Basic Statistics

Start Date: 02/23/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/basic-statistics

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About Course

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. 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). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. 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. We will discuss confidence intervals and significance tests. 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.

Course Syllabus

In this module we'll consider the basics of statistics. But before we start, we'll give you a broad sense of what the course is about and how it's organized. Are you new to Coursera or still deciding whether this is the course for you? Then make sure to check out the 'Course introduction' and 'What to expect from this course' sections below, so you'll have the essential information you need to decide and to do well in this course! If you have any questions about the course format, deadlines or grading, you'll probably find the answers here. Are you a Coursera veteran and ready to get started? Then you might want to skip ahead to the first course topic: 'Exploring data'. You can always check the general information later. Veterans and newbies alike: Don't forget to introduce yourself in the 'meet and greet' forum!

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Course Introduction

Basic Statistics Statistical analysis is the application of mathematical and statistical principles to the problem of population population growth and to the problem of measuring population changes. This course covers the basics of population growth and the concepts of probability, statistical analysis, and modeling.Module 1 Module 2 Module 3 Module 4 Biology: From Cells to Nations This course introduces the fundamental concepts of biology and describes the biology of modern diseases. It covers the genetic basis of degenerative diseases, the epidemiology of such diseases, and the treatment of such diseases. The course also describes the policy of the WHO-supported Alliance for Healthier Generations. It includes an overview of the progress of the Alliance, including the current status of its member countries. It reviews current research and theories on prevention and treatment of degenerative diseases and provides an overview of the epidemiology of such diseases. It also reviews current research in immunology and on the threats to health posed by infectious diseases. It reviews the international policies and programs aimed at promoting health and advancing the science of prevention and treatment of degenerative diseases. Finally, it reviews the traditional and emerging areas of biotechnology, especially in areas of infectious diseases, as well as the role of the multidisciplinary teams working in this field. Each module is introduced and explained in the context of the current research and the teaching of the course. The course is divided into seven sessions, each covering a particular theme

Course Tag

Statistics Confidence Interval Statistical Hypothesis Testing R Programming

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