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 ?
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A: Ans in step 2
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
2] also , is it needed to convert all the data to "ln" by typing " =ln" ,if regression is done using excel ?
We are going to analyze differences among the various forms of regression models.
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- 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 guests26) Consider the following regression line: i= -7.29 + 1.93 x YearsEducation. You are told that the t-statistic on the slope coefficient was 24.125. What is the standard error of the slope coefficient? (assume 5% level of significance) A. -0.08 B. 0.30 C. 1.64 D. 0.08An economic research centre has published data on GDP and Demand for refrigerators as given below:Year 2011 2012 2013 2014 2015 2016 2017GDP (billion) 20 22 25 27 30 33 35Refrigerator 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.
- What is the functional form of this equation? What are the advantages and limitations of this functional form? Interpret precisely the coefficients of Px and Py in the regression.Explain the OLS Estimator in Multiple Regression in detail?The Weibracht Corporation designs and manufactures custom beer steins for some of the numerous brew pubs in western North Carolina. Each stein costs Weibracht $8 to produce. The brew pubs purchase the steins from Weibracht for resale to their customers and require a 51% margin. Weibracht’s marketing director has conducted a regression analysis using historical data, resulting in the following regression line: q = -12 * p + 326, where q = slope * retail price + MWB What is the profit maximizing price that Weibracht should charge the brew pubs for these custom steins?
- Consider the simple linear regression model given by E(y) = 1.5 + 0.23*x where y is measured in litres and x is measured in dollars. What must be the value of the slope coefficient if x is measured in thousands of dollars while the unit of measurement of y is unchanged (i.e., x is divided by 1000)? Answer:In multiple OLS regressions, if you are using power terms to fit for nonlinearity, how do you interpret the coefficients? For example: Yi=B1+B2X+B3X^2+Ui and B2 and B3 are both significant.From the following data, determine if the data has a positive or a negative relationship with each other. Showcase the regression line, and determine if the data provided fits the approximate curve.
- You estimated the following regression. What value would you predict for Y, if X = 81? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 204 -------------+---------------------------------- F(1, 202) = 406.05 Model | 6131684 1 6131684 Prob > F = 0.0000 Residual | 3050340.21 202 15100.6941 R-squared = 0.6678 -------------+---------------------------------- Adj R-squared = 0.6661 Total | 9182024.21 203 45231.6463 Root MSE = 122.88 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 40.27997 1.998931 20.15 0.000 36.33853 44.22142 _cons | 192.9333 120.837 1.60 0.112…In the December, 1969, American Economic Review (pp. 886-896), Nathanial Leff reports thefollowing least squares regression results for a cross section study of the effect of age composition onsavings in 74 countries in 1964:log S/Y = 7.3439 + 0.1596 log Y/N + 0.0254 log G - 1.3520 log D1 - 0.3990 log D2 (R2= 0.57)log S/N = 8.7851 + 1.1486 log Y/N + 0.0265 log G - 1.3438 log D1 - 0.3966 log D2 (R2= 0.96)where S/Y = domestic savings ratio, S/N = per capita savings, Y/N = per capita income, D1 = percentage ofthe population under 15, D2 = percentage of the population over 64, and G = growth rate of per capitaincome. Are these results correct? Explain..You have a regression equation as follows: GDPt = α + β1Mt + β2Pt + ut where: t = time period GDPt = growth of gross domestic product (%) Mt = growth of money supply (%) Pt = percentage change of consumer price index (CPI) (%) ut = stochastic disturbance term 1) Based on the regression equation, construct the estimated regression equation. 2) Describe the residual precisely. Elucidate in what way it is related to ut.