Explain what is the relation between the CO2C emissions per capita between the GDP, gross fixed capital formation(GFCF), trade openness(trade openness), foreign direct investment (FDI). SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservations0.9940.9880.9860.25935.000ANOVASignificance F0.000dfMSFRegressionResidual4.000160.94340.236599.17930.00034.0002.0150.067Total162.958Coefficients0.8950.0000.0000.0000.035I Stat5.0040.6500.3904.2735.879Upper 95%1.2600.0010.0000.0000.048Upper 95.0%1.2600.0010.0000.000Standard ErorP.valueInterceptGDPGFCFFDI0.1790.0000.0000.0000.0060.0000.5210.6990.0000.000Lower 95%0.530-0.0010.0000.0000.023Lower 95.0%0.530-0.0010.0000.0000.023то0.048

Given information: The EXCEL output for the multiple linear regression is given.

Answered: Explain what is the relation between…

Explain what is the relation between the CO2C emissions per capita between the GDP, gross fixed capital formation(GFCF), trade openness(trade openness), foreign direct investment (FDI)

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter4: Polynomial And Rational Functions

Section4.6: Variation

Problem 37E

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Model Summary Adjusted R Square Std. Error of the Estimate Model R Square .506* .256 .248 5.286 a. Predictors: (Constant), PERCENT FEMALE COMMITTED NONOFFENSE, PERCENT FEMALES COMMITTED DELINQUENT, PERCENT MALE COMMITTED NONOFFENSE, LENGTH OF STAY: FEMALES - MONTHS, PERCENT OF MALES COMMITTED DELINQUENT, LENGTH OF STAY: MALES- MONTHS Coefficients Standardized Coefficients Unstandardized Coefficients Model B Std. Error Beta Sig. 1 (Constant) .617 .272 2.273 .023 LENGTH OF STAY: .785 165 .405 4.757 .000 MALES - MONTHS LENGTH OF STAY: FEMALES - MONTHS -.103 .163 -.052 -.630 .529 PERCENT OF MALES COMMITTED DELINQUENT -1.009 1.417 -.059 -.712 .477 PERCENT FEMALES COMMITTED DELINQUENT 4.818 1.344 .279 3.584 .000 PERCENT MALE COMMITTED NONOFFENSE -6.294 4.891 -.059 -1.287 .199 PERCENT FEMALE СOMMITTED NONOFFENSE -2.842 3.239 -.041 -877 .381 a. Dependent Variable: EDU STAFF INSTRUCT: PAYROLL-FT Order the effect of these variables on the number of educational staff on payroll-list the strongest…
The scatter plot shows the sizes and annual rents of some office spaces in the downtown area of a city. Rent (dollars) 140,000 105,000 70,000 35,000 SIZE AND ANNUAL RENT OF OFFICE SPACE 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Office Space (square feet) What would the line of best fit reveal about these data? There is a weak positive relationship between the cost of rent and the size of the office space. There is a strong negative relationship between the cost of rent and the size of the office space. There is a strong positive relationship between the cost of rent and the size of the office space. There is a weak negative relationship between the cost of rent and the size of the office space.
Weight 1.897 Cost 53.6 3.019 184.9 0.453 6.4 0.988 23.5 1.058 33.4 2.1 110.4 2.387 104.6 People at SpaceX Inc conducted a pilot study to determine the relationship between the weight and cost of a major object being sent into space. The results of the pilot study are contained in data file ASP.1, available in the LBRT #2 folder. Determine the number of observations needed to be 89% confident that the estimate of the average cost is within $15 of the overall mean cost. Note: Assume that the pilot study standard deviation is a good estimate of the population standard deviation, that an appropriate value from the Z-table can be used, and that hand calculations are used to find the answer (i.e. do not use Kaddstat).
Q13.Quantity demand of Heating Gas t=β0 - β1 Price of Heating Gas+ β2 Income per capital SUMMARY OUTPUT Regression Statistics Multiple R 0.91723906 R Square ………………….. Adjusted R Square 0.826215825 Standard Error 1.972012508 Observations ………………… ANOVA df SS MS F Regression 2 433.0140833 216.507 ………………… Residual 21 …………………. 3.888833 Total …. 514.6795833 Coefficients Standard Error t Stat P-value Intercept -2.105 2.374617513 -0.88646 0.385409797 Price of Heating Oil -0.578833333 0.104968381 -5.51436 1.79846E-05 Income Per Household 4.075 0.493003127 8.265668 4.85848E-08 Complete the (?) on table by correct number Write down the multiple regression equation. How much is the % variation in the house price that is explained by the independent…
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period.Seafood 2000 Qty. (lb) | 2000 Price ($/lb) | 2009 Price ($/lb)Halibut 75,090 1.91 2.33Lobster 83,080 3.72 3.09Tuna 50,779 1.87 1.97(a)Compute a price relative for each type of seafood. (Round your answers to one decimal place.)Seafood Price RelativeHalibut 122.0 Lobster _________ Tuna 105.3 (b)Compute a weighted aggregate price index for the seafood catch. (Round your answer to one decimal place.)I2009 = ___________ Comment on the change in seafood prices over the nine-year period. (Enter your percentage as a positive value. Round your answer to one decimal place.)Seafood prices have decreased by ______ % over the 9-year period according to the index.
S DAILY M x WEEK X 6 Microsc x S WEEK x O Micros x 6 Micros 8 SKILLC X M nbox ( x 8fad3ebd013fc2038e53478cfbbc97a28d/MyFiles/Downloads/Week One_Distance_Midpoint Segment.. Q* dition.pdf 3/ 12 129% + Find the value of x if H is the midpoint of GJ, GJ = 4x – 6, and GH = 27. 14. %3D
В. A model estimated using a dataset with 125 observations generates the following results. SS df MS Regression 919587.543 4 229896.9 Error 2590390.62 121 534.2113 Std. Variable B Error t P>lt| X2 -0.0126355 0.005519 -2.28937 0.022 X3 0.5957923 0.014482 41.13934 0.000 Х4 1.124589 0.877192 1.282032 0.200 X5 0.3237421 0.060709 5.332661 0.000 constant 8.86016 1.766116 5.016749 0.000 What is the R2 for this sample? What information does the R² provide?
1 Open on a Jamboard 8.SP.2-2014(#55) The scatter plot shows the sizes and annual rents of some office spaces in the downtown area of a city. SIZE AND ANNUAL RENT OF OFFICE SPACE 140,000 105,000 70,000 35,000 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Office Space (square feet) What would the line of best fit reveal about these data? A There is a strong negative relationship between the cost of rent and the size of the office space. There is a strong positive relationship between the cost of rent and the size of the office space. B There is a weak positive relationship between the cost of rent and the size of the office space. There is a weak negative relationship between the cost of rent and the size of the office space. 10:04 AM 4/5/2021 ASUS f8 O/点团 f9 f10 f11 f12 pause break prt sc insert de sysrq 7 8 9 backs Rent (dollars)
Based on the SPSS output given below is the Intention to Purchase Branded Shoes, BrandAwareness, Brand Association, Brand Perception and Brand Loyalty on customers in Kangar,Perlis for the year 2018. Model Summaryb Model R R Square Adjusted RSquare Std. Error of theEstimate Durbin-Watson 1 .218a .047 .036 1.08730 1.158 ANOVAa Model Sum ofSquares df Mean Square F Sig.1 Regression 18.972 1 4.743 4.012 .003bResidual 380.672 328 1.182Total 399.645 329Coefficientsa Model Unstandardized Coefficients StandardizedCoefficients B Std. Error Beta t Sig.1 (Constant) 2.7 .412 7.050 .000Brand Awareness -.107 .060 -.101 -1.774 .077Brand Association -.073 .054 -.077 -1.351 .178Brand Perception -11.228 5.552 -7.996 -2.022 .044Brand Loyalty 11.415 5.565 8.110 2.051 .041i. Identify dependent and independent variablesii. Develop a regression model to predict the Intention to Purchase Branded Shoes.iii. Interpret the value of 2.902.iv. Explains the value of R and Adjusted R Squared.v. Determine which…
Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: Intercept educ IQ KWW exper black feduc meduc Least Squares Wed, 23 Nov 2022 08:57:40 coef -503.0058 40.5455 3.1654 7.2094 16.2338 -59.2987 10.8278 5.0471 std err nonrobust 138.947 8.660 1.199 wage OLS 2.120 3.740 52.143 5.353 6.106 722 714 7 R-squared: Adj. R-squared: Prob (F-statistic): F-statistic: Log-Likelihood: AIC: BIC: -3.620 4.682 2.640 3.400 4.340 -1.137 2.023 0.827 P> [t] 0.000 -775.800 0.000 0.008 0.001 0.000 0.256 0.043 0.409 0.189 0.181 23.83 23.543 0.812 3.047 8.891 -161.671 0.318 -6.941 3.310-29 -5288.5 (0.025 0.9751 1.0590+04 1.063e+04 -230.212 57.548 5.519 11.372 23.577 43.074 21.338 17.035 Above is a regression of men's monthly earnings in dollars as a function of their years of education, their IQ score in "points", their Knowledge of the World of Work (KWW) score in points (a measure of their knowledge of how occupations work), their years of work experience…
A measure of variation which has minimal used because of its instability. "