The extraction Of answers from data provides the foot print for further investigation and research. There are several different types and levels of statistics, they are nominal, ordinal, interval, and ratio (“Data Levels of Measurement”, 2014).
The first level being nominal, allows for the use of numbers, words, and letters to classify information.The second level is ordinal, which allows for a more ordered relationship between the data. The third level is interval, shows the distances between each numbers on the scale. The fourth and final level is ratio. Ratio can have a value of zero and have an equivalent distance between points. The role of statistics in decision making is to provide the essential information need to make a proper decision.
Statistical analysis, especially in a competitive market allows a company to lean on factual information from data gathered, rather than guess work.Companies are no longer running off instincts or guesswork, if they want to survive and see profits, then they need old hard facts, managers are taking to the data to give them punctual and precise information, the information is at their fingertips, whether it be from production floor numbers and trends, to information gathered through software, such as SAP. The information gathered is not essentially sorted in to “understandable data”, we must organize the information into a table with understandable column titles. There are many ways in which statistics can help solve a problem.The first example could be the use of data to help build a model of money spent per onto/year in a corporation, by department. The company could even couple this with amount each person in that department spent. The second way statistics can help solve a problem is by giving a corporation real rime data from the floor to pug in to through put charts showing work trends and materials, so the company has factual information to provide investors and the board, when they are asking for more money to expand.