An economic research centre has published data on GDP and Demand for refrigerators as given below: Year 2011 2012 2013 2014 2015 2016 2017 GDP (billion) 20 22 25 27 30 33 35 Refrigerator 50 60 80 80 90 100 120 (a) Estimate regression equation R= a+by, where R= No of refrigerator sold and Y= GDP. Forecast demand for refrigerator in the year 2018 and 2019. The research centre has projected GDP for 2018 and 2019 at Rs. 38 billion and Rs. 40 billion respectively

An economic research centre has published data on GDP and Demand for refrigerators as given below: Year 2011 2012 2013 2014 2015 2016 2017 GDP (billion) 20 22 25 27 30 33 35 Refrigerator 50 60 80 80 90 100 120 (a) Estimate regression equation R= a+by, where R= No of refrigerator sold and Y= GDP. Forecast demand for refrigerator in the year 2018 and 2019. The research centre has projected GDP for 2018 and 2019 at Rs. 38 billion and Rs. 40 billion respectively

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 1.4CE

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Interpret the coefficients and calculate the price elasticity of soft drink demand.
The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week: week guests bar sales 1 16 $330 2 12 $270 3 18 $380 4 14 $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place): Bar sales = [___]+[___]X guests
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 answer
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 =$1.a. Interpret the results of the estimated demand.b. Compute point price elasticity of demand for the firm’s brand of coffeewith respect to its price.c. Compute the cross-price elasticity of demand for coffee with respect to theprice of competitive coffee brand b.d. At the current price level, would it be viable for the firm to increase the pricelevel of its brand of coffee? Support your answer.…
An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41
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The Ipod Touch has been out for several years now and a lot of data has been collected. There is a functional relationship between the Price of an IPod Touch and Weekly Demand. Below is a table of data that have been collected Price P ($) Weekly Demand S (1,000s) 150 207 170 208 190 192 210 190 230 185 250 169 A.. Find the linear model that best fits this data using regression and enter the model below (for entry round the slope value to nearest 0.01 and constant parameter to nearest 1) T(p) = Now answer these two questions: B.. What does the model predict will be the weekly demand if the price of an ipod touch is $191 ? (nearest 100) C.. According to the model at what should the price be set in order to have a weekly demand of 194,800 ipod Touches? $ (nearest $1) Note: In the "real" world Apple sold about 20 million Ipod Touch's from Sept. 2007-Sept. 2009
The diagram shows what happened to the consumption of lamb in the UK over the period 1974– 2015. How can we explain this dramatic fall in consumption? One way of exploring this issue is to make use of a regression model, which should help us to see which variables are relevant and how they are likely to affect 140 130 120 110 100 90 80 70 60 50 40 30 20 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 Note: Data from 2015 based on end of financial year. Source: Based on data in Family Food datasets UK consumption of lamb: 1974–2017 The following is an initial model fitted to annual data for the years 1974–2010. QL = 144.0 – 0.137PL – 0.034PB + 0.214PP – 0.00513Y + e (1) where: QL is the quantity of lamb sold in grams per person per week; PL is the ‘real’ price of lamb (in pence per kg, 2000 prices); PB is the ‘real’ price of beef (in pence per…
Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shells
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