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Probability distributions form the core of many statistical calculations. That's why we have included as many intuitive examples as possible. All in all, this is quite a theoretical module on a topic that is not always easy to grasp.
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We end with a lesson where conditional probabilities, independence and Bayes rule are explained. Here the relation is made to tree-diagrams again, as well as contingency tables. A graphical tool to understand these concepts is introduced here as well, the tree-diagram.Thereafter a number of concepts from set theory are explained and related to probability calculations. Next, we provide an intuitive definition of probability through an example and relate this to the concepts of events, sample space and random trials. We start by describing randomness, and explain how random events surround us. This is not only useful for answering various kinds of applied statistical questions but also to understand the statistical analyses that will be introduced in subsequent modules.
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This module introduces concepts from probability theory and the rules for calculating with probabilities. You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software. We will discuss confidence intervals and significance tests. The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. You need to know about these things in order to understand how inferential statistics work. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions.
#Basic statistical calculations how to
Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). In the first part of the course we will discuss methods of descriptive statistics. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In this course you will learn the basics of statistics not just how to calculate them, but also how to evaluate them. Range vs.Understanding statistics is essential to understand research in the social and behavioral sciences. Inferential Statistics: What’s the Difference? Using these six descriptive statistics, we can gain a pretty good understanding of the distribution of values in this dataset. Lastly, we can use the following descriptive statistic to understand how many total observations are in the dataset: We can use the following descriptive statistics to get an idea of how spread out the values are in the dataset: We can use the following descriptive statistics to get an idea of where the center of the dataset is located: The following screenshot shows how to calculate various descriptive statistics for this dataset, including the formulas used: Suppose we have the following dataset with 20 values in Google Sheets: Sample size (total number of observations)Įxample: Calculating Descriptive Statistics in Google Sheets.Standard deviation (the spread of the values).Range (the difference between minimum and maximum value).Mode (the most frequently occurring value).The following example shows how to calculate the following descriptive statistics for a dataset in Google Sheets: They help us gain an understanding of where the center of the dataset is located along with how spread out the values are in the dataset. Descriptive statistics are values that describe a dataset.