Statistics Example

Statistics

Date: 16th March

To: Vice President, Myra Reid

From: The forecast department

Subject: Advertising, Sales and production forecast

This memo outlines the sales forecast for the next one year in terms of sales and production,

Advertising in the company plays a major role in the organisation sales level and because competitors have increased their advertising budget then there is a high possibility that they may capture our market share if the company does not use a proper advertising strategy.

However previous data regarding advertisement budget by competitors, the company has spent less than its competitors and that advertisement budget has increased over the past years and as a result there has been an increase in market share and sales. Therefore an increase in advertising budget will increase sales and at the same time increase market share.

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According to the sales model estimated an increase in one unit of sales will result into a 0.0676 in sales budget, this model has a 0.9131 coefficient of determination and this means that 91% of deviations in the advertising budget are determined by sales holding other factors constant. The correlation coefficient for the data is 0.9555 and this means that there is a strong positive relationship between sales and advertisement spending.

The retail coverage model depict that if there is an increase in retail coverage by one unit then the advertisement budget will increase by 1.9 units holding all other factors constant, the coefficient of determination is 0.5934 depicting that 59% deviations in advertising budgets are explained by retail coverage. The correlation coefficient is 0.7703 meaning that there is a strong positive relationship between the two variables.

This last model is the competitors advertising budget model and this model depict that an increase in the level of competitors budget by one unit will result into an increase in the companies advertisement budget by 0.8666 units, the correlation coefficient in this case is equal to 0.8838 meaning that there is a strong relation between the two variables, the coefficient of determination is equal to 0.781 meaning that 78% of deviations in advertising budget are explained by the competitors budget.

Having estimated the three model the next step I took is to choose the best model that could be used in forecasting, the sales model is the best due to the strong relationship that exist between the two variables. The correlation matrix also shows that there is a strong correlation between sales and other variables.

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The expected market in the industry is 40 billion which is 5 billion less, in year 11 the sales level was 2,454 million and in year 12 sales level was 2,264 and therefore we expect the sales level to increase and if sales level increase then the advertising budget will also increase. If we expect the sales level to increase to 2,500 then the advertising budget will increase to 169 million. An Increase in advertising expenditure will increase promotional activity according to Tim.

Market size has steadily increased over the years and this means that there is a high possibility of increase in sales, in year 12 the sales level was 39,049 and this is expected to rise to over 40,000 in the next year, for this reason therefore we choose the weighted moving average based on 2 periods where year 11 has 0.1 weight and year 12 has 0.9 weight. From this point we set the production level at 60 units with seasonal variations of 12 units in the first quarter, 16 units in the second quarter, 13 units in the third quarter and 20 units in the forth quarter.

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References:

Bluman A. (2000) Elementary Statistics: A Step by Step Approach, McGraw Hill press, New York