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The marketing manager of Handy Foods Ltd. is concerned with the sales appeal of one of the company's present label for one of its products. Market research indicates that supermarket consumers ?nd little appeal in the drab, somewhat cluttered appearance of the label. The company hired a design artist who produced some prototype labels, one of which was chosen consistently as best by the marketing executives. Nevertheless, the marketing executive is still in some doubt as to whether the new label would appreciably bene?t sales. He decides to make further enquiries about the consequences of a decision to switch to a new label. The decision to change to a new label is denoted by D1 and to keep the old by D2.
First he considers the costs associated with converting his company's machinery, inventory, point of purchase displays, etc., to the new label, and estimates that an out-of-pocket, once and for all cost of £250,000 would be involved. If the new label were really superior to the old, the marketing executive estimates that the present value of all net cash ?ows over and above this cost related to increased sales generated over the next three years by the more attractive label will be £400,000. Based on his prior experience and the discussion held with his colleagues, he is only willing to assign a 0.5 probability to the outcome 'new label superior to old', denoted B1. Let B2 denote the event that 'new label is not superior to the old'. Rather than make his decision on these data alone, however, he could delay it and obtain further market research information. The survey is such that it is 'perfect' at a cost of £150,000. The information from the market research survey is shown as either positive (R) or negative ( R) in favour of the new label. Draw a decision tree and decide whether it is worth carrying out market research.
Suppose that $4 million is available for investment in three projects. The probability distribution of the net present value earned from each project depends on how much is invest
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hello I have a dataset including both categorical & numerical variable for market segmentation.how can i cluster them via k-means in matlab? thank you
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