不Complete parts (a) through (c) using the following data.Row 1Row 2090224555656867871917373805665(a) Find the equation of the regression line for the given data, letting Row 1 represent the x-values and Row 2 the y-values. Sketch a scatter plot of the data and draw the regression line.Input the values of the slope and intercept for the regression line when Row 1 represents the x-values.(Round to three decimal places as needed.)Construct a scatter plot of the data and draw the regression line. Plot Row 1 on the horizontal axis and Row 2 on the vertical axis. Choose the correct graph below.OA.O B.○ C.○ D.Q100G100pG100QG(b) Find the equation of the regression line for the given data, letting Row 2 represent the x-values and Row 1 the y-values. Sketch a scatter plot of the data and draw the regression line.Input the values of the slope and intercept for the regression line when Row 2 represents the x-values.(Round to three decimal places as needed.)Submit test

Given information:Required:Scatter diagram and estimated regression line. Solution:(a) Step 1: Excel…

Answered: 不 Complete parts (a) through (c) using…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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You estimated the following regression. What value would you predict for Y, if X = 47? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 324 -------------+---------------------------------- F(1, 322) = 354.54 Model | 3686788 1 3686788 Prob > F = 0.0000 Residual | 3348384.74 322 10398.7104 R-squared = 0.5241 -------------+---------------------------------- Adj R-squared = 0.5226 Total | 7035172.74 323 21780.7206 Root MSE = 101.97 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 24.51522 1.301971 18.83 0.000 21.95378 27.07666 _cons | 98.70791 117.4919 0.84 0.401…
6. (a) Suppose that n = 100 i.i.d observations for (Y, X,) yield the following results where SER stands for the standard error of the regression: Ý 32.1+66.8X, SER=15.1, R2 = 0.81 (15.1) (12.2) Another researcher is interested in the same regression, but he makes an error when he enters the data into his regression program: He enters each observation twice, so he has 200 observations. Using these 200 observations, what results will be produced by his program? Write it in the following format: = 9+X, SER= (b) Suppose that a random sample of 200 twenty-year-old-men is selected from a population and that these mens height and weight are recorded. A regression of weight on height yields Weight -99.41 +3.94 x Height. Suppose further that instead of measuring weight and height in pounds and inches, these variables are measured in centimeters and kilograms. What are the regression estimates from this new centimeter-kilogram regression? (1 inch= 2.54 cm, 1 pound=0.45 kg) =
In a simple linear regression you are told that the estimate of the slope coefficient was 0.7 and that the "t-statistic" for testing whether the slope parameter was unity or not was-2.4. What is the estimated standard error for the estimated slope coefficient? (The answer should be correct to two decimal places.) Answer:
The following information regarding a dependent variable y and an independent variable x is provided. Find the slope of the regression equation. Ex = 90 Ey = 340 n = 4 SSR = 103 E(y - )(x - x) E(x – x)2 = 236 E(y - y)2 = 1,978 = -153 %3D %3D Select an answer and submit. For keyboard navigation, use the up/down arrow keys to select an answer. a -0.648 b -0.265 0.265
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…
2. Consider a two variable regression model, which satisfies all the Gauss Markov assumptions except that the error variance is proportional to X² i.e.E(u?) = o²X? Y₁ = B₁ + B₂X₁ + Ui How would you obtain the best linear unbiased estimates from the above regression.
The table shows the yield (in bushels per acre) and the total production (in millions of bushels) for corn in a country for selected years since 1950. Let x represent years since 1900. Find a logarithmic regression model (y = a + b In x) for the yield. Estimate the yield in 2029. The regression model is y=+()Inx. (Round to one decimal place as needed.) ~ Year 1950 1960 1970 1980 1990 2000 2010 X Yield 50 60 70 80 90 100 110 41 54 85 96 110 146 158
Answer in typing
4) identify of intercept (B0), slope of regression line (B1) , and the p value and analysis of their meanings
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.
1. An analyst from your firm used a linear demand specification to estimate the demand for its product and sent you a hard copy of the results: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Price of X Income 0.38 0.14 0.13 20.77 150 df 2 147 149 SS 58.87 -1.64 1.11 10398.87 63408.62 73807.49 Coefficients Standard Error 15.33 0.85 0.24 MS 5199.43 431.35 t Stat 3.84 -1.93 4.63 F 12.05 P-value 0.00 0.06 0.00 Significance F 0 Lower 95% 28.59 -3.31 0.63 Upper 95% b. Which regression coefficients are statistically significant at the 5 percent level? a. Based on these estimates, write an equation that summarizes the demand for the firm's product. 89.15 0.04 1.56 C. When price is $10, what is the income elasticity for this product for an income level of 35?
5. The following estimated equation was obtained by OLS regression using quarterly data for 1978 to 1996 inclusive. Yt = 2.20+ 0.104Xt₁ - 3.48 Xt₂ + 0.34Xt3 (3.4) (0.005) (2.2) (0.15) Standard errors are in parentheses, the explained sum of squares was 109.6, and the residual sum of squares 18.48. a. Test at the 5% level for the statistical significance of the parameter estimates. b. Calculate the coefficient of determination.

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