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Algebra (for Probability and Statistics) | the Statistics Glossary. OpenIntro. Starting with the basics: how data are collecting, how to compute basic summaries, and how to generate common plots. Chapter 1 Slides, PDF LaTeX Source for Slides Probability forms a foundation for understanding nuances about data and the methods we use to analyze data. Chapter 2 Slides, PDF A solid grasp of the normal distribution plays a pivotal role in applying commonly used statistical techniques. Chapter 3 Slides, PDF Get a grasp of key concepts related to inference, starting in the context of point estimates, confidence intervals, and hypothesis tests for a sample mean. Chapter 4 Slides, PDF Learn how to apply inference techniques to situations where the outcome is a numerical variable, including cases where outcomes are being compared across several groups. Chapter 5 Slides, PDF Analyze categorical data like a pro by learning about inference in the context of proportions and contingency tables.

Chapter 6 Slides, PDF Chapter 7 Slides, PDF Chapter 8 Slides, PDF. Advice for stats students on the academic job market. Job hunting season is upon us. Openings are already being posted here, here, and here. So you should have your CV, research statement, and web page ready. I highly recommend having a web page. It doesn’t have to be fancy. The earlier you submit the better. If you are seeking an academic job your CV should focus on the following: PhD granting institution, advisor (including postdoc advisor if you have one), and papers. So what are the different types of jobs? So to simplify, your salary will come from teaching (tuition) and research (grants). 1) Soft money university positions: examples are Hopkins and Harvard Biostat. 1a) Some schools of medicine have Biostatistics units that are 100% soft money. 2) Hard money positions: examples are Berkeley and Stanford Stat. 3) Research associate positions: examples are jobs in schools of medicine in departments other than Stat/Biostat. 4) Industry: typically 100% hard.

Update: I reader points out that I forgot: Ok, that is it for now. Distribution Fitting, Formulate Hypotheses. General Purpose In some research applications, we can formulate hypotheses about the specific distribution of the variable of interest. For example, variables whose values are determined by an infinite number of independent random events will be distributed following the normal distribution: we can think of a person's height as being the result of very many independent factors such as numerous specific genetic predispositions, early childhood diseases, nutrition, etc. (see the animation below for an example of the normal distribution). As a result, height tends to be normally distributed in the U.S. population.

Another common application where distribution fitting procedures are useful is when we want to verify the assumption of normality before using some parametric test (see General Purpose of Nonparametric Tests). Fit of the Observed Distribution For predictive purposes it is often desirable to understand the shape of the underlying distribution of the population.

Where f(x) = G (? Cross Validated. Stats With Cats. Statistics. Quick-R: Home Page. Statistics, Probability, and Survey Sampling.