The experimental results were as follows:Tr. No. IrrigationOrganic fertilizerYield (t/hm² )Rep.1Rep.2Rep.31IQ1OF111.312.211.62IQIOF212.613.013.53IQ1OF315.614.816.04IQ1OF414.314.113.65IQ2OF111.811.713.26IQ2OF213.612.914.87IQ2OF314.518.318.28IQ2OF416.615.817.99IQ3OF111.512.814.510IQ3OF212.813.515.011IQ3OF315.715.414.812IQ3OF416.416.315.8You are required:Conduct the ANOVA by SPSS (F-test and multiple comparison) accordingdifferent experimental design mentioned above and draw a brief conclusion forthis result.

Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Step 7: Step 8: Step 9: Step 10: Step 11: Step 12:

Answered: The experimental results were as…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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2
Japan's high population density has resulted in a multitude of resource-usage problems. One especially serious difficulty concerns waste removal. The article "Innovative Sludge Handling Through Pelletization Thickening"t reported the development of a new compression machine for processing sewage sludge. An important part of the investigation involved relating the moisture content of compressed pellets (y, in %) to the machine's filtration rate (x, in kg-DS/m/hr). Consider the following data. 125.4 98.3 201.6 147.2 146.0 124.5 112.3 120.2 161.1 178.8 y 77.9 76.9 81.3 80.0 78.1 78.5 77.4 77.2 79.9 80.4 159.4 145.7 74.9 151.5 144.2 124.8 198.6 132.6 159.6 110.7 79.7 79.2 76.9 78.1 79.6 78.3 81.7 76.8 79.1 78.5 Relevant summary quantities are X; = 2817.4 Fy, = 1575.5, 5x? = 415,815.84, Fxy, = 222,698.08, Fy? = 124,149.53. Also, x = 140.870, y = 78.78, Sy = 18,928.7020, Sy = 757.395, and SSE = 9.222. The estimated standard deviation is o = 0.716 and the equation of the least squares line is…
Consider the following simple relationship between aggregate savings St and aggregate income Yt: St= a + BYt + εt, t = 1, ..., T. For some country this relationship is estimated by OLS over the years 1956-2005 (T = 50). The results are given below: = Variable Coefficients Constant 38.90 Y 0.098 22.57, R² = 0.93, DW = 0.70 Standard error 4.57 0.009 where ô is the standard error of regression, R² is the multiple correla- tion coefficient, and DW is the Durbin-Watson statistic. (a) Explain why the result indicate that there may be a problem of positive autocorrelation. Can you give arguments why in eco- nomic models positive autocorrelation is more likely than nega- tive one?
I need help with part (c) only, I need t statistic and p value. Mist (airborne droplets or aerosols) is generated when metal-removing fluids are used in machining operations to cool and lubricate the tool and workpiece. Mist generation is a concern to OSHA, which has recently lowered substantially the workplace standard. An article gave the accompanying data on x = fluid-flow velocity for a 5% soluble oil (cm/sec) and y = the extent of mist droplets having diameters smaller than 10 µm (mg/m3): x 88 177 182 354 369 442 970 y 0.39 0.60 0.50 0.66 0.61 0.69 0.92 (a) The investigators performed a simple linear regression analysis to relate the two variables. Does a scatter plot of the data support this strategy? Yes, a scatter plot shows a reasonable linear relationship.No, a scatter plot does not show a reasonable linear relationship. (b) What proportion of observed variation in mist can be attributed to the simple linear regression relationship between velocity and…
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…
Shown below is a portion of a computer output for regression analysis relating Y (demand) and X(unit price). ANOVA Df SS Regression 1 Error 3132.661 Total 47 8181.479 Coefficients Standard Error Intercept 80.390 3.102 X -2.137 0.248 Compute the coefficient of determination and fully interpret its meaning. Be very specific. Find the standard error for b1(Sb1). Perform a t test and determine whether or not demand and unit price are related. Let significance level= 0.05. Perform an F test and determine whether or not demand and unit price are related. Let significance level= 0.05.
Q3. Find out the regression coefficients of Y on X and of x on Y on the basis of following data: Ex = 50, X = 5, EY = 60, Y = 6, EXY = 350 Variance of X= 4, Variance of Y= 9
A worker with a weight of 65 kg and a body surface area of 1.74 , performs physical work activities in several work environments as shown in the following table. The work activities of these workers require energy and a measured metabolic rate of 400 W/m2. During their work activities, the worker wears a coverall made of polyolefin and wears a headgear (1) Calculate the average WBGT of the work environment. (2) Perform an analysis related to the heat stress experienced by the worker, if the worker performs his work activities for 75% of his total working time and the rest is rest, (note: use TLV from ACGIH and ISO 7243)
Source SS df MS Number of obs 935 F(2, 932) Prob F R-squared 66.31 %3D Model 19023273.1 9511636.56 0.0000 Residual 133692895 932 143447.312 0.1246 %3D Adj R-squared 0.1227 %3D Total 152716168 934 163507.675 Root MSE 378.74 %3D wage Coefficient Std. err. P>|t| [95% conf. interval] IQ 8.503461 .8240927 10.32 0.000 6.886169 10.12075 age 22.19023 3.991491 5.56 0.000 14.35688 30\.02358 _cons -637.3626 159.7452 -3.99 0.000 -950.8645 -323.8608 Consider the following regression estimates (ID233), where wage is monthly wage in US$, IQ is score on an IQ inte Interpret the age coefficient.

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