Course Content
Orientation, introduction to the course
1. Human-Robot Interaction (HRI)
2. Research Methods in Human-Robot Interaction
3. Smart Cities & HRI
The demand for city living is already high, and it appears that this trend will continue. According to the United Nations World Cities Report, by 2050, more than 70% of the world's population will be living and working in cities — one of many reports predicting that cities will play an important role in our future (UN-Habitat, 2022). Thus, as cities are growing in size and scope, it is shaped into complex urban landscape where things, data, and people interact with each other. Everything and everyone has become so connected that Wifi too often fails to meet digital needs, online orders don't arrive fast enough, traffic jams still clog the roads and environmental pollution still weighs on cities. New technologies, technical intelligence, and robots can contribute to the direction of finding solutions to ever-increasing problems and assist the evolution of the growing urban space.
Human-Robot Interaction
About Lesson

Presenting and Reporting data

Usually when reporting our data, a simple table is good enough to demonstrate the data. The mean, the median, and the standard deviation are the most common measures shown in such tables. Sometimes we can also show the confidence interval (especially if further analysis was based on these data). The confidence interval is the mean of your estimate plus and minus the variation in that estimate. This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Usually in data analysis for human-robot interaction, a 95% confidence interval is used. A 95% confidence interval is a range of values above and below the point estimate within which the true value in the population is likely to lie with 95% confidence. 


Another way to present our data is the box plot. A box plot, also known as a box and whisker plot, is a graphical representation of a dataset that displays its distribution, outliers, and quartiles. It is used to visualize the range, median, interquartile range (IQR), and the spread of data in a concise manner. 

A typical box plot consists of a rectangle, called the box, that represents the IQR, which is the range between the first quartile (Q1) and the third quartile (Q3). The median, or the middle value, is represented by a vertical line inside the box. The whiskers, represented by lines extending from the top and bottom of the box, show the range of the data, excluding outliers. Outliers, which are data points that fall outside of the whiskers, are represented as individual points. 

Box plots can be useful for comparing distributions between groups or visualizing the distribution of a single variable. They are often used in statistics, data analysis, and data visualization to gain insights into the nature of the data and to identify any patterns or outliers that may exist. 

For further studying about box plots watch this video:  


Lazar, J. , Feng, J. H., Hochheiser, H.  (2017), Research methods in human-computer interaction: Morgan Kaufmann, 2017. 

Rosenthal, R., Rosnow, R., 2008. Essentials of Behavioral Research: Methods and Data Analysis, third ed. McGraw Hill, Boston, MA. 

Dix, A. (2020). Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool Publishers.
Robertson, J., & Kaptein, M. (2016). An introduction to modern statistical methods in HCI (pp. 1-14). Springer International Publishing.
Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and R. Routledge.
Aldrich, J. O. (2018). Using IBM SPSS statistics: An interactive hands-on approach. Sage Publications.