INTRODUCTION
Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistics is a highly interdisciplinary field; research in statistics finds applicability in virtually all scientific fields and research questions in the various scientific fields motivate the development of new statistical methods and theory. In developing methods and studying the theory that underlies the methods statisticians draw on a variety of mathematical and computational tools. Two fundamental ideas in the field of statistics are uncertainty and variation. There are many situations that we encounter in science (or more generally in life) in which the outcome is uncertain. In some cases the uncertainty is because the outcome in question is not determined yet (e.g., we may not know whether it will rain tomorrow) while in other cases the uncertainty is because although the outcome has been determined already we are not aware of it (e.g., we may not know whether we passed a particular exam).
Management (or managing) is the administration of an organization, whether it is a business, a not-for-profit organization, or government body. Management includes the activities of setting the strategy of an organization and coordinating the efforts of its employees (or of volunteers) to accomplish its objectives through the application of available resources, such as financial, natural, technological, and human resources. The term "management" may also refer to those people who manage an organization. Whether designing new products, streamlining a production process or evaluating current vs. prospective customers, today’s business managers face greater complexities than ever before. Running a shop on instinct no longer suffices. Statistics provide managers with more confidence in dealing with uncertainty in spite of the flood of available data, enabling managers to more quickly make smarter decisions and provide more stable leadership to staff relying on them. Whether designing new products, streamlining a production process or evaluating current vs. prospective customers, today’s business managers face greater complexities than ever before. Running a shop on instinct no longer suffices. Statistics provide managers with more confidence in dealing with uncertainty in spite of the flood of available data, enabling managers to more quickly make smarter decisions and provide more stable leadership to staff relying on them.
Focusing on Big Picture
Statistical analysis of a representative group of consumers can provide a reasonably accurate, cost-effective snapshot of the market with faster and cheaper statistics than attempting a census of very single customer a company may ever deal with. The statistics can also afford leadership an unbiased outlook of the market, to avoid building strategy on uncorroborated presuppositions.
Backing Judgments
Statistics back up assertions. Leaders can find themselves backed into a corner when persuading people to move in a direction or take a risk based on unsubstantiated opinions. Statistics can provide objective goals with stand-alone figures as well as hard evidence to substantiate positions or provide a level of certainty to directions to take the company.
Making Connections
Statistics can point out relationships. A careful review of data can reveal links between two variables, such as specific sales offers and changes in revenue or dissatisfied customers and products purchased. Delving into the data further can provide more specific theories about the connections to test, which can lead to more control over customer satisfaction, repeat purchases and subsequent sales volume.
Ensuring Quality
Anyone who has looked into continuous improvement or quality assurance programs, such as Six Sigma or Lean Manufacturing, understands the necessity for statistics. Statistics provide the means to measure and control production processes to minimize variations, which lead to error or waste, and ensure consistency throughout the process. This saves money by reducing the materials used to make or remake products, as well as materials lost to overage and scrap, plus the cost of honoring warranties due to shipping defective products.
Considerations
Know what to measure, and manage the numbers; don’t let the numbers do the managing for you, or of you. Before using statistics, know exactly what to ask of the data. Understand what each statistical tool can and can’t measure; use several tools that complement one another. For example, don’t rely exclusively on an "average," such as a mean rating. Customers using a five-point scale to rate satisfaction won’t give you a 3.84; that may indicate how the audience as a group clustered, but it’s also important to understand the width of the spread using standard deviation or which score was used by the greatest number of people, by noting the mode. Finally, double-check the statistics by perusing the data, particularly its source, to get a sense of why the audiences surveyed answered the way they did. Business owners face many situations with outcomes that seem unpredictable. For example, your main supplier of a key batch of parts could have a lower cost, but more uncertainty in delivery time. Data and statistics can be used to concretely define and measure this uncertainty and predict when the next shipment is coming. Managerial decision-making with this statistical insight can avoid steering production, costs and customer service into bad avenues.
Operational Value
Many businesses rely on their Information Technology (IT) systems to manage data, facilitate payments and run operations. Unforeseen bottlenecks can occur when IT runs a necessary system upgrade, if the implementation stalls and temporarily keeps your business from running smoothly. To combat this, some IT systems have statistical algorithms that find the likely cause for the blockage before your business hits a dead end. Other operational benefits of statistics are accurate demand forecasting and sufficient inventory planning.
Strategic Value
In steering the direction of your business, statistics can be used to guide long-term forecasts for strategic planning. Analytical methods like statistics support the understanding of the holistic impact that strategic initiatives can have on your business. For example, a statistical model can provide a baseline forecast of your revenues and expenses for years to come, which your team can adjust depending on new product introductions, new markets and competitor activities.
Research Value
Instead of repeatedly reacting to lost sales from insufficient inventory, you can use statistics to learn about your customer’s behavior like how they react to promotions and when and what they buy. These research studies allow businesses to be proactive through predicting customer behavior and creating better marketing plans. Moreover, statistics can be used in the development and pricing of new products via survey analysis and regression models.
Software
Data is abundant for many types of businesses. Whether you seek demographic, attitudinal, or psychographic data, statistics can be used to discover insights from mining the data. For example, the accessibility of statistical tools in low-cost spreadsheet software makes discovering important insights within reach for any business, no matter the size.
Statistics aiding in the focused strategy
The statistics have always been the right tool to incorporate the focused strategy for the management. For example, the management wants to enhance the brand image of a particular product. The brand image is dependent on various factors like perception, satisfaction of the product, service provided, style, association, etc. There may be a lot of factors to look at. Moreover the organization may have a huge set of samples from a survey. So the priority would be to reduce the no of factors and the huge samples to manageable ones which helps in optimization of cost as well. The factor analysis reduces the number of factors and the cluster analysis reduces the number of observations. Then by removing the insignificant variables and combining the variables having high multicollinearity, a regression could be carried out with the brand image being the dependent variable. Thus the management has i) the significant variables to look at and ii) The choice of the combination of various independent variables for the desired level of the dependent variable. Thus it will help the management to incorporate a combination from these choices that fulfills the management objective. This helps the management to establish a focused strategy with the help of effective statistical tool and technique.
Interpretation of the output of the analysis
The most important aspect of statistics is the interpretation of the statistical outputs and the results to the mode easily understood by the management executives. The resistance against using statistical tools has been the difficulty to understand the outputs easily. The challenge of an analyst or a management executive is to understand the interpretation and the associated assumption fully and make it easily understandable for other executives. For example the standard error of the regression of the brand image in the previous example is say 0.15. This doesn’t mean that for each observation or each sample it will produce an error of 0.15 only. This merely means that if all the samples are taken and regressed the average error of all those samples together is 0.15. Thus for a particular case the error can be more than 0.15. If this interpretation is not known it will overestimate or underestimate the risk.
Statistical Tools Used in the various management decisions
Logistic regression is used in the credit approval industry, admission in an academic institution or indecisions where there is a binary choice of yes or no i.e. 1 or 0 is required. The logistic regression issued to predict if a particular observation using the initial behavioral patterns would fall in the yes or no region in future.
Regression Analysis is used to predict some continuous variables based on the initial independent variables like the revenue, sales, brand image, budget cost, etc.
Cluster Analysis and factor analysis is used for segmentation objectives.
Discriminant analysis is used for zeroing on factors helping in discrimination of the various observations into various categories. It also predicts in which category a particular observation would fall. Various other statistical models like ANOVA, ANCOVA, Time Series models like ARIMA and other multivariate analysis are used for different management objectives and problems.
Indexes of Inflation and Living Costs
Analyses of budgetary trends from year to year for a given library are often thwarted by the eroding effects of inflation on the library's purchasing power. This is especially true where library materials are concerned. Book prices are inflated at a higher rate than general consumer prices, and periodical prices are inflated at an even higher rate than books. Library managers cannot afford to overlook these facts in making budget decisions. Comparisons of salaries from library to library are thwarted similarly because living costs can vary dramatically from place to place. So, library managers must also be prepared to adjust such figures for differences in living costs. The index used most commonly to assess inflation is the consumer price index (U.S. Bureau of Labor Statistics, 1992). But this index is inadequate for most library purposes because it underestimates sometimes grossly the impact of inflation on materials budgets. There are many different sources of average materials prices and price indexes. These include annual articles on books (Grannis, 1991b) and periodicals (Carpenter & Alexander, 1991), which provide the most current data, as well as reports of recent figures in the Bowker Annual (Bentley, 1991; Grannis, 1991a). Managers of different types of libraries also have their own indexes to draw upon. Research Associates of Washington publishes the Higher Education Price Index and the Elementary-Secondary School Price Index (Research Associates of Washington, 1991; also Halstead, 1991). The former contains a sub-index, the Library Price Index, which itself contains separate index scores for different portions of an academic library budget (e.g., staff, materials, equipment, and contracted services). The latter contains sub-indexes for librarians (i.e., library media specialists) and materials by level (i.e., elementary or secondary) and format (e.g., books, periodicals, cassettes). There is no comparable index of prices for public libraries. However, the Library Research Center at the University of Illinois, Urbana-Champaign, produces the Index of American Public Library Expenditures annually (Palmer, 1991). This index is not nearly so pure a measure of inflation as the others, but it does break out comparable index scores for staff, materials, and other expenses. Despite a common misconception, the consumer price index does not provide a basis for comparing living costs from place to place. For an index of cost of living, library managers must turn to the American Chamber of Commerce Researchers Association (American Chamber of Commerce Researchers Association [ACCRA], 1991), which publishes a quarterly cost-of-living index for all urban areas in the United States. (Sample uses of all of these indexes will be described later.) Notably, ACCRA'S monopoly in this area is about to be challenged by Research Associates of Washington (1992), which is publishing its own annual cost-of-living index report.
This paradigm believes that the only knowledge (knowledge) is valid is the science of knowledge (science), that is knowledge which originated and is based on experience (experience) are caught and processed through the senses by reason (reason). Therefore, in practice, research with this quantitative approach to give meaning through interpretation of statistical figures or not through language or culture.
Statistics in quantitative research approach is one of the main components in the research stages, ranging from the preparation of research, data collection techniques, data processing until the effort to make the decisions / conclusions scientifically. Thus the statistics in the study with quantitative approach has a fairly dominant role in expediting the achievement of research objectives.
With regard to the role of these statistics, then at least there are four roles in research, among others:
First, the role of statistics in the Determination of Sample Research. The purpose of sampling techniques is to produce a representative sample of the population and obtain an adequate sample size to do the research. In relation to this role, statistics provides techniques and specific formulas in order to obtain samples representatives and adequate sample size.
Second, the role of statistics in the development of data retrieval tool. Before a person uses a device maker the data, he must have the assurance that the device he uses it with quality. The quality of data collection tools can be viewed from the side of validity and reliability. Therefore each data collection tools need to be tested by validitas and reliability, and the best way to test validates and reliability of data collection tools is to use statistical methods.
Third, the role of statistics in a Presenting data. Data collected through specific data collection technique is still raw data, therefore, that data was more communicative it must be presented in such a way that data is easy to read or understood. In connection with efforts to display the data to be easily read and understood, then the statistics provide specific techniques in processing data and presenting data, with descriptive statistical methods.
Fourth, the role of Statistics in Data Analysis or Testing Hypotheses. The ultimate goal in research is the conclusion as an ingredient to make a decision. In order to obtain results valid and reliable research, statistics have also developed a specific calculation technique and develop various methods to test hypotheses that could help researchers. Statistical analysis of data that discuss or test this hypothesis is inferential statistical methods.
Growth of the mass production industries has posed new and complex problems in industrial management. Scientific solution of these problems necessitates statistical analysis of the vast quantities of data generated in these industries as a by-product. To date, selected but limited headway has been made in using these data to solve the problems of industrial management.
Improvements bordering on the spectacular have been achieved in selected instances of industrial applications of statistical analysis. Quality control and market research afford two such instances.
Conclusion
These improvements have been mainly the result of commencing to use facts at all (to replace hunch) rather than the result of superior analysis of data long utilized. In consequence, these pioneering adventures in industrial applications of statistics have been made largely without the aid of statisticians.
However, the great need for science in management, and the presence of vast quantities of data on which to base a science, suggest that industry will use trained statisticians in increasing numbers, to the extent that the professional statistical societies can do much to aid the industry may become a principal employer of statisticians greater utilization of statistics in industry by:
(a). organizing in each society major division to deal with the problems of statistics in industry.
(b). sponsoring joint meetings with societies of managers, industrial engineers, and others interested in industrial statistics.
(c). injecting into the current literature and into the statistical text-books, cases and problems drawn from industry.
(d). research to develop new statistical tools, and to simplify existing tools for use by industrial personnel.
(e). recognizing useful work in industrial statistics through honors and awards