychology and sociology: business researchers, data scientists, data analysts, entrepreneurs, and so on.

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