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Amazon.com: Statistics, Third Edition (838): David Freedman, Robert Pisani. Get your Kindle here, or download a FREE Kindle Reading App. Oct 17, 2008 - David A. Freedman was Professor of Statistics at the University of California, Berkeley. He also taught. [PDF-Preprint] Statistical Science vol.
- Statistics. by David Freedman; Robert Pisani; Roger PurvesReview by: Gary A. SimonJournal of the American Statistical Association, Vol. 74, No. 368 (Dec., 1979), pp. 927-928Published by: American Statistical AssociationStable URL: http://www.jstor.org/stable/2286430 .Accessed: 15/06/2014 05:25Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp.JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact [email protected]..American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journalof the American Statistical Association.http://www.jstor.orgThis content downloaded from 195.78.108.147 on Sun, 15 Jun 2014 05:25:10 AMAll use subject to JSTOR Terms and Conditionshttp://www.jstor.org/action/showPublisher?publisherCode=astatahttp://www.jstor.org/stable/2286430?origin=JSTOR-pdfhttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp
- Book Reviews 927An Introduction to Bilinear Time Series Models. C.W. Granger and A.P. Andersen. Gottingen, Germany: Vandenhoeck and Ruprecht, 1978. 94 pp. $12.00 (paperback).This excellent monograph introduces statisticians to a poten- tially useful class of nonlinear time series models in a most ap- pealing fashion. The exposition consists almost exclusively of examples designed to capture the fancy of the time-domain analyst. A case is made for use of a simple diagnostic procedure-examina- tion of the sample autocorrelation function of the series of squares- that will spur immediate empirical experimentation by practitioners.The authors' main points are these:1. Fits of linear time-domain models that are judged adequate, in the sense that residual autocorrelations (or spectra) signal white- noise errors, tempt analysts to ignore the fitting of nonlinear models that can forecast consistently better than the linear models.2. Bilinear models are nonlinear models that are intuitively appealing and can be handled readily by anyone familiar with the traditional linear models.The authors offer persuasive anecdotal evidence for these points while admitting that many theoretical questions about bilinear models remain to be answered, especially conditions for stationarity and invertibility of the models.Three features of the authors' case that I found appealing are1. Presentationt of simple substantive models that are bilinear. As an illustration, suppose the true rate of return of series St} is given by the MA (1) modelXi(X-X4t - xi) = et + be-e i -i < b < 1where { tt} is a white-noise sequence of independent zero mean, variance a2 random variables. The model may be writtenX = Xt-1 + EtXt-i + b.t-lXt-g Xwhich is a bilinear model. Terms involving products of the errors and the data are the distinguishing characteristic of such models. This model can be further complicated by assuming that the actual data are of the form yt = xt + ?t, where litl is a white-noise disturbance.2. Demonstration of the time series structure of the square. Let {xei be generated by a bilinear model that enjoys suitable sta- tionarity restrictions. Let lyet be a series generated by an ARMA model that has identical second-order moment properties. The authors produce examples to show that the series lXt2 and Iyt2 I will not have identical second-order moment properties. Thus examination of the autocorrelation function of lXt2 suggests itself as a technique for identifying bilinearity in practice.3. Analysis of both sinulated and actual series. These examples yield considerable insight. For instance, the authors analyze Series B of Box and Jenkins (1970): IBM daily common stock closing prices for 169 trading days starting 17 May 1961. The linear model fitted by Box and Jenkins to the price change, xet, isXe = et + .26 eet-Iwith residual variance 24.8. Some autocorrelations of the residuals and the squared residuals are as follows:rk( O) 1 2 3 4 5 6rk(E) -.01 -.07 -.12 .16 0 -.14 rk (f2) .31 .06 -.04 -.06 -.07 -.09Box and Jenkins judged the rkt'i) not inconsistent with the as- sumption of white-noise errors, but the large re (V2) suggests bi- linearity. Granger and Andersen fitted the bilinear model=t .02 1e-ent-i + f twhere In{ t is assumed to be white noise with estimated variance 23.5. Forecasts from this model improved forecast MSE, relative to the linear model, by 10.7 percent over a 15-day period and 8.2 percent over a 30-day period.Granger and Andersen emphasize the use of bilinear models as tools to improve forecasting accuracy. I would suggest another potential use. My guess is that bilinearity frequently results from excluding important variables from our models, as we almostinvariably do when we do univariate time series analysis. As an illustration, I constructed an AR(3) model for daily average flow of a certain Wisconsin river at a certain site. The autocorrelations of the residuals signaled white-noise errors, but those of the squared residuals signaled bilinearity. Then I added a simple polynomial in daily precipitation and its lags to the model. Neither the residuals nor the squared residuals signaled any problems. In this applica- tion the bilinear analysis was used as a diagnostic tool to help decide the merits of adding a new variable to the model.While bilinear models may be useful ad hoc tools for producing better forecasts, they will have a more profound influence on time series analysis if they can assist us in thinking more deeply about variables that should enter our models. The extent to which they can do this cannot be determined until more theoretical and em- pirical work has been done.ROBEJRT B. MILLER University of Wisconsin, MadisonREFERENCEBox, G.E.P., and Jenkins, G.M. (1970), Time Series Analysis, Forecasting and Control, San Francisco: Holden-Day.Statistics. David Freedman, Robert Pisani, and Roger Purves. New York: W.W. Norton and Co., 1978. xv + 506 pp. $13.95.Consider the following statistical issue. Two exams were given in a course. The first exam had average score 50 and standard deviation 10. The second exam had average score 40 and standard deviation 15. The correlation between the two scores was +.4. If a student scored 60 points on the first exam, what score would you expect the student to have on the second exam?The solution goes something like this. The student was one standard deviation above average on the first exam. The student should be one standard deviation above average on the second exam, except for the regression effect. The regression effect means that he or she should be predicted to be +.4 X one standard de- viation -.4 standard deviation above average on the second exam. This prediction is then 40 + .4 X 15 = 40 + 6 = 46.Believe it or not, this solution will be produced by many students getting their basic course from Statistics by Freedman, Pisani, and Purves-a clever intuition-based introduction to the subject.Besides regression and correlation, the student will learn about controlled studies versuis observational studies, histograms, averages, standard deviations, the normal curve for data, elementary proba- bility, expected value and standard error, sampling and associated standard errors, confidence intervals, and several hypothesis tests. There are interesting chapters on the Current Population Survey and on chance models in genetics. All the material can be covered in a one-semester course.The regression-correlation problem, by the way, comes very early in the text. This is genuinely interesting to most students, and it can win their sympathy for the subject matter.The authors have clearly thought about the process of teaching statistics to mathematically disinclined college undergraduates. A valuable teaching guide accompanies the text. This guide con- tains chapter-by-chapter advice and solutions to the exercises. It also contains a diagnostic pretest, along with the results when the test was given at the University of California, Berkeley. Users of the text will be shocked when they learn how poorly the students at Berkeley performed; they will be shocked again when they administer the pretest to their own students.The authors try to develop a feeling for data. The student looks at many scatterplots before seeing a formal method for finding correlations. There are many exercises geared toward making the stuLdent appreciate the meaning of a standard deviation; most students will learn to look at a histogram and make a reasonable guess at the standard deviation.The statistical sampling process is conceptualized as drawing tickets from a box. This idea is not unique to Freedman, Pisani, and Purves, but here the idea is heavily emphasized. The student learns that the ticket-drawing model is the heart of statistical thinking. P. 407 reminds the reader, 'Statistical inference can be justified only in terms of an explicit chance model for the data. No box, no inference.' In many places in the text, the studentThis content downloaded from 195.78.108.147 on Sun, 15 Jun 2014 05:25:10 AMAll use subject to JSTOR Terms and Conditionshttp://www.jstor.org/page/info/about/policies/terms.jsp
- 928 Journal of the American Statistical Association, December 1979is asked to formulate a problem in terms of a box model, telling what is on the tickets, and noting how many tickets there are. Processes leading to successes and failures are conceptualized as 0-1 boxes. Processes leading to measurements have box models that may be largely summarized by the average of the box and the SD of the box.The reader is not overwhelmed by new symbols and exotic vocabulary. There are no Greek symbols. The word 'probability' has been replaced by 'chance.' There are no unions or intersec- tions. Mutually exclusive events are called 'incompatible.' Events themselves are called 'outcomes.' There are no summation signs. The letter n for sample size is not used-it is replaced by the ex- pression 'number of draws,' referring to a box model.The text contains many quick-and-easy examples for which the answers are in the back of the book. These are well suited for classroom discussion, and they can be used for relief from the usual lecture routine. There are many footnotes; the inquisitive reader will find these rewarding.An impressive innovation is the use of the SD line in regression problems. This is a line passing through the point of averages (which is (x, y) in usual notation) and having slope (sign of r) (SD of Y)/ (SD of X). The SD line is one that is instinctively drawn on a scatterplot, and the student learns why the actual regression line is different from the SD line. (My colleague Jerry Dallal has pointed out to me that the SD line need not have a slope close to that of the first principal component.)This text asks the student to do rather little computation. I sup- port this point of view; it has been my experience that even the well-intentioned student with a calculator will not be able to find a correlation coefficient correctly. It is far better that he or she appreciate what a correlation coefficient ought to be. Moreover, on all but the smallest data sets, most statistics will be computer generated. Problems in the text usually include averages, standard deviations, and correlations as given information. The student still has to do arithmetic to find standard errors, confidence limits, and chi-squared values. These calculations are not terrible, and the computational effort does not grow with the sample size.Even though calculations are not vital, techniques are still given. The standard deviation is found by (a) obtaining the average, (b) subtracting the average from each member of the list, (c) com- puting the rms (root mean square) of the resulting list. This is just the use of SD = (1/n _,_1 (xi-x)2)* without the symbols. There is a technical note (p. 65) to tell the reader that one can also computeSD = (average of (entries)2 - (average of entries)2)1 .The 1/n definition of the SD makes things easy to understand at this stage, although a bit of a problem is created later, since the t statistic requires the 1/ (n - 1) form, and the new version of the standard deviation is called SD+.Correlation coefficients can also be calculated. The technique is startling, although it makes sense at this level. To find the cor- relation coefficient, the student is given this method (p. 124): 'Convert each variable, to standard units. The average of the products gives the correlation coefficient.' This is, of course, an appalling computational technique. The student will only perform this arithmetic on a few very small data sets with round numbers. With a data set consisting of 20 pairs, the student is virtually certain to get the wrong answer with any computational method.The authors are very careful to give the reader only one method of doing any calculation, except perhaps with modifications in technical notes or footnotes. At least two of these situations are troubling to me. The first deals with the standard error of an aver- age. Because problems dealing with things like total number of successes use sample totals (of zeros and ones), the reader is taught that SE of sample total = estimate of SD X (sample size)*. Since other problems will deal with sample averages, the reader is later told that SE of sample average = SE of sample total/sample size. This is done on p. 325 for percentages and on p. 373 for other kinds of data. The authors explain it this way in their instructor's manual:Section 20.2 presents our version of (pq/n)*, except that the formula doesn't appear. This may seem a bit idiosyncratic, and we would like to explain why we moved from the con- ventional formula to our version.The students seemed to find (pq) rather hard to swallow. So we teach them to make a model with 0's and l's in the box. Since we are working in percents, the formula becomes(SD of 0-1 box/V/n) X 100% .We presented it this way for several years, but there was still a hitch. The students were willing to compute an SE as SD X V/n in part V (where SE of a sum was explained-G.S.). When they hit part VI (requiring SE of a percentage-G.S.), there was a tremendous shifting of gears needed to compute the SE as SD/V/n. Once they changed over, they stopped being able to compute the SE for a sum as SD X Vn: they insisted on dividing. We tried hard to explain that there was one formula to use with sums and another for averages, but they wouldn't buy it.Eventually, we decided to have only one formula: the SE for a sum. Everything else is worked out fr...
Professor Freedman passed away on 17 October 2008. Obituaries may be found at statistics.berkeley.edu/~stark/Preprints/dafObituary.htmandberkeley.edu/news/media/releases/2008/10/20_freedman.shtml.
Remembrances of Professor Freedman offered by family, friends and colleaguesat a memorial on 2 December 2008 may be found at statistics.berkeley.edu/~census/David_Freedman_Memorial.pdf.
David A. Freedman was Professor of Statistics at the Universityof California, Berkeley. He also taught in Athens, Caracas,Jerusalem, Kuwait, London, and Mexico City. He is the author ofseveral books, including a widely-used elementary text. He hasabout 200 papers in the professional literature, and was amember of the American Academy of Arts and Sciences. In 2003, hereceived the John J. Carty Award for the Advancement of Sciencefrom the National Academy of Sciences, recognizing his“profound contributions to the theory and practice ofstatistics.”
Freedman worked on martingale inequalities, Markov processes,de Finetti’s theorem, consistency of Bayes estimates, sampling,the bootstrap, procedures for testing and evaluating models,census adjustment, epidemiology, statistics and the law. Hisresearch interests included methods for causal inference,and the behavior of standard statistical models undernon-standard conditions; for example, how do regression modelsbehave when fitted to data from randomized experiments? (Not asexpected, is the short answer.)
Freedman consulted for the Carnegie Commission, the City ofSan Francisco, and the Federal Reserve, as well as severaldepartments of the U.S. government. He testified as an expertwitness on statistics in law cases that involve employmentdiscrimination, fair loan practices, duplicate signatures onpetitions, railroad taxation, ecological inference, flightpatterns of golf balls, price scanner errors, sampling techniques,and census adjustment.
Recent Books
D.A. Freedman. Statistical Models: Theory and Practice.Cambridge University Press (2005).[Cambridge website][What’s in this book?][Reviews][Student comments][Typography][Data sets][Schedule][Project][Errata][Supplementary Lecture Notes]
D.A. Freedman, R. Pisani, and R.A. Purves. Statistics.W.W. Norton, Inc. New York (1978).[Norton]2nd edition in 1991.Spanish translation in 1993.Chinese translation in 1995.3rd edition in 1998.Hungarian translation in 2005.4th edition in 2007.[Errata] for 1st printing of 4th edition
Recent Papers
D.A. Freedman and J. Sekhon.“Endogeneity in probit models.”[PDF-Preprint]
D.A. Freedman.“Diagnostics cannot have much power against generalalternatives.”[PDF-Preprint]To appear in Journal of Forecasting.
D.A. Freedman.“Do the N’s justify the means.”[WORD-Preprint]
S.P. Klein, D.A. Freedman, R. Shavelson and R. Bolus.“Assessing school effectiveness.”[PDF-Preprint]To appear in Evaluation Review. Vol. 32 (2008) Dec.
D.A. Freedman.“Randomization does not justify logistic regression.”[PDF-Preprint]Statistical Science vol. 23 (2008) pp. 237–49.
D.A. Freedman and R.A. Berk.“On weighting regressions by propensity scores.”[PDF-Preprint]Evaluation Review vol. 32 (2008) pp. 392–409.
D.A. Freedman.“Some general theory for weighted regressions.”[PDF-Preprint]
D.A. Freedman.“On types of scientific enquiry: Nine success storiesin medical research.”To appear in The Oxford Handbook of PoliticalMethodology pp. 300–18. Janet M. Box-Steffensmeier, HenryE. Brady and David Collier, editors. [PDF-Preprint]
D.A. Freedman.“On regression adjustments in experimentswith several treatments.” Annals of Applied Statistics vol. 2 (2008) pp. 176–96.[PDF-Preprint]
D.A. Freedman.“Survival analysis: A primer.”The American Statistician vol. 62 (2008) pp. 110–119.[PDF-Preprint]
D.A. Freedman.“Oasis or mirage?”CHANCE Magazinevol. 21 no. 1 (2008) pp. 59–61.[PDF-Preprint]
D.A. Freedman.“On regression adjustments to experimental data.” Advances in Applied Mathematicsvol. 40 (2008) pp. 180–193.[PDF-Preprint]
D.A. Freedman and K.W. Wachter. “Methods for Census 2000and statistical adjustments.” In SocialScience Methodology. Sage (2007) pp. 232–45. StevenTurner and William Outhwaite, editors[PDF-Preprint]
T. Dunning and D.A. Freedman. “Modeling selectioneffects.” In Social Science Methodology. Sage (2007) pp. 225–31. Steven Turner and WilliamOuthwaite, editors[PDF-Preprint]
D.A. Freedman.“How can the score test be inconsistent?”The American Statisticianvol. 61 (2007) pp. 291–295[PDF-Preprint]
D.A. Freedman.“Statistical models for causation:What inferential leverage do they provide?”Evaluation Review vol. 30 (2006) pp. 691–713.[PDF-Preprint]
D.A. Freedman.“On the so-called ‘Huber Sandwich Estimator’and ‘robust’ standard errors.”The American Statistician vol. 60 (2006) pp. 299–302.[PDF-Preprint]
D.B. Petitti and D.A. Freedman.“Invited commentary: How far can epidemiologists get with statisticaladjustment?”American Journal of Epidemiologyvol. 162 (2005) pp. 415–18.[AJE website]
D.A. Freedman and D.B. Petitti.“Hormone replacement therapy does not save lives:Comments on the Women’s Health Initiative”Biometricsvol. 61 (2005) pp. 918–920.[TXT-Preprint]
D.A. Freedman.“Linear statistical models for causation: A critical review.”In the Wiley Encyclopedia of Statistics in BehavioralScience (2005). B. Everitt and D. Howell, eds.[PDF]
M. L. Eaton and D. A. Freedman.“Dutch book against ‘objective’ priors.” The Bernoulli Journal, vol. 10 (2004) pp. 861–72.[PDF-Preprint]
D. A. Freedman.“Notes on the Dutch book argument.”[PDF]
D.A. Freedman. “On specifying graphical models for causation,and the identification problem.” Evaluation Review (2004) vol. 26 pp. 267–93.Reprinted in Identification and Inference for EconometricModels: Essays in Honor of Thomas Rothenberg,Cambridge University Press (2005) pp. 56–79,D.W.K. Andrews and J.H. Stock, eds,[PDF-Preprint]
D. A. Freedman, D. B. Petitti, and J. M. Robins.“On the efficacy of screening for breast cancer.”International Journal of Epidemiology, vol. 33 (2004) pp. 43–73.[IJE][PDF-Preprint]Correspondence, pp. 1404–6.
P. Diaconis and D. A. Freedman.“The Markov moment problem and de Finetti’s theorem:Parts I and II.” Mathematische Zeitschrift,vol. 247 (2004) pp. 183–212.[PDF-Preprint]
D.A. Freedman. “The ecological fallacy.”In the Encyclopedia of Social Science ResearchMethods. Sage Publications (2004)Vol. 1 p. 293.M. Lewis-Beck, A. Bryman, and T. F. Liao, eds.[TXT-Preprint]
D.A. Freedman. “Sampling.”In the Encyclopedia of Social Science ResearchMethods. Sage Publications (2004)Vol. 3 pp. 986–990.M. Lewis-Beck, A. Bryman, and T. F. Liao, eds.[PDF-Preprint]
D.A. Freedman and K.W. Wachter. “On the likelihood of improving the accuracyof the census through statistical adjustment.” InScience and Statistics: A Festschrift for Terry Speed.Institute of MathematicalStatistics Monograph 40 (2003) pp. 197–230.D. R. Goldstein, ed.[PDF-Preprint]
D.A. Freedman and P.B. Stark. “What is the probabilityof an earthquake?” InEarthquake Science and Seismic RiskReduction.NATO Science Series IV: Earthand Environmental Sciences, vol. 32, Kluwer, Dordrecht, TheNetherlands (2003) pp. 201–213.F. Mulargia and R. J. Geller, eds.[PDF-Preprint]
R.A. Berk and D.A. Freedman. “Statistical assumptionsas empirical commitments.”In Law, Punishment, and Social Control: Essays in Honor ofSheldon Messinger, 2nd ed.Aldine de Gruyter (2003) pp. 235–54.T. G. Blomberg and S. Cohen, eds.[PDF-Preprint]
D.A. Freedman and D.B. Petitti. “Salt, bloodpressure, and public policy.” InternationalJournal of Epidemiology vol. 31 (2002) pp. 319–320.[TXT-Preprint]
D.A. Freedman. “Ecological inference and the ecological fallacy.”International Encyclopedia of the Social & BehavioralSciences. Elsevier (2001) vol. 6 pp. 4027–30.Neil J. Smelser and Paul B. Baltes, eds.[PDF-Preprint]
D.A. Freedman and K.W. Wachter. “Census adjustment: Statisticalpromise or statistical illusion?” Society vol. 39 (2001) pp. 26–33[PDF-Preprint]
D.A. Freedman and P.B. Stark. “The swine flu vaccine andGuillain-Barré syndrome.” Law and Contemporary Problems,vol. 64 (2001) pp. 49–62[Duke Law Journals]
D.A. Freedman and D.B. Petitti. “Salt and blood pressure: Conventionalwisdom reconsidered.” Evaluation Review,vol. 25 (2001) pp. 267–87[PDF-preprint]
D.A. Freedman, P.B. Stark, and K.W. Wachter.“A probability model for census adjustment.”Mathematical Population Studies,vol. 9 (2001) pp. 165–80[PDF-preprint]
K.W. Wachter and D.A. Freedman.“The fifth cell: Correlation bias in U.S.census adjustment.” Evaluation Review,vol. 24 (2000) pp. 191–211[PDF-preprint]
K.W. Wachter and D.A. Freedman. “Measuring local heterogeneitywith 1990 U.S. census data.” Demographic Research, vol. 3 (2000)art. 10[Demographic Research][PDF]
D.H. Kaye and D.A. Freedman. “Reference guide on statistics.” 2nded. Federal Judicial Center, Washington, D.C. (2000)[PDF]
L.D. Brown, M.L. Eaton, D.A. Freedman,S.P. Klein, R.A. Olshen, K.W. Wachter,M.T. Wells, and D. Ylvisaker.“Statisticalcontroversies in Census 2000.” Jurimetrics, vol. 39 (1999)pp. 347–75[PDF-preprint]
D.A. Freedman. “From association to causation: Some remarks onthe history of statistics.” Statistical Science, vol. 14 (1999)pp. 243–58. Reprinted in Journal de la Société Francaise deStatistique, vol. 140 (1999) pp. 5–32 and inStochastic Musings: Perspectives from thePioneers of the Late 20th Century.Lawrence Erlbaum Associates (2003) pp. 45–71.J. Panaretos, ed.[PDF-preprint]
D.A. Freedman and P.B. Stark. “The swine flu vaccine andGuillain-Barré syndrome.” Evaluation Review,vol. 23 (1999) pp. 619–47[PDF-preprint]
D.A. Freedman and P. Humphreys. “Are there algorithms that discovercausal structure?” Synthese, vol. 121 (1999) pp. 29–54[PDF-preprint]
D.A. Freedman. “On the Bernstein-von Mises theorem with infinitedimensional parameters.” Annals of Statistics, vol. 27 (1999)pp. 1119–40[PDF-preprint]
D.A. Freedman and P. Diaconis. “Iterated random functions.”SIAM Review, vol. 41 (1999) pp. 45–67[SIAM]
D.A. Freedman, S.P. Klein, M. Ostland, and M.R. Roberts.“Review of‘A Solution to the Ecological Inference Problem.’ ”Journal of the American Statistical Association, vol. 93 (1998)pp. 1518–22;[PDF-preprint]with discussion, vol. 94 (1999) pp. 352–57.[PDF-preprint]
P. Diaconis and D.A. Freedman. “Consistency of Bayes estimatesfor nonparametric regression: Normal theory.” Bernoulli Journal,vol. 4 (1998) pp. 411–44.
D.A. Freedman. “De Finetti’s theorem in continuous time.” InStatistics, Probability and Game Theory: Papers in Honor ofDavid Blackwell. Institute of Mathematical StatisticsMonograph 30 (1997) pp. 83–98. T.S. Ferguson, L.S. Shapley, and J.B.MacQueen, eds.
T.H. Lin, L.S. Gold, and D.A. Freedman. “Concordance betweenrats and mice in bioassays for carcinogenesis.” Journal ofRegulatory Toxicology and Pharmacology, vol. 23 (1996)pp. 225–32.[preprint-PDF]
P. Humphreys and D.A. Freedman. “The grand leap.” British Journalof the Philosophy of Science, vol. 47 (1996) pp.113–23.[Br J Phil Sci][JSTOR]
K.W. Wachter and D.A. Freedman.“Planning for the Census in the year 2000.”Evaluation Review,vol. 20 (1996) pp. 355–377[PDF-preprint]
D.A. Freedman. “Some issues in the foundation of statistics.”Foundations of Science, vol. 1 (1995) pp.19–83.Reprinted in Some Issues in the Foundation of Statistics,Kluwer, Dordrecht (1997). Bas C. van Fraassen, ed.[PDF-preprint]
National Institute of Justice/RAND
S.P. Klein, D.A. Freedman, and R. Bolus,“A statistical analysis of charging decisions in death-eligiblefederal cases: 1995–2000.”[PDF][NIJ website][RAND website]
Lecture Notes
First Lectures in Statistics 215[PDF]
What is a Random Variable?[PDF]
What is the Error Term in a Regression Equation?[PDF]
General Formulas for Bias and Variance in OLS[PDF]
Another Proof of the Gauss-Markov theorem[PDF]Yet Another Proof[PDF]
Notes on Regression Asymptotics[PDF]
If the AssumptionsBreak Down, OLS Can Be Biased and Nominal SEs Can Be Wrong[PDF]
Orthogonality Does Not Imply Asymptotic Normality[PDF]
Comments on Standardizing Path Diagrams: What Are the Parameters?[PDF]
Direct and Indirect Effects[PDF]
Replicating Gibson on McCarthy: Weighted Regression[PDF]
An Example to Illustrate the Asymptotics of IVLS[PDF]
Endogeneity Bias Is Contagious[PDF]
Can Exogeneity Be Determined from the Joint Distributionof Observables?[PDF]
An Example of Under-Identification[PDF]
The Neyman-Scott Paradox[PDF]
Notes on the MLE[PDF]
More on the MLE: The Likelihood Function Can Be Bimodal[PDF]
Statistics David Freedman Pdf Download Windows 7
Hierarchical Linear Regression[PDF]
The Odds Ratio[PDF]
Notes on Ratio Estimators and the Delta Method[PDF]
On Kangaroos and Cookies: Causal Models for Paired Designs[PDF]
Statistics David Freedman Pdf Download Torrent
Greenwood’s Formula[PDF]
How To Make Power Calculations[PDF]
The Census Trial of 1992
David Freedman Statistics
Transcript: zipped text files[zip]
Datasets
Statistics By David Freedman
Extracts from the Current Population Survey: ASCII files[ftp]