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- A short column has a diameter X₁ and is loaded with an axial compressive load X₂. The ultimate compressive stress of the column is X3. The variables have the following mean and s.d. values: X₁ X₂ X3 Mean 3.5 10.0 2.5 Determine the reliability index of the system. s.d. 0.4 1.0 0.5I need help solving this problemThe following scatterplot shows the mean annual carbon dioxide (CO,) in parts (CO2) per million (ppm) measured at the top of a mountain and the mean annual air temperature over both land and sea across the globe, in degrees Celsius (C). Complete parts a through h on the right. f) View the accompanying scatterplot of the residuals vs. CO2. Does the scatterplot of the residuals vs. CO, show evidence of the violation of any assumptions behind the regression? 16.800 A. Yes, the outlier condition is violated. 16.725 O B. Yes, the linearity and equal variance assumptions are violated. 16.650 C. Yes, the equal variance assumption is violated. 16.575 O D. No, all assumptioris are okay. 16.500 O E. Yes, all the assumptions are violated. 325.0 337.5 350.0 362.5 CO2 (ppm) OF Yes, the linearity assumption is violated. his vear, What mean temperature does
- 1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a Detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 ????=3586.38−0.150746 ? ???????,?2=0.103287,???=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? Test the significance of the slope coefficient of the regression in Part (1) above. Use 5% level of significance on: a. Level of significance approach (show your calculations of t-ratio) b. P-value approach (show your calculation of p-value) please show the complete steps as well as the interpretation(s) involved in each of the above…
- 1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? A- The researcher claims that the model lacks a fundamental principle, namely, the impact of the worker’s gender on their efficiency. The researcher further claims that Men on average take more resting time than women. To clarify the researcher’s claim, add the binary variable MALE into your model, and write down the estimated results….*.. 2] Document3 OFF AutoSave ^ 中。 Design Layout References Mailings Review >> Tell me 2 Sha sert Draw Calibri (Bo... 12 A^ A Aa v Ao Paragraph Create and Share Adobe PDF Dictate BIUVab x, x A Av Styles The boxplot below shows salaries for Construction workers and Teachers. Construction Teacher 25 35 45 55 40 Salary (thousands of $) 20 Jennie makes the first quartile salary for a construction worker. Markos makes the third quartile salary for a teacher. Who makes more money? of O Jennie O Markos How much more? $ 1 of 1 O words English (United States) JAN1) What is the line of best fit for this data? a. y =-1.11x+ 11.83; r=-0.9760964904 Average Speed (mi/h) Time (hours) b. y 11.83x- 1. 11; r=0.9527643586 8.5 2.5 c. y= 11.83x –- 1. 11; r=-0.9760964904 7.5 3.75 d. y =-1.11x+ 11.83; r= 0.9527643586. 6.5 4.5 6.0 5.0 5.5 5.5 5.0 6.25 4.0 6.75 3.5 8.75 r notes MacBook Pro * 2$ & 4 5 6 7 8 9. %3D deletc { [ Y ] G H J K L > ? C V N M + || .. .. B
- For the following data. i_xi_fi 00 1 13 4 267 39 9 4 12 15 i) ii) if data points at i=1,2,3 are considered, write the followings: -the first differences=? -the second differences=? -P₂(s)=? in terms of s, and the first and second differences. -S=? df(x=6)/dx=? By using only two data points.need with bell shape curve pleaseDraw the appropriate graph corresponding to the frequency distribution