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HBO/WO

MBO

8 mei 2018

10-10: Data analysis (descriptive)- micro lecture IQR AND BOX PLOT

In this micro lecture the following items will be explained regarding this lecture: In this first module, we’ll introduce the basic concepts of descriptive statistics. We’ll talk about cases and variables, and we’ll explain how you can order them in a so-called data matrix. We’ll discuss various levels of measurement and we’ll show you how you can present your data by means of tables and graphs. We’ll also introduce measures of central tendency (like mode, median and mean) and dispersion (like range, interquartile range, variance and standard deviation). We’ll not only tell you how to interpret them; we’ll also explain how you can compute them. Finally, we’ll tell you more about z-scores. In this module we’ll only discuss situations in which we analyze one single variable. This is what we call univariate analysis. In the next module we will also introduce studies in which more variables are involved.

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Henk van der Kolk

8 mei 2018

10-25: Data analysis (descriptive)- ASSIGNMENT 10: SUMMARIZING RATIO VARIABLES

In this assignment the following items will be explained regarding this assignment: In this assignment you will learn how to omprehend the meaning and implications of basic summary measures for ratio variables. Mean, median, quartiles, interquartile range,

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Henk van der Kolk

2 juli 2025

Data Analysis and visualization BML

Learning Outcomes You can explain the fundamental concepts of data, including data types, data structures, and the importance of data quality, and you can apply these concepts in the context of both Excel and R. You can import data from various sources (such as CSV, TXT, Excel files) into both Excel and R, and you are able to select the appropriate methods and functions for different data formats and structures. You can clean and prepare data for analysis in both Excel and R by applying techniques for identifying and handling missing values, outliers, inconsistencies, and duplicates. You can apply and interpret various data analysis functions in both Excel and R, including determining minimum and maximum values, calculating percentiles, selecting specific data based on criteria, applying conditional formatting to visually identify patterns, and sorting data to gain insights. You can create effective data visualizations in both Excel and R (such as bar charts, line charts, pie charts, and box plots) to communicate patterns, trends, and relationships in data, and you can customize the visualizations for a clear and purpose-driven presentation. Duration: 18 lessons (5EC).

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Jurre Hageman