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CORNELL UNIVERSITY APPLIED ECONOMICS AND MANAGEMENT AEM 4240 – Management Strategy Tutorial for making initial decisions in CSG This tutorial answer key walks you through how to make the initial decisions that you will need to make for CSG. This tutorial uses historical market demand and average cost data. When you make the actual decisions for CSG, you should follow the same steps, but use the cost data specific to your firm (which will also be available through the CSG site). A. This answer key will focus on market C, and will use only the data where there is only one seller in the market. The other markets can be examined analogously: 1. In order to figure out what price to charge, you need to know how price relates to quantity. In this case, we have historical data on prices and quantities, which we can use to construct a demand curve. To do so, you can use Excel to estimate a regression. A regression is a way to fit a line through a set of data points, such that the line best fits the scatter plot of data. For example, if we simply plot our mkt quantity data as a function of price (using the Market C data for which there is only one firm), we get the following: 0 1000 2000 3000 4000 0 200 400 600 Market Price Market Quantity MKT QUANT We see that as price (x-axis) increases, quantity goes down. Now, what we want to do is find the straight line which best fits this data, so that we can consider other prices, and estimate what quantity would result. To do so, we will estimate the inverse of the demand curve, which shows how quantity varies as a function of price. To run the regression of quantity as a function of price using data from Market C, do the following steps in Excel using the data in the CSG tutorial spreadsheet that corresponds to Market C: Go to Tools, then Data Analysis, and then choose Regression. (Note that you may need to install optional packages in some versions of Excel to have the Data Analysis Tools. The public access versions on campus should all have it. If not, you can also do a scatter plot with a best-fit line and use display options to show the equation of the best fit line.)

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Where it says input Y-range, click on the little red arrow next to the space for the y-range. Then, highlight the cells B2-B21. Then click on the little red arrow. Where it says input X-range, click on the little red arrow next to the space for the x-range. Then, highlight the cells C2-C21. Then click on the little red arrow. Down near the bottom of the regression box, click on Line Fit Plots, and then click OK. You should then be shown regression output like this: SUMMARY OUTPUT Regression Statistics Multiple R 0.965073 R Square 0.931365 Adjusted R Square 0.927552 Standard Error 421.4425 Observations 20 ANOVA df SS MS F Significance F Regression 1 43383495 43383495 244.2574 6.47E-12 Residual 18 3197049 177613.8 Total
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