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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- 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 answerThe 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 guestsGiven 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…
- Given the following summary statistics, determine the regression equation used to predict y from Ta Round all answers to 2 decimal places. slope - y-intercept Sy SI T 15 Y 1.02 1.6 -0.71 20.65 77-9 Use the exact value of slope when calculating the y-intercept.1. Suppose that you have following data: Variable Description CEO salary measured in thousands of $ Firm's sale measure in millions ofS Return on equity in percent Salary sales roe *Return on equity is a measure of financial performance calculated by dividing net income by shareholders' equity. Your estimated regression is given by log (salary) = 4.322 + 0.276 log(sale) + 0.0215roe - 0.0008roe?, R = 282, n = 209. (324) (0.033) (0.0129) (0.00026) a) Is the effect of all independent variables statistically equal to 0? b) Interpret the coefficient on log(sale). c) Interpret the effect of roe on log(salary). • Without more information, your interpretation of the effect of roe on log(salary) should include answers to these sub-question. Should the roe be included in this model? il. Comment on relationship between roe and log(salary): is it U-shaped or inverse U-shaped? What is the turning point? How would you interpret this point? Plot log(salary) vs roe. v. ii. iv. Compute predicted value…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.…
- How do you interpret the R-squared obtained from running this regression?A company sets different prices for a particular DVD system in eight different regions of the country. The accompanying table shows the numbers of units sold and the corresponding prices (in dollars). Sales 420 380 350 400 440 380 450 420 Price 104 195 148 204 96 256 141 109a. Graph these data, and estimate the linear regression of sales on price. b. What effect would you expect a $50 increase in price to have on sales?26) 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. 0.08
- 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…Suppose the relationship between the government's tax revenue (T) and national income (Y) is represented by the equation T = 30 +0.5Y. Plot this relationship on a scale diagram, with Y on the horizontal axis and T on the vertical axis. Interpret the equation. Use the line drawing tool to draw the equation. Make sure to plot the vertical axis as one endpoint of the line. Properly label this line. Carefully follow the instructions above, and only draw the required objects.6) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)
