Statistical Methods in Measuring Corporate Performance

2008 WordsOct 5, 20119 Pages
The purpose of this paper is to provide an overview of commonly used statistical data in the measurement of corporate performance by examining various models and methods. Corporate performance can be defined as fundamental measures of organizational aptitude used to assess the “health” of the organization and to provide focused direction to operations while supporting managers. In order to measure the company’s intangible assets such as customer relationships, internal processes, and employee learning and development while aligning the corporation’s overall business strategy, statistical analysis of performance measurement data must be utilized. The task of evaluating the latest performance measures and aligning business strategy…show more content…
Each is a reaction to the findings of the one before until a pattern can be observed. This is also rarely used in performance measurement. Probability Distributions Range and distributions depend upon the amount of data available. The information sought in performance measurement is usually continuous, that is, measurable – and therefore denoted as f(x). Since the probability or distribution of possible probabilities is continuous, the likelihood that the probability is any specific point estimate is really zero. The range for probability then depends on the level of valid, available information. A probability distribution indicates all values that a random variable can assume and shows the likelihood of occurrence. By examining distributions of data, important characteristics such as shape, location, variability, and unusual values can be detected. The conclusion as to the relationships between different variables can then be generated from the observation of patterns in data. An example of a normal distribution test is the Gauss curve. The bell shaped curve represents the odds of each outcome on the range of several outcomes. In a normal distribution observations are distributed symmetrically around the mean, 68% of all values under the curve lie within one standard deviation of the mean and 95% lie within two standard deviations3. This is good for intervallic continuous variables. Another type of distribution is done when the results are not ranged along a
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