Last edited by Nikoktilar

Thursday, May 21, 2020 | History

6 edition of **Regression Basics** found in the catalog.

- 374 Want to read
- 5 Currently reading

Published
**March 8, 2001**
by Sage Publications, Inc
.

Written in English

- Research methods: general,
- Statistics,
- Research,
- Psychology,
- Sociology,
- Research & Methodology,
- Social Science / Research,
- Regression analysis,
- Reference

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 208 |

ID Numbers | |

Open Library | OL8029159M |

ISBN 10 | 0761924132 |

ISBN 10 | 9780761924135 |

Regression Basics by Leo Kahane is a well written introduction to regression anlysis. The book is easy to read and can be used by any student or researcher who has successfully completed an introductory statistics course. I found the four examples referred to throughout the book diverse and entertaining.4/5(1). Chapter 1 The Basics of Bayesian Statistics. Bayesian statistics mostly involves conditional probability, which is the the probability of an event A given event B, and it can be calculated using the Bayes rule. The concept of conditional probability is widely used in medical testing, in which false positives and false negatives may occur.

Such as a basic understanding of p-values, hypothesis tests, confidence intervals, and correlation. Again, it would be helpful to start with some of that knowledge, but I do explain how those concepts apply to regression. My book focuses on the practical usage of regression and understanding the concepts. Multiple Regression: The Basics 4. Multiple Regression: Special Topics Appendix References Index. Supplements. Data Files. This is a great book to acquaint students with the world of linear models. It is perfect to use in combination with other texts, or as a stand-along book in introductory courses. The Lewis-Beck’s have updated the.

It depends what you want from such a book and what your background is. E.g. do you want proofs and theorems or just practical advice? Have you had calculus? What field are you going into? etc. However. Gelman and Hill Data Analysis Using Reg. The basics A. Types of variables Your variables may take several forms, and it will be important later that you are aware of, and understand, the nature of your variables. The following variables are those which StatLab Workshop Series Introduction to Regression/Data AnalysisFile Size: KB.

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Regression Basics by Leo Kahane is a well written introduction to regression anlysis. The book is easy to read and can be used by any student or researcher who has successfully completed an introductory statistics course.

I found the four examples referred to throughout the book diverse and entertaining/5(6). Chapter 8. Regression Basics Regression analysis, like most multivariate statistics, allows you to infer that there is a relationship between two or more variables.

These relationships are seldom exact because there is variation caused by many variables, not just the variables being : Thomas K. Tiemann, Tiemann, K Thomas. This book shows readers how to get the most from regression by providing a friendly, non-technical introduction to the subject.

Accessible to anyone with only an introductory statistics background, the book begins with the simplest, two-variable linear model and gradually builds towards models of more complexity, such as multivariate regression.

Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of ible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Regression Basics by Leo Kahane is a well written introduction to regression anlysis.

The book is easy to read and can be used by any student or researcher who has successfully completed an introductory statistics course/5(6). Using a friendly, nontechnical approach, the Regression Basics book Edition of Regression Basics introduces readers to the fundamentals of regression.

Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable Price: $ Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers.

Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity.

Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not. Regression basics Item Preview remove-circle Accessible to anyone with only an introductory statistics background, the book begins with the simplest, two-variable linear model and gradually builds towards models of more complexity, such as multivariate regression.

Kahane uses three engaging examples to illustrate regression : Interpretation of coefficients in multiple regression page 13 The interpretations are more complicated than in a simple regression.

Also, we need to think about interpretations after logarithms have been used. Pathologies in interpreting regression coefficients page 15 Just when you thought you knew what regression coefficients meant 1File Size: KB.

In simple linear regression, the topic of this section, the predictions of Y when plotted as a function of X form a straight line. The example data in Table 1 are plotted in Figure 1. You can see that there is a positive relationship between X and Y.

If you were going to predict Y from X, the higher the value of X, the higher your prediction of Y. Get this from a library. Regression basics. [Leo H Kahane] -- Although many people have PCs with software capable of performing regression techniques, only a few know how to capitalize on the flexibility and wide application of regression analysis.

This book. COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle.

Regression Analysis: Basic Concepts Allin Cottrell 1 The simple linear model Suppose we reckon that some variable of interest, y, is ‘driven by’ some other variable x. We then call y the dependent variable and x the independent variable.

In addition, suppose that the File Size: 57KB. Linear Regression & Correlation. If you are looking for a short beginners guide packed with visual examples, this book is for you.

Linear Regression is a way of simplifying a group of data into a single equation. For instance, we all know Moore’s law: that the number of transistors on a computer chip doubles every two years/5(59).

Regression analysis is the “go-to method in analytics,” says Redman. And smart companies use it to make decisions about all sorts of business issues. Explanation versus Prediction. Before we even begin to discuss regression, we make a bold announcement: STAT is not a course about inference.

It is very possible that there will be zero causal claims in this book. While it would certainly be nice (but extremely difficult) to uncover causal relationships, our focus will be on predictive relationships.

In Chapter 2, the regression model assumptions were introduced with a brief explanation about theirin Chapter 4, we added the assumption of “no perfect multicollinearity.”It was noted that if these assumptions are met, then OLS produces the best possible estimation of our model, or OLS is BLUE.

TY - BOOK. T1 - Practical Regression. T2 - Regression Basics. AU - Dranove, David Stuart. PY - Y1 - N2 - This is the second in a series of lecture notes which, if tied together into a textbook, might be entitled "Practical Regression."Author: David Stuart Dranove.

b) Linear Regression is sensitive to outliers: Linear regression tries to fit a best line among the training data. If the dataset has some outlying extreme values that do not fit a general pattern, linear regression models can be heavily impacted by the presence of outliers. We will have to watch out for these outliers and normally remove then.

Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Bilodeau and Brenner: Theory of Multivariate Statistics Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: An Introduction to Times Series and Forecasting Chow and Teicher: Probability Theory: .This book shows readers how to get the most from regression by providing a friendly, non-technical introduction to the subject.

Accessible to anyone with only an introductory statistics background, the book begins with the simplest, two-variable linear model and gradually builds towards models of more complexity, such as multivariate : $Basics of linear regression; Linear hypothesis testing; Ridge regression; There are numerous books 1 2 available on the theory of linear regression.

What is the purpose to write a chapter about these models in another book of data science? Many audience would be interested in how to implement their own regression models rather than using the.