By comparing the Information for an individual stock with information about the stock market averages, a financial analyst can begin to draw a conclusion as to whether an individual stock is over- or underpriced. For example, data suppliers and Information purchase point-of-sale scanner data from grocery stores, process the data, and then well statistical summaries of the data to manufacturers. Manufacturers spend hundreds of thousands of dollars per product category to obtain scanner statistics and the promotional activity statistics to gain a better understanding of the relationship between promotional activities and sales.
A variety of statistical quality control charts are used to monitor the output of a production process. In particular, a v-bar chart can be used to monitor the average output. Suppose, for example, that a machine fills containers with 12 ounces of a soft drink. Periodically, a production worker selects a sample of containers and computes the average number of ounces in 2. This average, or the value, is plotted on a v-bar chart.
A plotted value above the chart's upper control limit indicates overfilling, and a plotted value below the chart's lower control limit indicates under filling. The process is termed ""in control"" and allowed to continue as long as the plotted v-bar values fall between the chart's upper and lower control limits. Properly interpreted, a v-bar chart can help determine when adjustments are accessary to correct a production process. They use a variety of statistical information in making such forecasts.
For example, in forecasting inflation rates that economist's use statistical information on such indicators as the Producer Price Index, the unemployment rate, and manufacturing capacity utilization.
These statistical indicators often entered into computerized forecasting models that predict inflation rates. There are lots of methods in statistics. Different methods are used in different research works.
This assignment denotes some methods which plays different roles in different business researches. Mean: It is called as average value. This average value is used to identify the nature of data and to estimate the value of data.
Ex :Performance of employees in a year of a company. Mode: The observation which occurs maximum number of times is called the mode of the given data. Example: Daily wages of 40 workers in a cement factory are given below. Find the mode of the data. Daily wages in rupees. Sol: Here the maximum frequency is 14 then the daily wage is These belongs to the most common useful statistical tools to compare effects and performances of variables. Correlation analysis is.
To discover whether there is a relationship between variables. To find out the direction of the relationship-whether it is positive negative or zero. This measure varies from 0 to 1 and 0 to1. The test statistic correlation coefficient measures the strength of the relationship between the two variables. Seasonal and cyclical movements in the phenomenon for determining the business cycle.
Population means whole data we have and Sample means some small part in that whole data. There are two types of sampling techniques such as large sample test and small sample test. Large Sample Test:- The sample size is denoted by n.
We checking the performance of employees. Ex: A small scale industry with employees 24 in that we check performance of employees. Because the formulas in these techniques depends by mean, variance, standard deviation for testing the problems. Non parametric tests: Chi square test is one of the simplest and most widely used nonparametric tests A non-parametric test makes no assumption about the parameters of the population from which the sample is drawn.
A non-parametric test makes NO assumption about the parameters of the population from which the sample is drawn. Chi-square describes the magnitude of the discrepancy between theory and observation. It is very popularly known as test of goodness of fit for the reason that it enables us to ascertain how appropriately the theoretical distributions such as Binomial, Poisson, and Normal fit empirical distributions.
It is also considered as a test of Homogeneity. Such a test is designed to determine whether two or more independent random samples are drawn from the same population or not. The following conditions must be fulfilled before applying the chi-square test. Sample size must be large preferably more than 50 items. Statistical tool plays a role play an important role business research.
Research design: Qualitative, quantitative and mixed methods approach. Kothari, C. The knowledge of data collection would assist business managers both in internal and external areas. The source of internal data of an enterprise arises from its daily operation and these facilitate their decisions making process.
These data are gotten based on different areas of interest like production unit, selling and financial activities fall under secondary internal data. External data refer to the outside environment of the business such as market, government, competitors, suppliers, the economy as a whole etc.
External primary data could be in form of surveys, questionnaires, opinion polls etc. Statistical Knowledge in business, help business managers make use of statistical decision analysis in order to promote their business. In the face of bleak uncertainties, managers are faced with an important point in business to make decision.
The decision maker would sit down, close eyes and think about the problem on a basis of combination of experience, hunch and judgement.
Alternatively, the decision-maker can approach the problem rather differently and systematically sort out various elements contained in the decision and the use certain decision rules to help him decide on what to do. How do we design a sample? Or how is the plan made to identify the mind of people, the type of retail shops, or the schools we wish to research or investigate?
Most often our emphasis mostly lies on how statistical procedures are employed to solve problem of our interest. Managers in business organisation employ statistical methods to find information which helps them to manage profitably, by planning well and knowing their target, for example you would like to know the market demand for soap Y, Bread X, or Razor Blade Z you may like to know who your competitors are, their strength and weaknesses.
Adherence to these statistical methods of predicting uncertainties will help you make better decisions, help you the business managers to minimize costly errors. One other thing noteworthy in statistics in the proper arrangement of numbers, their classification and summarization rather than mass of figures, numbers and formula 4. Tabular and graphical methods of arranging and presenting statistical data. The purpose of these methods is, of course to condense the information from a mass of figures raw data into a form which provide an informative summary and readily convey the essential features of the data, this is important for managers who only see and interpret result consider, for example the data shown in Table 1.
This shows that beer sales in each of the 60 public houses owned by Bitter Beer Brewery for January Table 1 Beer sales in public houses 48 71 52 53 36 41 69 58 47 60 53 29 41 72 81 37 43 58 68 42 73 62 59 44 51 53 47 66 59 52 34 49 73 29 47 16 39 58 43 29 46 52 38 46 80 58 51 67 54 57 58 63 49 40 54 61 58 66 47 50 A simple way to condense such data is to draw up a tabular summary in the form of a frequency distribution as shown in table 2 in which the variable beer sales has been grouped in order to reduce it to a manageable form.
Graphical representations which include line graphs, line charts, bar charts, Histograms, Frequency polygons, Cumulative frequency curves 0 gives , Pie charts, Graphs.
Business movements overtime are often of special interest in line graphs in that they highlight trends line charts are instructive given that in a survey by a company, the number of defective goods returned to each of its 60 distributors makes it easier to ascertain where the variable number of defective goods is discrete and the data is ungroup. Whereas, for grouped data, bar www. Bar charts are simple extension of the idea of a line chart, using bars to replace lines.
Bar charts are also particularly useful when the variable of interest can be subdivided into a number of components which are to be illustrating of the same time. Histogram being similar to bar charts except for that there are no gaps between the bars. It can be used to cases involving either discrete or continues data, unlike bar charts which apply only to the former. Frequency polygons provide a useful alternative to the histogram, particularly when one wants to compare two distributions on the same diagram.
To try to do this with histogram would mean that some parts would over-lap and it would be difficult to distinguish one histogram from the other.
Cumulative frequency curves 0gives show the distribution of categories 1 data say weights of male passengers on a cumulative basis. The data presented on this form are referred to as a cumulative frequency distribution and the graphical representative as a cumulative frequency curve 0give. Arithmetic mean is the most commonly used and is simply known as average or the mean. It is obtained simply by se Adding the value of all the items in a data set denoted by X1, X2, Xn and dividing by the number of items denoted by n.
This is particularly, relevant in business situations where the manager may be interested in measuring the rate of increase in sales, costs, price etc. There are large number of techniques particularly the concepts of probability and probability distributions lie at the heart of statistical analysis and, in particular statistical inference and decision making. The study of probability involves assessing, in the contest of uncertainty, the likelihood that something will occur by change e.
In statistical inference we typically wish to make some decision about population parameters. Given information available only from a sample of data draw from the population. There are two main areas of the subject; estimation of the population parameters and testing hypothesis about them.
Another important aspect of data analysis is the measurement of relationship between variables. This topic involves, on the one hand, statistical tests of association between actual and assumed distributions of data as well as tests of independence of categorized data.
On the other hand, we are able to determine the nature of www. Thus, there is always an element of uncertainly on most of the decisions of managers undertake. If a manager makes the outcome is certain, the puzzle is said to be deterministic. For the manger, it spears as if he is doing a random selection. He cannot say for sure the content of his venture due to some chance of elements that spoil his expectations. Probability is a brother to may-be or may-be-not type of statement. If the manager is perfectly sure of his outcome, he is said to be certainty operator and certainly denotes a probability of one 1 i.
A situation of perfect uncertainty has a probability of 0. All the decisions where the manager is neither perfectly unsure nor perfectly sure have probabilities of valve between 1 and 0.
Probability cannot be greater than 1 and there is no negative probability. The probability of any management event stands between one and zero.
IT uses Statistics in various areas like, optimisation of server time, assessing performance of a program by finding time taken as well as resources used by the Program. It is also used in testing of the software. In Marketing, Data mining can be used for market analysis and management, target marketing, CRM, market basket analysis, cross selling, market segmentation, customer profiling and managing web based marketing, etc. In Risk analysis and management, it is used for forecasting , customer retention, quality control, competitive analysis and detection of unusual patterns.
In Finance, it is used in corporate planning and risk evaluation, financial planning and asset evaluation, cash flow analysis and prediction , contingent claim analysis to evaluate assets, cross sectional and time series analysis, customer credit rating, detecting of money laundering and other financial crimes.
In Operations, it is used for resource planning , for summarising and comparing the resources and spending. In Retail industry, it is used to identify customer behaviours, patterns and trends as also for designing more effective goods transportation and distribution policies, etc.
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