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The article presents a response to the paper "Observations on Jensen's Experimental Design for Study of Effects of Accounting Variations in Decision Making" by Thomas R. Dyckman and discusses the scale of observation in an analysis of variance (ANOV) model. Dyckman questions Jensen's research in proposing violations in the underlying assumptions of the ANOV model and whether or not these violations would appreciably affect the conclusions of his experiment. An interval scale was used during some of Jensen's data selection for discussion which Dyckman feels is incorrect in its additivity assumption.

This article analyzes the second-order interaction in multidimensional contingency tables in marketing research. THe author examines the introduction of chi-square analysis in marketing research as well as the use of chi-square to study three-dimensional contingency tables. The author also examines the association of the three attributes of multidimensional contingency tables. The author suggests that these complex techniques are needed to examine the more complicated relationships that exist between marketing and consumers.

In this article we propose two minimum chi-square estimators for a bivariate probit model. We call one estimator the Full Information and the other Limited Information Minimum Chi-Square because the first takes account of all the a priori information while the second does not. Both estimators are shown to be consistent. Moreover, the first is shown to be asymptotically as efficient as the maximum likelihood estimator and yet is computationally much simpler. For illustration, estimates are computed for the data used by Ashford and Sowden [1970.] [ABSTRACT FROM AUTHOR]

Necessary and sufficient conditions for admissibility are given for test procedures based on a preliminary test of significance. Three types of problems are studied-testing the normal mean, fixed effects models of the analysis of variance and random effects models. Admissibility in this instance is equivalent to the intuitive and practical condition that acceptance regions of the procedures have convex sections in certain variables, while other variables are fixed, It is easy to check when the conditions hold. A discussion of optimality properties of these and other types of pooling procedures is given. [ABSTRACT FROM AUTHOR]

Exact small sample behavior in two-way contingency tables is investigated for Pearson's chi-square statistic (X²), Light and Margolin's C statistic and its related R² measure of association, Kuliback's minimum discrimination information statistic (2&lcirc;), and Goodman and Kruskal's Lambda. R² is shown to be identical to Goodman and Kruskal's tb, leading to a test for independence based on 4. In small samples from a product of multinomials model, the null distribution of C is better approximated by a x² distribution than is the null distribution of X²; both are considerably better approximated by a x² distribution than is the null distribution of 2&lcirc;. It is proved for tables with two columns and any number of rows that if the column totals are equal, then X² ≤ 2&lcirc;; thus, X² is more conservative than 2&lcirc;. Hence, use of 2&lcirc; should be avoided in testing independence in tables with small samples. [ABSTRACT FROM AUTHOR]

This article presents a data analysis approach consisting of a synthesis of experimental design methods and spectral methods of time series analysis which is appropriate in exploratory situations where the re- cording process generates a long sequence of correlated observations. [ABSTRACT FROM AUTHOR]

The use of Patnaik's [28] chi-square approximation to the noncentral chi-square distribution and the Wilson-Hilferty [321 transformation of chi-square to approximate normality are explored as a simple, efficient means of finding k-order-statistic confidence bounds on parameters and reliability of the one- and two-parameter negative exponential distributions. An important implication of the results leads to obtaining simple closed-form approximations to percentiles of the beta distribution either with integer or noninteger parameters. The general methodology is also used to approximate confidence or prediction intervals for the time of the kth exponential failure based on r

Three statistics for testing additivity and making detailed inferences on interactions in a two-way setup are considered. The first method uses the well-known sum of squares for testing and yields Scheffe bounds for all contrasts; the second introduces a maximum characteristic root statistic and Roy bounds, but is restricted to product-type contrasts; the last is limited to tetrad differences and its statistic is the maximum of all such differences. Bounds on percentage points for this maximum are provided in the appendix. Uses of the three methods in inferring various types of interaction contrasts are discussed and illustrated. [ABSTRACT FROM AUTHOR]

This article presents an analysis of variance techniques for unbalanced data. Some of the more important issues encountered in the analysis of unbalanced data is examined here within a single framework. These issues are broadly defined as solutions to the normal equations for linear models where the incidence or design matrix is not of full column rank, estimable functions of the parameters and tests of hypotheses, and reductions in sums of squares and interpretation of results.

If the research goal in applying classical discriminant analysis techniques is to test for differences in the general characteristics of groups, then standard tests from multivariate analysis of variance are appropriate. Statistically significant differences among group means and dispersions indicate that the groups arose from separate populations. If, however, the goal is to explore group structure in terms of group overlap and the locations of individual observations relative to each other and to the group means, then it can be shown that the significance tests provide incomplete and often misleading representations of the data. Similarly, in problems involving just more that just two groups, the null hypothesis can be rejected, when in fact only one of the several groups arose from a different population.

This article analyzes the relationship between stochastic dominance (SD) and mean-semivariance (ES) criteria. This paper contains two main results. First, there are good conceptual bases for anticipating greater consistency between ES and second SD (SSD) rules than between mean-variance (EV) and SSD rules. In particular, with one possible and relatively minor exception (identical mean and semivariance, the efficient set of portfolios derived from the ES rule with semivariance calculated around a fixed point is a subset of the SSD efficient set.

This paper presents a follow-up records' study of 773 former marriage counseling clients of 21 marriage counselors. The clients' primary treatment form was compared with six categories of counseling outcome. A chi-square statistical treatment demonstrated that the six forms of counseling resulted in different outcomes significant beyond the .001 level. A rank ordering of the six forms for effectiveness suggested conjoint interviews and conjoint groups to be the most effective forms. Different forms may be helpful to those who divorce as opposed to those remaining married, and some findings are quite different from current supposition. [ABSTRACT FROM AUTHOR]

This study employed several statistical techniques in testing the hypothesis that classification according to (1) growth, (2) stable, and (3) cyclical characteristics represents a factor for grouping stocks. These techniques showed that the residuals obtained by removal of general market effects from a sample of 100 stocks displayed cross-sectional dependence conforming to four distinct stock categories, including an oil group as well as the three hypothesized groups. In addition, regression analysis results indicated that these stock groupings accounted for an avenge of 14 percent of the variance in rate of return of stocks in the sample in comparison to 31 percent represented by general market effects. It was thus considered appropriate to assign a factor to the explanation of the variance of returns of a common stock additional to market, industry, and company, and based upon a system of classification corresponding to (1) growth, (2) stable, (3) cyclical, and (4) oil stocks. Correspondingly, the presence of stock groupings among the stock return residuals implied a violation of the specification of cross-sectional independence of the residuals for the single-index model. An examination of the residual correlation matrices for the single- and four-index models confirmed the existence of a significant degree of dependence among the residuals from the single-index model, whereas the four-index model showed little indication of a violation of this specification. As a result of the superiority in accounting for systematic effects among securities, the four-index model provided a closer approximation to the true correlation matrix than was provided by the single-index model. Since these analyses indicated significant violation of the specification of cross-sectional independence of residuals, the form of the single-index model was examined for additional departures from model specifications. A test of the intercepts from regressions of the four stock groupings on the S & P market index showed that these parameters were significantly different from zero, thereby indicating that the returns of the indexes were not consistently proportional to their risk as measured by the slope coefficient from the regression equation. Risk and return measures developed in conjunction with the test of the form of the regression equation and a statistical test of these measures indicated a consistency as well as an independence of risk-return relationships among the four groups of stocks. [ABSTRACT FROM AUTHOR]

Examines some statistical measures in marketing research design and hypothesis testing. Regression analysis; Simple correlation; Analysis of variance; Strategy implications.

The asymptotic non-null distribution is obtained for the modified form of the Pearson chi-square statistic studied by Dahiya and Gurland [3]. By utilizing this result the power is obtained for specific alternative distributions in testing for normality, This enables recommendations to be made as to the number of class intervals to be employed in applying the aforementioned modification of the Pearson chi-square test of normality. [ABSTRACT FROM AUTHOR]

Sequential search procedures are described for determining an optimal dosage in the following biomedical problem. People are subjected to a certain chemotherapeutic treatment. The optimal dosage is the maximal dose for which the proportion of patients whose toxicity level does not cross the allowable limit is gamma. We discuss Bayesian and non-Bayesian sequential procedures for searching the optimal dosage, assuming a linear regression between toxicity and dosage, and normal conditional distribution of the toxicity level at each dose, with a known variance. [ABSTRACT FROM AUTHOR]

In experimental designs with randomized blocks, estimates of the inter-block variance often have few degrees of freedom since they depend only on the block averages (the inter-block information). These degrees of freedom are further reduced by the number of parameters appearing in the inter-block information. However, some parameters also appear in the intra-block information, and thus allow two independent estimators. The difference of these two estimators provides additional information about the inter-block variance. This paper shows how this extra information may be recovered to improve tests of hypotheses concerning inter-block parameters. [ABSTRACT FROM AUTHOR]

Use of the Bonferroni inequality for controlling family Type I error rate is discussed and new tables of t and chi square are provided. The tabled percentage points provide the capability for unequal Type I error allocation over a family of statistical tests. Use of these tables is illustrated. [ABSTRACT FROM AUTHOR]

Two methods, one based upon grouping, are proposed for fitting o regression line to bivariate data when the means of the observations have known order along the line. The two procedures are compared with respect to their limiting distributions and asymptotic efficiency. Criteria are given for choosing one of these procedures to fit the line. [ABSTRACT FROM AUTHOR]

This article presents a procedure for estimating the true error variance sigma[sup 2, sub 3] after two preliminary tests of significance in ANOVA Model II and considers its comparison as regards to bias and mean square error with that of an unbiased estimator of sigma[sup 2, sub 3]. [ABSTRACT FROM AUTHOR]

This study concerns the relative efficiency of the least-squares estimator of the mean of a covariance stationary sequence that has a constant mean and an autoregressive representation of order p. Our results augment those in [2]. The results in Section 1 include for any positive integer p the variance of the LSE, the BLUE in terms of autoregressive coefficients, the variance of the BLUE and the range of applications of Ylvisaker's theorem [7] regarding a lower bound on relative efficiency. For second-order schemes it also includes bounds on parameter values for relative efficiency is greater than or equal to 0.80. Graphical results for the first-order scheme in Section 2 add to the detailed study made in [2] and imply that the relative desirability of the LSE should be based on variance as well as relative efficiency considerations. [ABSTRACT FROM AUTHOR]

Consider sampling in two stages for testing a simple hypothesis about a population whose elements may be classified into a finite number of classes. The joint asymptotic distribution of the two Pearson chi[sup 2] statistics based on the first and combined samples is derived and used to construct a two-stage test. The advantage of this test relative to the conventional one-stage test is discussed in terms of its asymptotic power. Tables of critical values for 1 percent and 5 percent level tests for 1-10 degrees of freedom ore given as well as modifications for cases involving unknown parameters. [ABSTRACT FROM AUTHOR]

Investigates the power function of the F-test in analysis of variance under non-normal situations. Background on attempts to investigate the power function of F-tests; Numerical values of the power; Effect of non-normality on the power of the F-test. [ABSTRACT FROM AUTHOR]

Testing the hypothesis of equality of means, as in a one-way analysis of variance against the hypothesis of a particular ordering of the means can be done in two ways. One approach uses the maximum likelihood philosophy while the other uses a single degree of freedom contrast with coefficients chosen to be best in some sense. These two methods are applied to some data from a small scale social survey so as to illustrate their calculations and conclusions. Although the single degree of freedom approach may be accused of arbitrariness in scores, still it is relatively more convenient computationally and permits somewhat more comprehensive conclusions. [ABSTRACT FROM AUTHOR]

A measure of variation for categorical data is discussed. We develop an analysis of variance for a one-way table, where the response variable is categorical. The data can be viewed alternatively as falling in a two-dimensional contingency table with one margin fixed. Components of variation are derived, and their properties are investigated under a common multinomial model. Using these components~ we propose a measure of the variation in the response variable explained by the grouping variable. A test statistic is constructed on the basis of these properties, and its asymptotic behavior under the null hypothesis of independence is studied. Empirical sampling results confirming the asymptotic behavior and investigating power are included. [ABSTRACT FROM AUTHOR]

A step-by-step method is presented herein far partitioning a certain kind of hypothesis H about the m-way contingency table into (a) a series of hypotheses about marginal tables formed from the m-way table by ignoring one or more of table's m dimensions; and (b) a hypothesis about independence, conditional independence, or conditional equiprobability in the m-way table. This step-by-step method facilitates both the testing of H and the calculation of F, the estimated expected frequencies in the m-way table under FI. The method introduced herein for calculating F is easier to apply than the usual iterative-scaling method in many cases. [ABSTRACT FROM AUTHOR]

A test is scale and location invariant when the decision to accept or reject is not affected by multiplying each data point by the same positive constant or by adding the same constant to each data point. Two well-known statistics are shown to be test statistics of certain most powerful scale and location invariant tests: the ratio of range to standard deviation for normality versus uniformity, and the ratio of minimum deviation to standard deviation for normality versus exponentiality. The most powerful scale and origin invariant test of uniformity versus exponentiality is also considered. [ABSTRACT FROM AUTHOR]

Suppose that independent samples are available from two normal populations with means mu[sub 1] and mu[sub 2] and known variances. It is desired to test H: mu[sub 1] = 0. A possible preliminary test procedure in this case consists of first testing mu[sub 1] = mu[sub 2] and then testing H using either the sample mean from the first population or the pooled sample mean depending on the outcome of the test regarding equality of mu[sub 1] and mu[sub 2]. This procedure is shown to be biased. A power comparison of this procedure with the generalized likelihood ratio test of H indicates that the latter test is more powerful in a fairly extensive parametric region. It is noted that biasedness is frequently encountered in preliminary test procedures. In light of the example, it is recommended that power comparisons be made between such procedures and any available unbiased tests to ascertain whether extensive power loss regions exist or not. [ABSTRACT FROM AUTHOR]

A one-sample nonparametric test is proposed which sequentially examines the values of the Wilcoxon signed-rank statistic. The test rejects the null hypothesis of symmetry about zero if for any n between 1 and N the Wilcoxon signed-rank statistic for X[sub 1],..., X[sub n] differs from its null mean by |z|[sup alpha] N standard deviations; otherwise the null hypothesis is accepted. The critical constant |z|[sup alpha] N is determined by Monte Carlo sampling. [ABSTRACT FROM AUTHOR]

The powers of several one-sided Kolmogorov tests for a shift in the location of a normal distribution with known variance are computed and compared. An accurate approximation to the power of the ordinary Kolmogorov test for this problem is also given. [ABSTRACT FROM AUTHOR]

This article proposes a refinement of the usual approximation due to Satterthwaite for making a significance test when the test statistic involves a single mean square in the numerator and a linear combination of mean squares in the denominator, that linear combination involving known positive constants. The test procedure is outlined in detail, and its derivation given in a separate section. The development of the technique essentially involves determining, by successive approximations, a function which serves as the test criterion. This function is given through terms in the cubes of the reciprocals of the degrees of freedom involved in the problem, thereby giving a deviation from the advertised significance level which is smaller (in magnitude) than the deviation provided by the Satterthwaite procedure. An expression approximating the power of the test is also developed. [ABSTRACT FROM AUTHOR]

Natural test statistics for the hypothesis that an equation is overidentified have been developed by Anderson and Rubin and by Basmann. If the disturbances are jointly normal, serially uncorrelated, and small, both the above overidentification test statistics have the Fisher variance-ratio distribution asymptotically as the variance of the error terms gets small. This gives an analytic explanation of Monte Carlo results of Basmann. The results given apply to linear models in which predetermined variables are exogenous. [ABSTRACT FROM AUTHOR]

The article presents information on a nomogram for the "Student"-fisher t test. A nomogram is given for estimating the probability (P) for a given value of the "Student"-Fisher t test. W.S. Gosset, an employee of the Guiness brewing company in Dublin, published papers in 1908 in which he correctly solved three problems: the probable error of a mean, the distribution of the mean divided by its estimated standard deviation and the distribution of the estimated correlation coefficient between independent variates. Later "Student" and economist R.A. Fisher calculated tables of the relevant t distribution and Fisher gives a table of t and probabilities, corresponding to various degrees of freedom. Fisher and F. Yates, scholar provide in addition a column for P. It seemed that presentation of the P, degrees of freedom, t relationship in the form of a nomogram would be advantageous. It makes possible a fairly exact estimate of probabilities less than 0.0001 and makes it possible to get an estimate of P for any value of t from 1 to 65, instead merely of selected values.

Given a sample of n vector observations from a multivariate normal population, Anderson and Roy-Gnanadesikan have given for the variances a set of confidence intervals which are approximate in that a lower bound only is known for the joint confidence coefficient. In the present study, exact procedures are developed in terms of multivariate Chi-square distributions, and more general approximate procedures are given via Bonferroni's inequality. A numerical investigation suggests that the Bonferroni lower bound is fairly sharp for a variety of parameter values, and it always is superior to the Roy-Gnanadesikan procedure in the bivariate case examined. A lower bound in terms of independent statistics further is examined far a special class of one-sided intervals. [ABSTRACT FROM AUTHOR]

Results are given of an empirical sampling study of the sensitivities of nine statistical procedures for evaluating the normality of a complete sample. The nine statistics are W (Shapiro and Wilk, 1965), square root of b[sub I] (standard third moment), b[sub 2] (standard fourth moment), KS (Kolmogorov-Smirnov), CM (Cramer-Ven Mises), WCM (weighted CM), D (modified KS), CS (chi-squared) and u (Studentized range). Forty-five alternative distributions in twelve families and five sample sizes were studied. Results are included on the comparison of the statistical procedures in relation to groupings of the alternative distributions, on means and variances of the statistics under the various alternatives, on dependence of sensitivities on sample size, on approach to normality as measured by the W statistic within some classes of distribution, and on the effect of misspecification of parameters on the performance of the simple hypothesis test statistics. The general findings include: (I) The W statistic provides a generally superior omnibus measure of non-normality; (ii) the distance tests (KS, CM, WCM, D) are typically very insensitive; (iii) the u statistic is excellent against symmetric, especially short-tailed, distributions but has virtually no sensitivity to asymmetry; (iv) a combination of both square root of b[sub 1] and b[sub 2] usually provides a sensitive judgment but even their combined performance is usually dominated by W; (v) with sensitive procedures, good indication of extreme non-normality (e.g., the exponential distribution) can be... [ABSTRACT FROM AUTHOR]

The article discusses the analysis of cross-classified data. There are two different kinds of contingency tables, that is, truncated contingency tables in which the entries in some of the cells of the table are omitted from the analysis and the more usual contingency tables in which none of the cells of the table are omitted. In the usual non-truncated contingency table, one need to calculate statistician Karl Pearson's chi-square statistic for testing the null hypothesis of independence in the table, using the appropriate number of degrees of freedom. The concept of "quasi-independence" is useful in analyzing tables for which some of the cells are truncated and also in analyzing the more usual kinds of cross-classification tables since it leads to methods that focus attention in turn on various subsets of the entire table, making possible a more detailed analysis of the association between the row and column classifications in the table. For the non-truncated cross-classification table, if the rows and columns are not independent, it will often be of interest to estimate what are the kinds of interaction or association present in the table and what are their magnitudes.

Several writers have studied the problem of obtaining confidence bands for a straight line regression under the requirement that the bands be at a specified confidence level, (1 - α) say, for values of the independent variable restricted to a pre-specified closed interval. The bands studied by other writers have been either bands of equal width throughout the interval or trapezoidal bands. This paper studies confidence bands of the classical hyperbolic type under the restriction noted above. Relevant distribution results differ according to whether the pre-specified interval on the independent variable is symmetric or asymmetric about the mean of the independent variable values in the experiment. In the former case the problem under study, is shown to be equivalent to a problem studied by Halperin et al (JASA, Sept. 1967) for which distribution theory and tables are already available. The symmetrical case is generalized to obtain confidence bands in multiple linear regression at level (1 - α) for an ellipsoidal region on the independent variables centered at the point of means of the independent variable values used in the experiment. For straight line regression some numerical comparisons are made with bands of the other types mentioned above. These comparisons suggest that the bands proposed in this paper are uniformly superior to bands of equal width in the sense of having smaller average width; the proposed bands appear to be superior to trapezoidal bands in the same sense for intervals of practical interest, i.e. within or reasonably close to the range on the independent variables used in the experiment. [ABSTRACT FROM AUTHOR]

Tests are developed concerning the mean of the three-parameter Weibull distribution with the variance known and with the variance unknown, and concerning the variance with the mean unknown. The shape parameter is considered known. These tests are compared with tests developed by Hatter and Dubey based on the usual statistics for the normal case. A two-sample test for difference in means and for difference in reliability is also discussed. For the most part the tests are simple yet provide improvements over existing methods. [ABSTRACT FROM AUTHOR]

Experimental design considerations have been virtually ignored by economists who have conducted computer simulation experiments with models of economic systems. The objective of this paper is to spell out in detail the relationship between existing experimental design techniques and techniques of data analysis and the design of simulation experiments with economic systems. We begin by defining the problem of experimental design as applied to computer simulation experiments. With the aid of an example model, we explore several techniques of data analysis and a number of specific experimental design problems. Although this paper is oriented towards the design of computer simulation experiments in economics, the techniques which are discussed are of a general nature and should be applicable to the design of simulation experiments in other disciplines. [ABSTRACT FROM AUTHOR]

The article presents a table of the values of the power of the F-test corresponding to the degrees of freedom. The use of F-test while using a classical analysis of variance model, rejects the null hypothesis at the significance level. The values of &b.beta;, the Type II error, are tabulated to three decimal places. In this article the values of &b.beta; are tabulated to four decimal places and to obtain the values of &b.beta; for odd values, a linear interpolation may be used which generally gives three decimal place accuracy. The computation was terminated when the values of &b.beta; no longer changed in the sixth decimal figure. These values agree with the values of the power-function of the non-central chi-square distribution.

The standard tables used for the Kolmogorov-Smirnov test are valid when testing whether a set of observations are from a completely specified continuous distribution. If one or more parameters must be estimated from the sample then the tables are no longer valid. A table is given in this note for use with the Kolmogorov-Smirnov statistic for testing whether a set of observations is from a normal population when the mean and variance are not specified but must be estimated from the sample. The table is obtained from a Monte Carlo calculation. A brief Monte Carlo investigation is made of the power of the test. [ABSTRACT FROM AUTHOR]

The critical regions for the uniformly most powerful unbiased tests of ... and ..., where σ² is the variance of a normal distribution with unknown mean, can be expressed in terms of equations which require numerical methods for solution. Graphs are included from which these critical regions may be read for significance levels α = .05, .10. The graphs may also be used to obtain critical regions for the corresponding hypotheses concerning the parameters of two other distributions. Calculation of power is discussed. [ABSTRACT FROM AUTHOR]

This paper compares the Pearson Chi-Square and Kolmogorov goodness-of-fit tests with respect to validity under the following conditions: (1) the N independent observations are tabulated and arranged into k mutually exclusive groups that are equally probable under the hypothesis to be tested; and (2) both N and k are "small"; i.e., not greater than 50. A random sampling experiment was performed, and the results show that in general for the conditions considered, the Pearson test is more valid than the Kolmogorov test. [ABSTRACT FROM AUTHOR]

After a brief motivating discussion of approaches to significance tests in Section 1, the concept of expected significance level (ESL) is defined and its basic properties are presented in Section 2. Section 3 discusses the relationship between ESL and the familiar criterion of power. While a complete description of power for each size alpha is more informative than ESL, it is argued that ESL is often a reasonable compromise between high ideals and practicability. Section 4 gives two estimation methods for Monte Carlo simulation of ESL and shows how to estimate variances and covariances for the second of these methods. The first method leads to the familiar Wilcoxon two-sample statistic which is clearly a sample analogue of ESL. The second and more apposite method leads to a modification of the Wilcoxon statistic. [ABSTRACT FROM AUTHOR]

In this article, four ratio estimators designated as simple, Quenouille's, Beale's and modified ratio estimators are compared with respect to bias, efficiency, approach to normality and computational convenience. They are shown to be asymptotically minimum variance bound estimators. Some additional ratio estimators are discussed briefly and compared with these. Quenouille's, Beale's, and modified ratio estimators are found to be more attractive than the alternatives compared. [ABSTRACT FROM AUTHOR]

Confidence intervals for the population mean of a normal distribution can be determined from the distribution of the variate H = Square root of n(x - mu)/d, analogue to Student's t-distribution but based on the mean absolute deviation d instead of the standard deviation. The H-distribution is derived: the frequency function is symmetric about zero, with central ordinate (1/pi) Square root of (1-n[sup -1]); asymptotically it is normal, N(0, Square root of (pi/2)). An approximate formula for the calculation of the percent values is developed and numerical factors tabulated by which the mean absolute deviation of a normal sample of size n is to be multiplied in order to obtain 95 percent and 50 percent confidence limits of the mean, for n = 2(1)15(5)30, 40, 60,120. It is shown that the increase in length against confidence intervals from the standard deviation and Student's t is negligible. The usefulness of confidence intervals from the mean absolute deviation for estimating the precision of measurements in Physics is discussed. [ABSTRACT FROM AUTHOR]

Explores the use of statistical tests for decision-making in regional geographical construction in the United States. Chi square; Variance analysis; Relationship between regional constructs and the areal dispersion of selected traits within regional frames.

The article presents information on a possibility theorem on dynamic organizational research. This discussion of the problems of rigorous research in the dynamic organizational context is specifically methodological, addressing itself to the demonstration of possibility by indirect proofs. For illustrative purposes, the authors concern themselves with organization seeking to effect resolution of social problems. Although research has more frequently addressed itself to these particular organizations in the guise of action research, it is not to be assumed that this restricts the general applicability of the findings. While experimental designs are frequently used in research within an organization, for example, in industrial research applications, designs are rarely applied to the study of an organization and its management. Scholar James L. Price has pointed out that rigorous research designs are virtually unknown in the study of organizations. Scholar Stanely E. Seashore, in his discussion of problems and prospects of the utilization of rigorous designs, points out that the five or ten attempts to use designs reported in the literature are primitive, pioneering ventures. It is not difficult to explain why the organizational researcher has tended to case studies and anecdotal evidence. The universe of organizations is ill defined; the number of units accessible to an investigator is small.

Methodological presentation of the panel, drawing attention to the interest of this method for thorough analysis of change, its nature and its elements. Short history of its application to electoral decisions insisting on the value of certain results and the interest of the perspectives thus opened. Lastly, the A. endeavors to show that the particularities of the French political system do not rule out the use of the panel and that its application could lead to a realistic study of the French citizen, and the process of influence to which he is subjected. [ABSTRACT FROM AUTHOR]

This article presents information regarding parole prediction variables and the time factor in violations by burglars. Parole prediction studies have shown that it is possible to determine, before an inmate is released from prison under parole supervision, what are his relative chances for making a successful extramural adjustment. The original data were set up in 23 contingency tables with the number of months before violation as the independent variable in each case and one of the 23 parole prediction factors as the dependent variable. The Chi-square test was undertaken to test the probability that differences between the actual and the theoretical frequencies would occur by chance. In these instances where the probability that the differences were due to chance was very small the coefficient of mean square contingency was determined in order to measure the amount of the relationship that exists between the variables. Whether the relationship is a positive or a negative, one was determined by inspection of the data and the signs of the differences.