Statistics is the lingua franca of modern science, including the social sciences. It is also of ever greater importance in daily life, as data of all sorts are now ubiquitous. Statistical literacy is hence of great value, both for academic purposes and for our daily routines. This course offers a solid foundation in statistical reasoning and its uses in the quantitative social sciences.
Learning statistics can be a daunting experience. There is a plethora of statistical concepts to master and many of them come with a hefty dose of mathematical notation. The goal of the present course is to develop a clear path through the conceptual forest and to explain each concept both in its narrow meaning and as a part of the larger enterprise of statistical reasoning. Mathematical skills are not taken for granted; instead, we shall review the necessary mathematical tools so that you will not get stuck on this aspect.
The field of statistics is sometimes divided into descriptive and inferential statistics, with probability theory forming a bridge between the two. In this course, we start out descriptively, by considering different ways in which we can learn from data. We then delve into the subject of probability theory, to end with a discussion of statistical inference. The emphasis in this part is on learning how to draw conclusions about populations with the help of data from a sample.
By the end of this course, you should have a good feeling for descriptive statistics, statistical inference, and probability theory. You should also understand the interplay of these elements in the broader enterprise of statistical reasoning. And you should feel more comfortable reading about statistics and using them in your own work.
Learning statistics can be a daunting experience. There is a plethora of statistical concepts to master and many of them come with a hefty dose of mathematical notation. The goal of the present course is to develop a clear path through the conceptual forest and to explain each concept both in its narrow meaning and as a part of the larger enterprise of statistical reasoning. Mathematical skills are not taken for granted; instead, we shall review the necessary mathematical tools so that you will not get stuck on this aspect.
The field of statistics is sometimes divided into descriptive and inferential statistics, with probability theory forming a bridge between the two. In this course, we start out descriptively, by considering different ways in which we can learn from data. We then delve into the subject of probability theory, to end with a discussion of statistical inference. The emphasis in this part is on learning how to draw conclusions about populations with the help of data from a sample.
By the end of this course, you should have a good feeling for descriptive statistics, statistical inference, and probability theory. You should also understand the interplay of these elements in the broader enterprise of statistical reasoning. And you should feel more comfortable reading about statistics and using them in your own work.
