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- I need to answer these questions as soon as possible, pleaseAn independent researcher in district Wwants to study the monthly electricity consumption of households (E, measured in kilowatt hours) in the district. For his study, he selects a random sample of 100 households from the district and estimates the following regression function: Ê= 102.5+2.57N-0.0052N², (2.4558) (0.5455) (0.0061) where N denotes the number of members in the household and standard errors appear in parentheses. The researcher wants to test the hypothesis that the relationship between number of members in the house and the electricity consumption by households is linear, against the alternative that it is nonlinear. Suppose 2 denotes the population slope coefficient on the regressor N. The researcher conducts the test Ho: P₂ = 0 vs. H₁: ₂ *0. The test statistic associated with the test the researcher wants to conduct is (Round your answer to two decimal places. Enter a minus sign if your answer is negative.) At the 5% significance level, the researcher will (1). members…Find the equation of the least squares regression line of y on x, for the following sets of data: (a) 3 4 8 9. 11 14 4 5 7 8. y 1 2 4 9
- Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?(b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?Solve the second question in regression analysisFind the equation of the regression that model the relationship between the weight of mail and number of order using HYPERBOLIC EQUATION. Compute for the correlation coefficient using PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT (PPMCC).
- In a simple linear regression model, B1 has important relationships with a sample correlation coefficient r. Prove the following two identities. Tyn – 2 Syy SIT (2) se(3) (1) BIn a simple linear regression analysis (where y is a dependent and x an independent variable), if the y-intercept is positive, then a) there is a positive correlation between x and y. b) if y is increased, x must also increase. c) if x is increased, y must also increase. d) the estimated regression line intercepts the positive y-axis.1. Consider two least-squares regressions and y = Xíễ tế y = Xí$i+ XzB2 tê Let R2 and R2 be the R-squared from the two regressions. Show that R22 R2.
- Q2: For the raw data shown in Table below: 1) Find out which of the x₁ or x₂ variables is better correlated to y. 2) Find out the linear regression lines: y₁ = f(x₁) and y2 = f(x₂) depending on the results of 1) above. X1 1 3 4 11 14 X2 1 2 3 12 15 y 1 2 4 9 65 87 99 4 5 сл 7 12 8A researcher conducts a multiple regression with Y as the dependent variable and X1, X2, X3 and X4 as explanatory variables. Using the regression output below, fully describe this model and discuss important parts of the output. What is the predicted value of Y if X1 = 3, X2 = 15, X3 = 7 and X4 = 0.003? %3D SUMMARY OUTPUT Regression Staistics Muliple R R Square Adjusted R Square Standard Emor Observations 0.7236 0.5236 0.5159 5.3928 252 ANOVA Significance F 1. 10662E-38 SS MS Regression Residual 1973 9392 29.0820 67.8749 7895.7567 7183.2599 4 247 Total 251 15079.0166 Upper 95% 33.4049 Coefficients Standard Eror t Stat Pvalue 2.2273 0.026830873 Lower 95% 7.9594 2.0508 Intercept X1 17.7278 1.5583 0.2750 5.6662 4.05265E-08 1.0166 2.0999 X2 1.8376 0.1997 9.1999 1.4442 -74708 -3721 4324 1.55861E-17 2.2310 X3 55100 -5.5348 7.94036E-08 -3.5492 X4 -3.1079 1887 8435 -0.0016 0.998687788 3715 2166
