- 16 Videos
- 45 Exercises
- 4 hours
- 46,781 Participants
- 2850 XP

**Instructor(s):**

Andrew Conway is a Psychology Professor in the Division of Behavioral and Organizational Sciences at Claremont Graduate University in Claremont, California. He has been teaching introduction to statistics for undergraduate students and advanced statistics for graduate students for 20 years, at a variety of institutions, including the University of South Carolina, the University of Illinois in Chicago, and Princeton University.

Not sure if this is the type of online statistics course you’re looking for? Or perhaps not yet familiar with DataCamp’s interactive learning interface? By taking this free course, you can discover it for yourself! Via a combination of videos and interactive coding challenges, this introductory course will teach you about variables, plotting, and summary statistics like the mean and standard deviation. Enjoy learning by doing!

In this chapter professor Conway will cover types of variables. It is very important to understand what type of variable you are dealing with when conducting a particular type of statistical analysis. You will cover variables such as nominal, ordinal, interval and ratio, and you will experiment with these via interactive exercises in R.

You will look here at distributions in graphs called histograms. A histogram is one of the simplest graphs used in statistics, but they are very useful and very informative. Studying histograms will help you to overcome the tendency to put too much of a focus on summary statistics.

When working with data it is very important to keep in mind what type of scale you are dealing with, hence this chapter on scales of measurement. This chapter will introduce you to the different types of scales with a specific focus on the standard scale, the z-scale.

In the previous chapters you looked at distributions and the importance of these. In this chapter the focus is more on summarizing all available information and drafting summary statistics. To make it a little bit more fun, the examples will be based on a wine tasting experiment :-).

Measures of central tendency try to capture the center point of a distribution. Measures of variability want to capture how much spread there is, or how wide the distribution is. The two measures you will look at in this final chapter will be standard deviation and variance.