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Generating forecasts - Essay Example

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Informed decision is grounded on realistic factors. Realistic factors include all independent factors. Each independent factor separately affects the outcome of the future dependent factor’s…
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Generating forecasts
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Download file to see previous pages Since the current and future customers have more money to buy the company’s goods and services, it is possible to predict an increase in the purchase of the stores’ product sales and services revenues. Statistical tools help make more informed store management decisions.
In the same manner, the increase in certain independent factors may indicate a possible decline in the dependent factor. For example, an increase in the government’s taxes will reduce the workers’ take home pays or salaries. Consequently, the reduced take home pays will reduce the workers’ purchasing power. Consequently, the decision makers must expect a decline in the stores’ sales and service revenues. With the reduced take home pay, the employees must cut down their avoidable expenses.
The table 1 data shows the company can generate the future weeks’ projected revenues (Johnson, 2010). The expected future sales are grounded based on the above multiple independent variables. The dependent variable is the revenues. As dependent variable, the sales output is normally dependent on the many independent variables.
The above table shows that the competitors often sell their products at prices that are reasonable. A reasonable price takes into consideration several relevant factors. One of the relevant factors is the demand for the products. A high customers’ demand for the products will encourage the stores to increase their selling prices. However, a low demand for the stores’ products and services persuades the store managers to offer discounted prices. With the discounts, the customers will take advantage of the price reductions. A price reduction will normally trigger a higher demand for the stores’ products and services (Johnson, 2010).
The above table 2 shows the summary of the statistical findings’ regression analysis for the ten weeks. The Multiple regression output is shown to be 0.63. The R Squared figure is 0.40. The Adjusted R squared figure is -.0950. ...Download file to see next pagesRead More
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