elasticity is to regress the natural logarithm (In for short) of quantity against the natural logarithm of price as follows: such a model is called a log-log model. In(QQRRaaKRIMYRIOQRNNTERRIINNSSTTRR In this case, the coefficient of the In(Price) directly gives an estimate of the average elasticity. Thus, the elasticity, = pp Compute the elasticity by estimating a log-log regression model. ' (For instructions on running a regression model in Microsoft Excel, see, for example: https://www.wikihow.com/Run-a-Multiple-Regression-in-Excel ). d. (0.5 point) Using the elasticity calculated in (b) or (c) above, calculate the optimal markup on cost using the formula in footnote 6. Apply this optimal markup formula to calculate the optimal price using the formula in the article. e. (0.75 point) Should you run further A/B tests to confirm the optimal price? (Hint: You could estimate how much additional profit you might gain by fine-tuning the price through an additional A/B test - this could then be compared with the cost of running a test. To estimate the potential profit from fine-tuning the price, estimate the demand at the optimal price that you calculated in (d), and then estimate the profit at that price.) f. (0.5 point) According to the rule of thumb in the article, "A Dashboard for Online Pricing,", how would the elasticity calculated in (b) change if the number of competing sellers doubles? What would be the corresponding optimal price? An alternative regression model for estimating the clasticity is to use the following model: QQ RRaa RRINTERNCKRN NITERMNSSITRR The slope goof this regression line gives an estimate of JOG in the elasticity formula. It is then customary to calculate the average elasticity using the following formula: A log-log model, on the other hand, directly provides the elasticity measure from the coefficient of In (Price)