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Importance and Application of probability:
Importance of probability theory is in all those areas where event are not certain to take place as same as starting with games of chances. Probability has become one of the basis tools of statistics. Statistics and probability are much interrelated with each other that it is difficult to discuss statistics without understanding the meaning of probability. A knowledge of probability theory makes it easier, simpler and possible to interpret the statistical result, as many of the statistical procedures include involve conclusions based on samples which are randomly choosen.
Probability theory is being applied not only in mathematical problems but also for the solutions of social economical, political and business problems. The insurance insustry emerged in the 19th century, required exact or definite knowledge about the risk of loss inorder to calculate premium. Within few decades the concept of probability has assumed great importance and the mathematical theory of probability has become the basis of statistical application and social and decision making. In fact probability has become a part of everyone life. In personal and management decisions one faces uncertainty and uses probability theory. By organising information and considering it systematically, one will be able to recognise his or her assumptions, communicate his or reasoning to others and make a better decision than he or she could by using a shot in the dark approach. Probability theory in fact is the foundation of statistical conclusion or inference.
A study was designed to investigate the effects of two variables - (1) a student's level of mathematical anxiety and (2) teaching method - on a student's achievement in a mathemati
Features of index numbers
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practical application of standard error
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For calculating the mode of the grouped data graphically, the following procedure is adopted. Draw a histogram of the data; the modal class is the tallest rectangle.
The 4 assumptions of regression: 1. Variables are normally distributed 2. Linear relationship between the independent and dependent variables 3. Homosced
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