As mentioned above‚ bacterial growth rates during the phase of exponential growth‚ under standard nutritional conditions (culture medium‚ temperature‚ pH‚ etc.)‚ define the bacterium’s generation time. Generation times for bacteria vary from about 12 minutes to 24 hours or more. The generation time for E. coli in the laboratory is 15-20 minutes‚ but in the intestinal tract‚ the coliform’s generation time is estimated to be 12-24 hours. For most known bacteria that can be cultured‚ generation times
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sold‚ their capital expenditure and their labour expenditure for the past 10 years. After that we ran a regression test so as to find out if the industry has received increasing or decreasing or negative returns to scale. Cobb-Douglas production function Q= a*(L^b)*(K*c) where Q= output‚ L= labour‚ K= Capital and b+c= 1 and if b+c=1…… Constant returns to scale b+c>1……………..Increasing returns to scale
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FORECASTING FORECASTING The Role of the Manager Planning Organizing Staffing Leading Controlling Future ? Data Information • Short-range • Medium-range • Long-range Features Common to All Forecasts Forecasting techniques generally assume that same underlying causal system that existed in the past will continue to exist in the future. Forecasts are rarely perfect. Forecasts for groups of items tend to be more accurate than forecasts for individual items. Forecast
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Patrick England Ducati individual write up #3 Management 495 2/7/2012 Can Ducati sustain its position in the sport segment? Can Honda and other Japanese manufactures stop its growth in this segment? The ability for Ducati to sustain its position in the sport segment of motorcycles is going to depend on management’s ability to stay focused on keeping their image and story alive and in the minds of the motorcycle community. If management continues its current trend I believe that
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Cézanne’s painting break the illusion of the visual effect created by the harmony of colours‚ whereas in the Bathing Nymphs there is no apparent trace of the brushwork. The lines that are used by Cézanne around the central figures‚ the bodies‚ have as function to distinguish these bodies from the background‚ another aspect that affects the creation of illusion‚ since these lines do not exist in the visible world. In Vecchio’s there are no lines of this sort‚ and we distinguish each surface not for the
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the sum of the infinite sequence tn. The analysis will also discuss the scope and limitations of the general statement and test the validity by using various values of a and x. Conclusions will be supported by mathematical examples. The following function will be considered‚ where x and a remain constant throughout the entirety of the sequence and the numerator is raised to the next consecutive number. Tn = As observed‚ a is the argument of the natural logarithm (ln)‚ x
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ATILIM UNIVERSITY DEPARTMENT OF MATHEMATICS Math 211 - Discrete Mathematics with Applications 2010-2011 Fall Semester Problem Set I Prepared by Mehmet TURAN O−‚ Ω−‚ Θ− Notations 1. Let f and g be real valued functions defined on the same set of nonnegative real numbers. (a) Prove that if g(x) is O(f (x))‚ then f (x) is Ω(g(x)). (b) Prove that if f (x) is O(g(x)) and c is any nonzero ral number‚ then cf (x) is O(cg(x)). (c) Prove that if f (x) is O(h(x)) and g(x) is O(k(x))‚ then f (x) + g(x) is
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Quantitative Method- Used when situation is “stable” and historical data exists. Used for existing products and current technology. Involves mathematical techniques. E.G.‚ forecasting sales of color televisions. Naïve approach‚ moving averages‚ exponential smoothing‚ trend projection‚ linear regression. Time Series Forecasting- Set of evenly spaced numerical data. Obtained by observing response variable at regular time periods. Forecast based only on past values‚ no other variables important. Assumes
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from the graph that the four-week moving average forecast smoothes the data the most‚ while the naïve forecast responds to change the best. (1 mark) 4.5 Given the following data‚ use exponential smoothing ( = 0.2) to develop a demand forecast. Assume the forecast for the initial period is 5. Exponential Smoothing Forecast Ft = Ft-1 + (At-1 – Ft-1) ie F2 = F1 + (A1 – F1) = 5 + 0.2 (7 – 5) = 5.4. Carrying this through to week 7 gives: Period Demand Exponentially Smoothed Forecast 1
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past patterns in data can be used to forecast future data points. 1. Moving averages (simple moving average‚ weighted moving average): forecast is based on arithmetic average of a given number of past data points 2. Exponential smoothing (single exponential smoothing‚ double exponential smoothing) - a type of weighted moving average that allows inclusion of trends‚ etc. 3. Mathematical models (trend lines‚ log-linear
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