In business mathematics and statistics, the interpretation of results is an essential stage in the decision-making process. Statistical calculations by themselves do not provide managerial solutions unless their results are properly understood and applied to business situations. Managers use numerical information to understand past performance, evaluate present conditions, forecast future trends, compare alternatives, control operations, and select the most suitable course of action. Thus, interpretation means converting statistical results into meaningful information that can guide managerial decisions. Business statistics provides managers with various tools such as measures of central tendency, measures of dispersion, correlation, regression, probability, index numbers, time-series analysis, sampling, hypothesis testing, and statistical quality control. The results obtained from these techniques help managers reduce uncertainty and make decisions based on evidence rather than intuition alone. Measures of central tendency, such as arithmetic mean, median, and mode, help managers identify the typical or representative value of a dataset. For example, a sales manager may calculate the average monthly sales of a company to understand its normal level of performance. If the average sales are increasing over several periods, the manager may interpret this as an indication of business growth. However, the average should not be interpreted without considering the nature of the data. A few extremely high or low observations may distort the mean. Therefore, the median may sometimes provide a better representation of the typical value. Similarly, the mode can help identify the most frequently purchased product, the most common customer preference, or the most popular price category. Measures of dispersion, such as range, variance, standard deviation, and coefficient of variation, provide information about the consistency and risk associated with business activities. Two companies may have the same average profit but substantially different variations in their profits. A company with lower variation may be considered more stable and predictable. Managers can therefore use dispersion measures to assess operational risk, sales fluctuations, investment uncertainty, and employee performance. The coefficient of variation is particularly useful when comparing the variability of two or more datasets having different averages. Correlation analysis helps managers understand the degree and direction of association between two variables. For instance, a manager may examine the relationship between advertising expenditure and sales revenue. A positive correlation may indicate that higher advertising expenditure is associated with higher sales. A negative correlation may indicate an inverse relationship. However, managers must remember that correlation does not necessarily establish causation. A strong relationship between two variables may be influenced by another factor. Therefore, correlation results should be interpreted carefully before taking major business decisions. Regression analysis provides a more practical basis for prediction and planning. A manager can use a regression equation to estimate sales from advertising expenditure, demand from price, or production cost from output. If the estimated relationship is statistically meaningful and appropriate for the business environment, the manager can use it for forecasting. For