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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Query.. |

Date |
Tue, 16 Apr 2013 18:43:48 -0400 |

Peter, That's a reasonable request. I'm not able to give an extensive list at the moment, but I can give a few examples. I have the third edition. Let me repeat that I have found the book very helpful in learning to use Stata. At the end of Section 5.6 (page 112) the discussion of the box plot says, "Lines extend from the edge of the dark-gray box 1.5 box lengths, or until they reach the largest of smallest cases. Beyond this, there are some dots representing outliers or extreme values." In fact, the lines extend only as far as the most extreme observations that are not more than 1.5 box lengths from the edge of the box. Observations more extreme than that are plotted individually, but they are not necessarily outliers. In exploratory data analysis, such observations are called "outside." They deserve to be investigated, and they might then be determined to be outliers. In Section 10.3 (page 248) the interpretation of unstandardized regression coefficients is oversimplified and unsatisfactory: "For example, a 1-unit change [increase] in com3, identification with the community, produces a 0.05-unit change in env.con, environmental concern, holding all other variables constant." The problem is the phrase "holding all other variables constant," which does not reflect the way multiple regression works. I realize that other books give the same interpretation, but that does not make it satisfactory. The students who learn about multiple regression from those books are being done a disservice. The title of Section 10.5 asks "Is the dependent variable normally distributed?" This is not a useful question. The component of the regression that one would like to have follow a normal distribution is the fluctuations or "errors," whose behavior is examined via the residuals (discussed in Section 10.6). I was delighted to see the hanging rootgram in Figure 10.4 (page 252). An important reason for using a hanging rootgram (or histogram) is that it brings the departures from the fitted normal density together along the horizontal line at 0. A suspended rootogram would be better, because then positive deviations would extend up from that line and negative deviations would extend down. On page 253, the discussion of values of kurtosis that depart from that for the normal distribution (3.00) is reversed: "A value of less than 3.00 means that the tails are too thick (hence, too flat in the middle), and a value of greater than 3.00 means that the tails are too thin (hence, too peaked in the middle)." In fact, heavier-than-normal tails correspond to kurtosis > 3, and lighter-than-normal tails correspond to kurtosis < 3. The discussion on page 254 confuses robust regression with "robust" standard errors. The -regress- command continues to use ordinary least squares. David Hoaglin On Tue, Apr 16, 2013 at 11:45 AM, Lachenbruch, Peter <Peter.Lachenbruch@oregonstate.edu> wrote: > David - > It would be good for you to specify what you find problematic with Acock's book. I've used it and not had any problems - but maybe i'm just ancient and not seeing issues > > Peter A. Lachenbruch, > Professor (retired) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: Query..***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**Re: st: Query..***From:*Nick Cox <njcoxstata@gmail.com>

**References**:**st: Query..***From:*"Hutagalung, Robert" <Robert.Hutagalung@med.uni-jena.de>

**Re: st: Query..***From:*David Hoaglin <dchoaglin@gmail.com>

**AW: st: Query..***From:*"Hutagalung, Robert" <Robert.Hutagalung@med.uni-jena.de>

**RE: st: Query..***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

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