is a word with a variety of meanings. To the man in the street, it most often means simply a collection of numbers, such as the number of people living in a country or city, a stock exchange index, or the rate of inflation. These all come under the heading of descriptive statistics, in which items are counted or measured and the results are combined in various ways to give useful results. That type of statistics certainly has its uses in engineering. But another type of statistics will engage our attention to a much greater extent. That is inferential statistics or statistical inference. For example, it is often not practical to measure all the items produced by a process. Instead, we very frequently take a sample and measure the relevant quantity on each member of the sample. We infer something about all the items of interest from our knowledge of the sample. A particular characteristic of all the items we are interested in constituting a population. Measurements of the diameter of all possible bolts as they come off a production process would make up a particular population.
Good Online Courses for Learning Probability and Statistics
Here we enlisted online courses for learning Probability and Statistics efficiently and effectively. It includes both paid and free learning resources available online to help you learn Probability and Statistics. These courses are suitable for beginners, intermediate learners as well as experts.
This course offers how to communicate statistical results, critique data-based claims, evaluate data-based decisions and visualize data with R. It is created and taught by Mine Cetinkaya-Rundel, Associate Professor of the Practice; David Banks, Professor of the Practice; Colin Rundel, Assistant Professor of the Practice and Merlise A Clyde, Professor. This is an ideal choice if you want to learn Probability and Statistics with R.
This course commences from the basics of probability and statistics before moving on to data analysis techniques and machine learning algorithms. It is suggested that you need to have college-level calculus, mathematical reasoning, and python programming proficiency to make the most of this certification.
This workshop is ideal for starters and people with intermediate level understanding. It will teach you probability, sampling, regression, and decision analysis. You'll be able to pass any introductory statistics course by the end of this workshop.
This is a beginner level program, so no specific prerequisite is required for enrollment. You'll learn to discover the principles of scientific methods in the behavioral and social sciences. You'll also learn about data collection, description, analysis and interpretation in qualitative research. This course covers the concepts of statistics so that you can use it to find the solution to various issues. Gain practical experience and perform necessary tests using software introduced in the courses. Collaborate with your fellow learners for the capstone project and formulate a research hypothesis and perform the investigation and analysis. Finally, complete all the graded assignments and assessments to earn the completion badge.
In this course, you'll learn about the core stats required for a career in data science. It will help you master Statistical Significance, Confidence Intervals and a lot more. This course covers:
- Normal Distribution.
- Standard Deviation.
- Sampling Distribution.
- Central Limit Theorem.
- Hypothesis Testing for Means and Proportion.
- Z - Score and Z - Tables.
- t - Score and t - Tables.
Books Available for Learning Probability and Statistics
Books are the best supplement for learning anything well concisely. It will provide everything you need and make yourself a pro. Books are the best comprehensive reference and it will provide you the complete outline in brief. There are several books written by the experts which will help you to go through step-by-step. Make a start and shape up your plan to learn in a more efficient and effective way.
This book assumes that you have a certain degree of mathematical maturity, but gives you very thorough proofs of the basic concepts of rigorous probability.
This is a two-volume book and the first volume is what will likely interest a beginner because it covers discrete probability. The book tends to treat probability as a theory on its own.
This is a good book if you are new to statistics & probability while simultaneously getting started with a programming language. The book supports R and is written in a casual humorous way making it an easy read.
This book is a great compilation that covers quite a bit of puzzle. What I like about these puzzles are that they are all tractable and don't require too much advanced mathematics to solve.
This is an outstanding book for those with a strong math background. It covers everything that one would learn in a one-year statistics course and more, including lots of sections on Bayesian methods.
This is a great book to own. The second half of the book may require some knowledge of calculus. It appears to be the right mix for someone who wants to learn but doesn't want to be scared with the lemmas.