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- The demand equation for a particular candy bar is px + x + 20p = 3000 where 1000x candy bars are demanded per week when p dollars is the price per bar. If the current price of the candy is 49 dollars per bar and the price per bar is increasing at the rate of 0.2 dollars each week, find the rate of change in the demand.Variables typically included in a multivariate demand function (other than the price and quantity of the item the demand function represents) are consumer tastes and preferences, the number of buyers, spendable (disposable) income, prices of substitute goods, prices of complementary goods, advertising expenditures, weather, and expectations. Recalling that the price of the item being considered is placed on the vertical axis, and the quantity on the horizontal axis, the other variables are termed demand shifters. Please answer the following questions about the affect changes in other variables might have on the demand for the item. These changes will either cause demand to increase (shift right) or decrease (shift left). Use either word as applicable, for the short answer. If the price of a good increases because the demand for it increases, What would you expect the demand for its complement to do? If the demand for coffee beans increases, then what is likely to happen…The demand equation for a particular candy bar is px + x + 20p = 3000 where 1000x candy bars are demanded per week when p cents is the price per bar. If the current price of the candy is 49 cents per bar and the price per bar is increasing at the rate of 0.2 cent each week, find the rate of change in the demand.
- 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?…3. An econometrician regressed the cake sales on the cake price as well as the expenditure on advertising and its square. She obtained the following fitted regression equation, SALES = 109.719 – 7.640PRICE + 12.151ADVERT - 2.768ADVERT² Economic theory says the firm should increase advertising expenditure to the point where an extra $1 of expenditure results in an extra $1 of sales (i.e., marginal cost = marginal revenue). Choose the wrong statement. A. The term ADVERT2 captures some nonlinearity. A. This is an example of the (estimated) multivariate linear regression model. B. The estimated marginal revenue is 12.151 +2(−2.768)ADVERT. C. The estimated optimal level of advertising is approximately 2.014. D. A t test can be used to test the hypothesis that advertising does not affect sales. 4. Suppose we are interested in predicting the price of house using the interior area of house. Consider the regression model, PRICE = B1 + B2SQFT + yıPOOL + y2(POOL × SQFT) + u where PRICE is the…Filll in the values in the equation to calculate the PED for the Malabar coffee Price Elasticity of Demand (PED)= abs( % / % )=
- The demand equation for a particular candy bar is px + x + 20p = 3000 where 1000x candy bars are demanded per week when p cents is the price per bar. If the current price of the candy is 49 cents per bar and the price per bar is increasing at the rate of 0.2 cents each week, find the rate of change in the demand.Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash's most popular shoes, Pis the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Intercept P Coefficients Standard Error t Stat 21,055.04 1428.27 14.74 8.1E-16 -4.398 0.000 2.064 0.047 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for Balder Dash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c. What are the values for the own-price, income, and cross-price elasticities? d. If Pincreases by 6%, what would…TABLE 12.1 | Two Stage Least Squares Estimates of the Demand for Cigarettes Using Panel Data for 48 U.S. States Dependent variable: In(Qfte) – In(Qfte) cigarettes Regressor (1) (2) (3) In(Peisarettes) - In(Pçigarettes -0.94** -1.34** -1.20** i,1995 i,1985 (0.21) (0.23) (0.20) In(Inc;1995) – In(Inc;1985) 0.53 (0.34) 0.43 (0.30) 0.46 (0.31) Intercept -0.12 -0.02 -0.05 (0.07) (0.07) (0.06) Both sales tax and Instrumental variable(s) Sales tax Cigarette-specific tax cigarette-specific tax First-stage F-statistic 33.70 107.20 88.60 Overidentifying restrictions J-test and p-value 4.93 (0.026) These regressions were estimated using data for 48 U.S. states (48 observations on the 10-year differences). The data are described in Appendix 12.1. The J-test of overidentifying restrictions is described in Key Concept 12.6 (its p-value is given in parentheses), and the first-stage F-statistic is described in Key Concept 12.5. Individual coefficients are statistically significant at the *5%…
- 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.Q3. You are working as a researcher in an economic Institute, you want to study the relation between the Unit sales as a Dependent variable and the following independent variables (selling expenditure, advertising, competitive price) As shown in the following model Unit Sales + = b0+b1 Exp + + b2 Adv t b3 t+ compt + Ut After collecting your data, and estimating your linear regression over the data, you got the following regression equation comp t Unit Sales t = -10.5 - 0.51 Exp + + 0.09 Adv 3.05 b3 t + (2.45) (-1.5) t- value (4.2) (2.94) R² = 0.24 F- Value 0.33 ' 1- What is the economic meaning of the coefficient b0 (-10.5) 2- Describe the meaning of R² and its value, F - Value 3- What do you think about the Model as a whole, with F, R2 values....is it significant or not ....explain your answerGiven the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…