Part Two- Statistics for Data Analysis with SPSS

This course provides a comprehensive introduction to inferential statistics, focusing on both parametric and non-parametric tests. Students will learn key statistical methods, including one-sample t-tests, independent samples t-tests, one-way ANOVA, two-way ANOVA, along with non-parametric alternatives such as the Mann-Whitney U test and the Wilcoxon signed-rank test. This will ensure that participants are able to identify the appropriate testing methods based on their data characteristics and research questions.

Through the course, participants will understand when and how to apply these tests in real-world scenarios, enabling them to analyze and interpret data effectively. Through a combination of theoretical concepts and practical applications, participants will learn to formulate hypotheses, perform statistical analyses, and understand the implications of their findings in the decision making process. Emphasis will be placed on mastering SPSS, a relatively easy to use yet relevant and marketable statistical software tool, allowing students to efficiently conduct tests and interpret output.

By exploring various situations where these statistical methods are applicable, students will gain insight into how statistical analyses inform decision-making processes in diverse fields. The course aims to equip learners with the skills necessary to utilize statistics confidently, empowering them to make data-driven decisions in their professional and academic pursuits. Join me to enhance your analytical abilities and proficiency in inferential statistics, setting a solid foundation for further study in data analysis and research methodologies.