Fundamentals of Statistics covers topics on the introduction, fundamentals, and science of statistics. The book discusses the collection, organization and. the number of copies of a book sold found that raising prices by 1% reduced sales the fundamentals of statistics and introduce you to concepts that are used . Fundamentals of Statistics covers topics on the introduction, fundamentals, and science of statistics. The book discusses the.
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books. Agresti, A. & Finlay, B., Statistical Methods for the Social Sci- ences, 3th Edition. .. A group for which the distribution is bell-shaped is fundamentally. medical-site.info - download Fundamentals of Statistics book online at best prices in india on medical-site.info Read Fundamentals of Statistics book reviews & author details and. Fundamentals of Statistics is the brief version of Statistics: Informed Decisions Using Data. With Fundamentals of Statistics, author and.
Later in the course we will introduce the idea of prior knowledge, which is meant to reflect the knowledge that we bring to a situation. This prior knowledge can vary in its strength, often based on our amount of experience; if I visit a restaurant for the first time I am likely to have a weak expectation of how good it will be, but if I visit a restaurant where I have eaten ten times before, my expectations will be much stronger.
Statistics provides us with a way to describe how new data can be best used to update our beliefs, and in this way there are deep links between statistics and psychology.
In fact, many theories of human and animal learning from psychology are closely aligned with ideas from the new field of machine learning. Machine learning is a field at the interface of statistics and computer science that focuses on how to build computer algorithms that can learn from experience. In this book I will try to blend the two cultures together because both approaches provide useful tools for thinking about data. In the example of the PURE study above, we took more than , numbers and condensed them into ten.
It is this kind of aggregation that is one of the most important concepts in statistics. As we will see, statistics provides us ways to characterize the structure of aggregates of data, and with theoretical foundations that explain why this usually works well.
However, it also means that there will be many people who smoke their entire lives and never get lung cancer. Statistics provides us with the tools to characterize uncertainty, to make decisions under uncertainty, and to make predictions whose uncertainty we can quantify.
The idea of sampling says that we can summarize an entire population based on just a small number of samples from the population, as long as those samples are obtained in the right way. For example, the PURE study enrolled a sample of about , people, but its goal was to provide insights about the billions of humans who make up the population from which those people were sampled. As we already discussed above, the way that the study sample is obtained is critical, as it determines how broadly we can generalize the results.
Another fundamental insight from statistics about sampling is that while larger samples are always better in terms of their ability to accurately represent the entire population , there are diminishing returns as the sample gets larger. In fact, the rate at which the benefit of larger samples decreases follows a simple mathematical rule, growing as the square root of the sample size. The data are consistent with such a relationship, but they are equally consistent with some other factor causing both higher saturated fat and longer life.
For example, it is likely that people who are richer eat more saturated fat and richer people tend to live longer, but their longer life is not necessarily due to fat intake — it could instead be due to better health care, reduced psychological stress, better food quality, or many other factors. In medicine, such a study is referred to as a randomized controlled trial RCT.
To do this, we would sample a group of people, and then assign them to either a treatment group which would be told to increase their saturated fat intake or a control group who would be told to keep eating the same as before. Preface 1. Introduction 2.
The Collection of Data 2. The Classification of Data 2. Graphical Representation of Data 2. Random Sampling 2. Random Numbers 2. How to Use Random Sampling Numbers 3. Elementary Probability 3.
Introduction 3. Mutually Exclusive Events 3. Independent Events 3. Introduction to Permutations and Combinations 3. Probability Distributions 3. Mathematical Expectation and Arithmetic Mean 4. The Binomial and Poisson Distributions 4. The Binomial Distribution 4. The Mean of the Binomial Distribution 4.
The Poisson Distribution 4. The Mean of the Poisson Distribution 4. The Additive Property of the Poisson Distribution 5. Measures of Central Tendency 5. Introduction 5. The Mean 5. The Median 5. The Mode 5. The Geometric Mean 6 Measures of Dispersion 6. Introduction 6. The Range 6. The Mean Deviation 6. The Variance 6. The Coefficient of Variation 7 Continuous Distributions 7.
Introduction 7. The Modal and Median Values 7. Mathematical Expectation, the Mean and the Variance 7.
The Mean Deviation about the Mean 7. Introduction 8. Properties of the Normal or Gaussian Distribution 8. TheAustrian said: I just discovered that I will be using the software program R together with statistics at university some time in the future, yet the most important thing for me is that the concepts such as confidence interval, standard deviation etc are explained with visual figures which demonstrate clearly what is meant by the definitions.
This book looks very nice indeed, everything is highlighted with data from actual case studies and it is organized around type of experiments. This may be a good choice for a supplement to whatever book your course recommends which will probably have a more traditional focus.
If you are willing to try a 'sideways approach', the best introductory book to statistics, for me, is "Intro to Error Analyis" by Taylor. It's amazing how he manages to explain in very simple terms concepts that in 'pure' statistics book always look too abstract.
It is not a rigorous textbook, but in my opinion is the best starting point to approach the subject. Once you've read Taylor you can proceed with the other 'more conventional' and 'pure' introductory statistics textbooks and you will know why certain concepts need to be introduced.
As far as conventional introductory statistics book go, I have yet to find one to impress me. Let's say one I might suggest is Devore's "Probability and Statistics for Engineering and the Sciences" , and for introductory mathematical statistics I'd go with Hoeg's "Intro to Mathematical Statistics" , but they are just two names among many. Taylor's book on the other hand is one of a kind and I believe it will fit your bill.
Oh, BTW: Jun 1, I've spent hours looking around and I think I am going to go with the 2 books Elementary statistics a step by step approach. This book has many detailed calculation examples and visual figures. This book has a wordy way of explaining each equation in statistics how to interprete it https: Want to reply to this thread? Related Threads for: The best EE fundamentals book?