University of Bosaso
Prof. geyre Abduwali & Mr. Guhaad Abdirahman
Business Analysis and Decision Making
IntroductionMany firms always have difficult time of decision on some of the tools they can employ to come up with better decisions that can improve their sales. Most a times, management of a firm shall always hold meetings in with stake holders shall give various views why one option is better than the other. Business managers seldom think of any computer aided decision making tools which can help a company to come up with better decisions that can help them to predetermine the status of the sales in future based on any independent parameters that the sales depend on. Even though there are a number of options, linear regression stands out be the best statistical option that a business can put in practice to predict the quantity of sales its likely to make in future based on array of factors. This write-up shall use the number of hits on a webpage to predict the value of sales the business is likely to have in the next three months. Therefore, the write-up shall help to come up with records that shall help the client to determine the quantity of sales the client shall make daily in the future. This forecast shall be obtained using statistical linear regression method that shall be drawn using Excel spreadsheet.
Linear Regression Data AnalysisRegression curves are often used as an approach of visually illustrating the relationship between the independent (x) and dependent (y) variables so that one can easily conclude whether the two variables are related or not (Wilson, Keating & Galt, 2012). In addition, this research also uses the co-efficient of correlation, also called the R-square to determine whether the forecasted sales values can be relied on or not. The graph represents a scattered graph of the data provided on sales with equivalent hits that were made. Since Hits are independent of any other variable in this research, they are therefore put on the x-axis. In this research, the sales are dependent on the hits made on the web page. Since they are dependent on the number hits, they are put on the y axis. When a trend line is drawn it gives an equation y = 1.2485x + 519.44 and an R² of 0.5973. Based on the equation of the trend line, more forecasted values can be obtained as long as the number of hits is obtained. For instance, the following table summarises the outcome of sales in the next three months
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