Download Applied Regression Modeling, Second Edition by Iain Pardoe(auth.) PDF

By Iain Pardoe(auth.)

Praise for the First Edition

"The cognizance to element is striking. The publication is especially good written and the writer is very cautious together with his descriptions . . . the examples are wonderful." ?The American Statistician

Fully revised to mirror the newest methodologies and rising purposes, Applied Regression Modeling, moment Edition maintains to focus on some great benefits of statistical equipment, particularly regression research and modeling, for knowing, studying, and analyzing multivariate info in company, technological know-how, and social technology applications.

The writer makes use of a bounty of real-life examples, case experiences, illustrations, and pics to introduce readers to the area of regression research utilizing a variety of software program programs, together with R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a transparent and cautious writing variety, the publication introduces modeling extensions that illustrate extra complicated regression thoughts, together with logistic regression, Poisson regression, discrete selection versions, multilevel versions, and Bayesian modeling.

In addition, the Second Edition good points rationalization and growth of not easy issues, such as:

  • Transformations, indicator variables, and interaction
  • Testing version assumptions
  • Nonconstant variance
  • Autocorrelation
  • Variable choice methods
  • Model development and graphical interpretation

Throughout the publication, datasets and examples were up-to-date and extra difficulties are integrated on the finish of every bankruptcy, permitting readers to check their comprehension of the awarded fabric. additionally, a comparable web site positive factors the book's datasets, presentation slides, distinctive statistical software program directions, and studying assets together with extra difficulties and tutorial videos.

With an intuitive procedure that isn't heavy on mathematical aspect, Applied Regression Modeling, moment Edition is a wonderful publication for classes on statistical regression research on the upper-undergraduate and graduate point. The publication additionally serves as a beneficial source for pros and researchers who make the most of statistical equipment for decision-making of their daily work.

Content:
Chapter 1 Foundations (pages 1–33):
Chapter 2 basic Linear Regression (pages 35–82):
Chapter three a number of Linear Regression (pages 83–135):
Chapter four Regression version development I (pages 137–188):
Chapter five Regression version construction II (pages 189–242):
Chapter 6 Case reviews (pages 243–266):
Chapter 7 Extensions (pages 267–283):

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Extra info for Applied Regression Modeling, Second Edition

Sample text

D) As suggested on page 27, calculate a 90% prediction interval for an individual sale price in this market. 5. (a) A journalist speculates that the population mean of Life is at least 68 years. Based on the sample of 55 countries, a smart statistics student thinks that there is insufficient evidence to conclude this. Do a hypothesis test to show who is correct based on a significance level of 5% [computer help #24]. Hint: Make sure that you lay out all the steps involved—as on page 21—and include a short sentence summarizing your conclusion; that is, who do you think is correct, the journalist or the student?

B) What would we expect the histogram to look like if the data were normal? (c) Construct a QQ-plot of the Salary variable [computer help #22]. (d) What would we expect the QQ-plot to look like if the data were normal? (e) Compute the natural logarithm of quarterback salaries (call this variable Logsat) [computer help #6], and construct a histogram of this Logsal variable [computer help #14]. Hint: The "natural logarithm" transformation (also known as "log to base-e," or by the symbols loge or In) is a way to transform (rescale) skewed data to make them more symmetric and normal.

So for this type of calculation we always use a t-distribution from now on. However, we can't completely forget about the normal distribution yet; it will come into play again in a different context in later chapters. When using a t-distribution, how do we know how many degrees of freedom to use? One way to think about degrees of freedom is in terms of the information provided by the data we are analyzing. Roughly speaking, each data observation provides one degree of freedom (this is where the n in the degrees of freedom formula comes in), but we lose a degree of freedom for each population parameter that we have to estimate.

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