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
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A: Ans in step 2
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.
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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 guestsQ3. 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
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- Given 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…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 $1) Note: In the "real" world Apple sold about 20 million Ipod Touch's from Sept. 2007-Sept. 2009Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shells
- RQ7. A teacher is trying to predict student test grades (Q). She believes test grades are a function of incoming GPA, hours studying, and hours spent on social media (a distraction). She runs a regression and it produces these coefficients: Variable Coefficient Intercept GPA Hours Studying Social Media 70.0 3.5 2.4 -4.0 For a given student Julian, his GPA is 2.0, he studies 4 hours for the exam, and he spends 6 hours on Facebook. Predict his exam score (round to the nearest whole number).A manufacturer is developing a facility plan to provide production capacity for its factory. The amount of capacity required in the future depends on the number of products demanded by its customers. The data below reflect past sales of its products: Year Annual Sales (number of products) Year Annual Sales (number of products) 1 490 5 461 2 487 6 475 3 492 7 472 4 478 8 458 Use simple linear regression to forecast annual demand for the products for each of the next three (3) years, by using the tabular method to: derive the values for the intercept and slope derive the linear equation plot the linear regression line develop a forecast for the firm’s annual sales for each of the next three years4. The following regression is fitted using variables identified that could be related to tuition charges ($) of a university. TUITION = a+ B ACCEPT + y MSAT + 1 VSAT Where ACCEPT = the percentage of applicants that was accepted by the university, MSAT = Median Math SAT score for the freshman class and VSAT = Median English SAT score for the freshman class. The data was processed using MNITAB and the following is an extract of the output obtained: Predictor Coef StDev Constant -26780 6115 ACCEPT 116.00 37.17 MSAT -4.21 14.12 VSAT 70.85 15.77 т P -4.38 0.000 0.003 -0.30 4.49 0.767 ** S = 2685 R-Sq 69.6% R-Sq (adj) = 67.7% Analysis of Variance Source DF SS MS Regression 3 Residual Error 49 Total 52 808139371 353193051 1161332421 269379790 7208021 F 37.37 Р 0.000 a) Write out the regression equation. b) State the dependent and independent variable(s) c) Fill in the blanks identified by ** and ****. d) Is significant, at the 10% level of significance? [1] [2] [6] [4] e) State one…