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A number of problems are faced today by those who are attempting to carry instruction in business statistics beyond the stage of the elementary course in method. In the first place, students do not learn in the elementary course, to use in a practical way, their statistical technique. This observation applies equally to students who have had a thorough preparation in mathematics and to those who have not. Conversation with other teachers has led me to conclude that the failure of elementary instruction to enable the student to apply his knowledge is confined to no one course or institution. This is not intended to be critical of such courses. It is probably impossible in the brief time allotted to them to develop in the student both a knowledge of statistical technique and skill in the application of method to concrete problems. The emphasis in an elementary course must be on the nature and calculation of statistical constants, rather than on argument from those constants. The emphasis must be on statistical description rather than on statistical inference. But if the study of statistical method in training for business is to be justified, it must be carried to the point where the student will have more than a bowing acquaintance with method and will acquire facility in drawing conclusions from quantitative data. It is easy to suggest this and somewhat difficult to accomplish it. The faculty which needs to be developed in the student is something in the nature of a power to see analogies between a new situation and a previous experience. The mental processes involved are perhaps obscure, and no final answer possible as to the method by which this power can be developed. But certain conditions essential to its development can be pointed out. The most important of these conditions is that the original experience of the student must be vivid. Tell the student that when the relationship between associated variables is disturbed by many minor causes, a process of curve fitting or graduation is an appropriate method for describing the normal relationship between the variables. He would promptly forget it. Give him a concrete problem involving the same principle and he will remember provided that the illustration is a significant one. The original experience of the student will be vivid in proportion as it seems to the student to have a significant conclusion. At the present time, it is

I T IS my belief that the individual business units in every industry should be permitted to act together voluntarily, if they desire, for the purpose of organizing and controlling the industry of which they are a part, in general as follows: i. Individual concerns should be permitted to enter into voluntary associations, sales agencies, or other groupings in whatever manner they might elect, for the purpose of collecting and compiling current information pertinent to the industry or line of business in question; of conducting research and experimentation; of simplifying, improving, and standardizing products; of agreeing on prices, terms, and conditions of sales; of agreeing upon division of territory or markets; and for the purpose of acting in unison on all other matters of common interest and mutual benefit. 2. Voluntary agreements for the same purposes should be permitted among and between such groups and all such agreementsthose between groups, between the members of any group, or between the members of a group and the group-should be legally enforcible in the courts. 3. No compulsion should be exercised on any individual to force him to join any such organization. He should be entirely free to stay out or come in as his own interest would determine. 4. Whatever business statistics and information are collected and used by such groups should be published currently so that competitors and consumers would have the advantage equally of such information.

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A FULL explanation of the data and methods used A1 in constructing our index of general business conditions was given in THE REVIEW OF ECONOMIC STATISTICS for January and April i919. That explanation should be consulted by readers of the REViEw who want information concerning the technique or details of the construction of the index. The majority of readers, however, are interested in results rather than in methods or technical details. The explanation which follows is designed to meet the needs of such readers. Although the explanation is non-technical, it is nevertheless complete and it is intended to give a statement sufficient for an understanding of our index. Our index of general business conditions is derived from those series of monthly items of the industrial, commercial, and financial statistics which ordinarily serve as the basis for judgments concerning the fundamental speculative, business, and banking situation. Measurement of business activity, however, is relative to some standard. Isolated items of statistical series, upon which such a measure must be based, can have no significance by themselves. Only by a comparison of items over a period of time can we ascertain their meaning. Items pertaining to widely separated periods cannot, however, be used in their crude form. Each monthly item of bank clearings, pig-iron production, interest rates, and the like, is a composite, the make-up of which depends on the year and season. That is, various elements contribute to make bank clearings for January I920, for instance, the precise total reported. In general the actual items result from the combination of four elements -secular trend, seasonal variation, cyclical fluctuation, and a residual factor. The secular trend is the regular increase or decrease, according to some principle, over the whole period under consideration. For most series it is a growth element, dependent upon population and the development of industry. There is a normal change year after year in a developing or altering industrial society, just as there is a normal change in the physical or mental status of a growing child. As used in this exposition, the expression " secular trend " may refer either to a statistical series or to one item of a series. When it refers to a series it means the straight line fitted to the data; the measure of growth is given by the slope of the straight line. When the expression refers to one item of a series it designates the vertical distance (or its numerical value) from the curve or line of trend to the zero base line. The seasonal variation is the movement of the items within the year, which we attribute to the round of the seasons. There is a seasonal change in various lines of business activity just as there is a seasonal change in temperature or rainfall. Although an iron-clad system is not to be expected, the movement of the items, to be seasonal, must be systematic year after year. The seasonal variation of an item for any month is given by an index which expresses the " normal " for that month as a percentage of the monthly average for the year. The cycles are the undulating curves, or numerical values, secured by removing from the actual items the secular trend and the seasonal variation, and expressing the results in terms of comparable units.' The actual figures thus corrected and expressed measure the rhythmic movement of business, the ebb and flow corresponding to depression and prosperity. The residual element includes all sporadic developments which affect individual series, or widespread changes due to momentous occurrences, such as wars or national catastrophes, which affect a number of series simultaneously. Thus pig-iron production may take a sudden slump if a strike occurs, railway gross earnings may drop because of unusual storms or floods; trading on the stock exchange may be greatly affected by a court decision. The continuity of nearly all series was interrupted by the outbreak of the Great War. This irregular or residual element has not been eliminated by our process of correcting the data and hence it is present, along with the cyclical fluctuation, in the statistics used in constructing our Index. In order to adapt the statistical data to our purpose, the securing of a numerical measure of the ebb and flow of business, two problems were solved; first, we devised a method of correcting the statistics for secular trend and seasonal fluctuations; that is, we unraveled the tangle of elements which constitute the fluctuations in fundamental series. In other words, we devised a method whereby the actual series of business statistics now published in our trade and financial journals were set forth in such a form as to be significant and reliable indices of business conditions. Second, having corrected the various series of fundamental statistics for secular trend and seasonal variation, it was necessary to solve another statistical problem. The corrected series have wave movements; for all corrected series the elapsed periods from crest to crest or trough to trough are approximately the same. The times, however, at which crests or troughs of the waves of the several series occur are frequently not the same. Never

In this paper, the relationships between life insurance premium expenditures and various economic and demographic characteristics of households are examined in the context of cross-sectional data. Income, net worth holdings, and stage in the life cycle of the household and education and occupation of the household head are generally determined to be significantly related to premium expenditures. Estimates of the variable coefficients in a linear model and estimates of the income elasticity of premium expenditures are provided. Differences in life insurance premium expenditures among households are of interest because of their implications regarding the life insurance industry's effectiveness and potential. Yet, little is known about the factors which determine individual purchases of life insurance. While life insurance institutions publish data on various measures, mostly aggregate, of life insurance consumption, they provide little or no data which give meaningful insights into the determinants of individual expenditures on premiums. Few attempts have been made by researchers to analyze the relationships between variables associated with life inJ. D. Hammond, Ph.D., C.L.U., is Associate Professor of Business Administration in Pennsylvania State University. Formerly on the faculty of Ohio State University, Dr. Hammond is Assistant Editor of the Journal of Risk and Insurance. David B. Houston, Ph.D., is Associate Professor of Economics in the University of Pittsburgh. Dr. Houston was formerly on the faculty of the Graduate School of Business Administration of the University of California at Los Angeles and the Pennsylvania State University. E. R. Melander, Ph.D., is Assistant Professor of Business Statistics in Pennsylvania State University. Dr. Melander was formerly on the faculty of the University of Minnesota. This article was submitted in April, 1967. surance consumption and expenditures by families on premiums.' In general, the demand function for individual life insurance has not been studied and attempts to determine income and price elasticities of life insurance have not been undertaken.2 ' There are, however, at least three notable studies: Life Insurance Ownership Among American Families (Ann Arbor: Survey Research Center, Institute of Social Research, University of Michigan, 1957), a study by Kreinin, Lansing, and* Morgan, "Analysis of Life Insurance Premiums," Review of Economics and Statistics, February, 1957, and some analysis by Katona, Lininger, and Mueller in 1964 Survey of Consumer Finances (Ann Arbor: Survey Research Center, Institute of Social Research, University of Michigan, 1965). The first study, on pages 79-83, utilizes multiple regression analysis to study the effects of income, veteran status, urbanrural status, occupation, and life-cycle upon life insurance premium expenditures among households in the Survey Research Center Sample. These regression results are referred to later in our paper. The second study utilizes variance analysis in determining, among other things, that life cycle is important in determining the ratio of premiums to income and that city size also has a positive effect upon that ratio. The third study utilizes multivariate analysis of data collected for family units rather than for spending units as in earlier surveys conducted by the Survey Research Center. 2 A very recent study is an exception. Muzaffer

A mere statement of our problem-the content of a well-rounded curriculum in statistics-immediately raises a number of questions on which statisticians differ. For example, can the curricular question be considered in the abstract or is it necessary to use adjectives to makes the question read: a well-rounded curriculum in economic statistics, or a well-rounded curriculum in mathematical statistics or psychological, industrial, agricultural, or business statistics?