An efficient variant of the product and ratio estimators

Abstract  This article presents a variant of the usual ratio and product methods of estimation, with the intention 10 improve their efficiency. The first order large sample approximations to the bias and the mean square error of the proposed estimator are obtained and compared with those of the well-known methods (simple expansion, ratio, product, difference and linear regression methods). For a special case, the accuracy of the first order approximation (terms up to the order n-¹ ) is examined by including terms upto the order n^-2 . With suitable choice of a design parameter, the proposed estimator turns out to be superior to the three methods mentioned first. The relation to the other two methods is examined; if the design parameter can be chosen near to the optimal value, the proposed method is seen to be approximately as efficient as the linear regression estimator. Finally some extensions are indicated.     

Scales of measurements: An introduction and a selected bibliography

Stevens' (1946) distinctions among nominal, ordinal, interval and ratio measurements have had a marked influence on researchers of social, psychological and statistical sciences. This paper reviews those distinctions and includes some comments concerning the applicability of different statistical techniques to each scale. A detailed bibliography is also provided.On study leave from Department of Statistics, University of Karachi, Karachi-32, Pakistan.     

An efficient estimator using auxiliary information

The use of ratio and product methods of estimation using auxiliary information for estimating the mean of a finite population is well known.Srivastava [1967] andReddy [1973] proposed ratio-cum-product type estimators. This paper proposes a transformed estimator which is even more efficient than these estimators for a wide range of the value of the correlation coefficient between the main and auxiliary variables.     

Talking straight about competitive strategy

This paper argues that the offering—what customers choose to buy—should be the primary focus of competitive strategy. A classification of competitive strategies based solely on how an offering could be positioned is developed, and a new output‐ or buyer‐based vocabulary devised for it. It is argued that this language provides a surer way of becoming customer orientated than an input‐ or seller‐based language. This new perspective leads to a surprising and novel insight about the appropriate unit for framing competitive strategy. It also helps in providing a more realistic model of the dynamics of competitive strategy, and the role it plays in achieving profitability. Finally, the new language and concepts are put to the test; it is shown that they are of practical value, since they would help managers in better framing and answering some key competitive questions.     

Bounds for the variance of an inverse binomial estimator

Summary  B est [1] found the variance of the minimum variance unbiased estimator of the parameter p of the negative binomial distribution. M ikulski and Sm [2] gave an upper bound to it, easier to calculate than B est's expression and a good approximation for small values of p and large values of r (the number of successes). In this paper both lower bounds and closer upper bounds are derived.     

If You Have a Hammer,You Only Look for Nails: The Relationship Between the Einstellung Effect and Business Opportunity Identification

We use the theory of Einstellung to examine individual differences in business opportunity identification. Einstellung is the tendency to habitually rely upon existing cognitive frameworks even when they become redundant. This study examined the relationship between individuals' proclivity toward Einstellung and the number and innovativeness of business opportunities they identified. Our results suggest that the Einstellung effect was negatively related to the number as well as the innovativeness of opportunities people identified. These findings indicate that habitual reliance upon existing cognitive frameworks can sometimes have negative consequences for the critical entrepreneurial ability of opportunity identification.