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- You estimated the following regression. What value would you predict for Y, if X = 46? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 452 -------------+---------------------------------- F(1, 450) > 99999.00 Model | 451909533 1 451909533 Prob > F = 0.0000 Residual | 1435457.34 450 3189.9052 R-squared = 0.9968 -------------+---------------------------------- Adj R-squared = 0.9968 Total | 453344990 451 1005199.53 Root MSE = 56.479 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 12.27466 .0326116 376.39 0.000 12.21057 12.33875 _cons | 69.67934 3.949949 17.64 0.000…A firm keeps a record of sales and prices over the past seven months, resulting in the following table: Price (ZMW/ton) Sales (tons) Nov. 1985 7.5 84.5 Dec. 8.0 82.0 Jan. 1986 8.0 84.0 Feb. 7.2 92.0 March 7.0 95.0 April 8.0 92.0 May 8.5 91.5 Use these observations to estimate demand as a linear function of both price and time. Further, utilise this function to estimate demand for the following month, on the assumption that: (a) price remains unchanged, (b) price increases to ZMW9/ton. Hence estimate the price elasticity of demand between these prices and find the price which would maximise sales revenue. Given the nature of the observations, comment on any difficulties in interpreting your results for decision-making purposes.A newspaper article mentions that two of South Korea’s largest semiconductor manufacturers, - Samsung Electronics Company and Hynix Semiconductor - would suspend all their memory chip production for some time. The article goes on to say that another large semiconductor manufacturer was likely to follow suit. Collectively, these chip manufacturers produce about 30 percent of the world’s basic semiconductor chips. Based on this information, how is the market for computers altered? This answer involves no numerical computation. However, graphs/plots should be used to elaborate your answer. Explain plus make graphs for this answer.
- part a b and c solved Suppose that a coffee producing firm estimated the following regression of thedemand for its brand of coffee:Qc = 1.5 − 3.0Pc + 0.8Y + 2.0Pb − 0.6PS +1.2 Awhere Qc = sales of coffee brand C, in dollarsper pound Pc = price of coffee brand C,in dollars per poundY = personal disposable income, in millions of dollars per yearPb = price of the competitive brand of coffee, in dollarsper pound Ps = price of sugar, in dollars per poundA = advertising expenditures for coffee brand C, in hundreds of thousands ofdollars per year.Suppose also that this year, Pc = $2, Y = $2.5, Pb = $1.80,Ps = $1 and A =$1.a. Interpret the results of the estimated demand.b. Compute point price elasticity of demand for the firm’s brand of coffeewith respect to its price.c. Compute the cross-price elasticity of demand for coffee with respect to theprice of competitive coffee brand b.d. At the current price level, would it be viable for the firm to increase the pricelevel of its brand of coffee?…1] kindly tell the difference between log -linear , linear - log ,log -log and linear - linear regression . Out of all these, which is approporaite to carry out GDP regression . 2] also , is it needed to convert all the data to "ln" by typing " =ln" ,if regression is done using excel ?The table to the right contains price-demand and total cost data for the production of projectors, where p is the wholesale price (in dollars) of a projector for an annual demand of x projectors and C is the total cost (in dollars) of producing x projectors. Answer the following questions (A) - (D). (A) Find a quadratic regression equation for the price-demand data, using x as the independent variable. X 270 360 520 780 The fixed costs are $. (Round to the nearest dollar as needed.) ITTI y = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) Use the linear regression equation found in the previous step to estimate the fixed costs and variable costs per projector. The variable costs are $ per projector. (Round to the nearest dollar as needed.) (C) Find the break even points. The break even points are (Type ordered pairs. Use a comma to separate answers as needed. Round to the nearest integer as…
- You estimated the following regression. Source | SS df MS Number of obs = 117 -------------+---------------------------------- F(1, 115) = 0.06 Model | 2523.61281 1 2523.61281 Prob > F = 0.8003 Residual | 4513680.09 115 39249.3921 R-squared = 0.0006 -------------+---------------------------------- Adj R-squared = -0.0081 Total | 4516203.7 116 38932.7906 Root MSE = 198.11 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 40.18036 158.4597 0.25 0.800 -273.6979 354.0586 _cons | -556.9531 11761.12 -0.05 0.962 -23853.48 22739.57 ------------------------------------------------------------------------------…DVANCED ANALYSIS Assume that demand for a commodity is represented by the equation P=80−2Qd.�=80−2��.Supply is represented by the equation P=−20+2Qs,�=−20+2��,where Qd and Qs are quantity demanded and quantity supplied, respectively, and P is price.Instructions: Round your answer for price to 2 decimal places and enter your answer for quantity as a whole number. Using the equilibrium condition Qs = Qd, solve the equations to determine equilibrium price and equilibrium quantity. Equilibrium price = $ Equilibrium quantity = unitsDetermine and discuss the impact of diesel prices on fuel revenues and the impact of gasoline prices on fuel revenues. What is the relationship between meal prices and meals? Define, measure, and discuss the price elasticities. HINT: Find price elasticity of demand by multiplying the slope of the regression line from Excel by the mean real price then divide by the mean quantity demanded. Please do fast ASAP
- As the selling price of a product increases, demand decreases. Fewer people will buy a product if it is more expensive. This is a very important concept. The demand equation for new iSmarts is given by 0.025x , where p is the unit p = 100 price (in dollars) of the iSmart and x is the number of units produced. We can determine the total revenue (money earned for selling x iSmarts) by multiplying the number of items we sell, x and the price p. The total revenue obtained by producing and selling x iSmarts is given by = xp 100x – 0.025x². It makes sense that this function is quadratic. The company can price the iSmarts so high that no one will buy them and the revenue will be zero. The can also set the price to $0, but the revenue will still be $0, no matter how many iSmarts they "sell". In between, the graph is a parabola opening downward. Determine prices p that would keep revenue above 6530 dollars. Give your answers to 2 decimal places. The revenue will be above 6530 if price is…With milk sales sagging of late, The Milk Processor Education Program (MPEP) decided to move on from the famous "Got Milk" ad slogan in favor of a new one, "Milk Life." The new tagline emphasizes milk's nutritional benefits, including its protein content. MPEP began collecting data on the number of gallons of milk households consumed weekly (in millions), weekly price per gallon, and weekly expenditures on milk advertising (in hundreds of dollars) for the period following the launch of the new campaign. These data are available via the link below. Use these data to estimate a linear regression. Linear Model Log-Linear Model Q P A lnQ lnP lnA 4.76 2.46 472.68 1.56 0.90 6.16 0.90 4.28 326.41 -0.10 1.45 5.79 1.74 3.72 357.36 0.55 1.31 5.88 0.96 4.20 475.82 -0.04 1.43 6.17 2.38 4.14 494.25 0.87 1.42 6.20 1.28 4.59 458.62 0.25 1.52 6.13 2.86 3.30 421.67 1.05 1.19 6.04 1.87 4.34 534.85 0.63 1.47 6.28 2.19 3.31 524.75 0.78 1.20 6.26 1.38 3.35…Given are five observations for two variables, x and y. Excel File: data14-17.xlsx 9 13 20 Yi 7 18 26 23 The estimated regression equation for these data is ŷ = 7.6 + 0.9x . Compute SSE, SST, and SSR (to 1 decimal). SSE SST SSR What percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % What is the value of the sample correlation coefficient (to 3 decimals)? 2] 100