The Turbine Oil Oxidation Test (TOST) and the Rotating Bomb Oxidation Test (RBOT) are two different procedures for evaluating the oxidation stability of steam turbine oils. An article reported the accompanying observations on x = TOST time (hr) and y = RBOT time (min) for 12 oil specimens. TOST 4200 3600 3750 3650 4050 2795 RBOT 370 340 375 310 350 205 TOST 4870 4500 3450 2700 3750 3275 RBOT 400 375 285 225 345 290 (a) Calculate the value of the sample correlation coefficient. (Round your answer to four decimal places.)

The Turbine Oil Oxidation Test (TOST) and the Rotating Bomb Oxidation Test (RBOT) are two different procedures for evaluating the oxidation stability of steam turbine oils. An article reported the accompanying observations on x = TOST time (hr) and y = RBOT time (min) for 12 oil specimens. TOST 4200 3600 3750 3650 4050 2795 RBOT 370 340 375 310 350 205 TOST 4870 4500 3450 2700 3750 3275 RBOT 400 375 285 225 345 290 (a) Calculate the value of the sample correlation coefficient. (Round your answer to four decimal places.)

Name: The Turbine Oil Oxidation Test (TOST) and the Rotating Bomb Oxidation
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Description: The Turbine Oil Oxidation Test (TOST) and the Rotating Bomb Oxidation Test (RBOT) are two different procedures for evaluating the oxidation stability ofsteam turbine oils. An article reported the accompanying observations on x = TOST time (hr) and y

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Question 2: A study of the properties of metal plate-connected trusses used for roof support yielded 35 observations on axial-stiffness index (kips/in.) for plate lengths 4, 6, 8, 10, and 12 inches. Use the given output from an analysis of variance with a = 0.01 to answer (a) and(b). Let 'pl.4' denote a plate length of 4 inches, similarly for the other lengths. Df Sum Sq Mean Sq F value Pr (>F) 4 43993 10998 10.48 1.96e-05 *** 1049 0 ***' 0.001 '**' 0.01 0.05 '.' 0.1 ' group Residuals 30 31475 Signif. codes: (a) (b) Tukey multiple comparisons of means 99% family-wise confidence level Fit: aov (formula p adj $group diff 1wr upr pl.12-pl.10 29.814286 -31.98149 91.610065 0.4363237 pl.4-pl.10 -74.142857 -135.93864 -12.347078 0.0015294 pl. 6-pl.10 -39.300000 -101.09578 22.495779 0.1827394 pl. 8-pl.10 -32.228571 -94.02435 29.567208 0.3591363 pl.4-pl.12 -103.957143 -165.75292 -42.161364 0.0000130 pl.6-pl.12 pl. 8-pl.12 pl.6-p1.4 pl.8-pl.4 1 pl. 8-pl.6 -69.114286 -130.91006 -7.318507 0.0033328…
Attempt 2 Question 5 of 10 Suppose a biologist studying the mechanical limitations of growth among different species of tulips monitors a national preserve. He collects data on the heights of 10 different types of tulips in the reserve and rounds each height to the nearest centimeter. 25, 21, 26, 24, 29, 34, 29, 25, 20, 23 Compute the first quartile (Q), the third quartile (Q3), and the interquartile range (IQR) of the data set. %3D cm cm Q3%D contact us help about us careers privacy policy terms of use 44 De
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?
The price X (dollars per pound) and consumption y (in pounds per capita) of beef were samples for 10 randomly selected years. The following data should be used to answer the question that follows. n = 10 Ex = 36.19 Ix² = 134.17 2.9 < x s 6.2 Ey = 774.7 Iy² = 60739.23 Exy = 2832.21 Using this data, a student calculated SSy = 28.43 SSx = 3.2 SSy= 717. Calculate the %3D %3D value of the standard error or regression, Se , and enter you answer accurate to the nearest hundredth (2 decimal places).
Please use the accompanying Excel data set or accompanying Text file data set when completing the following exercise. An article in Urban Ecosystems, "Urbanization and Warming of Phoenix (Arizona, USA): Impacts, Feedbacks and Mitigation" (2002, Vol. 6, pp. 183–203), mentions that Phoenix is ideal to study the effects of an urban heat island because it has grown from a population of 300,000 to approximately 3 million over the last 50 years and this is a period with a continuous, detailed climate record. The 50-year averages of the mean annual temperatures at eight sites in Phoenix are shown below. Check the assumption of normality in the population with a probability plot. Construct a 95% confidence interval for the standard deviation over the sites of the mean annual temperatures. Site Sky Harbor Airport 23.3 Average Mean Temperature (°C) Phoenix Greenway 21.7 Phoenix Encanto Waddell 21.6 21.7
Question 1 {0 decimal places } Find the value of the sample mean.
Chlorophylls a and b are plant pigments that absorb sunlight and transfer the energy into photosynthesis of carbohydrates from CO2 and H2O, releasing O2 in the process. Chlorophylls were extracted from chopped up grass and measured by spectrophotometry. The table shows results for chlorophyll a for four separate analysis of five blades of grass. Chlorophyll a (g/L) Blade 1 Blade 2 Blade 3 Blade 4 Blade 5 1.09 1.26 1.1 1.23 0.85 0.86 0.96 1.21 1.3 0.65 0.93 0.8 1.27 0.97 0.86 0.99 0.73 1.12 0.97 1.03 Four replicate measurements for each blade of grass tell us the precision of the analytical procedure (sanalysis). Differences between mean values for each of the five blades of grass are a measure of variation due to sampling (ssampling). Using Excel and it’s ANOVA function, find the standard deviations attributable to sampling and to analysis, as well as the overall standard deviation arising from both sources.
Question 5 of 19 Compute the MSE for the following data. Period 1 2 3 4 5 6 13 Select the correct response. 12 11 Actual 492 470 485 493 498 492 14 Forecast (Error) 495 465 487 490 499 498 -5 -3 1 6 |Error] 3 LO 5 2 3 1 6 Error² 9 25 4 9 1 36 Error/Actual*100 0.60 1.06 0.41 0.60 0.20 1.21
Aa Febru The body mass index (BMI) of a person is defined to be the person's body mass divided by the square of the person's height. The article "Influences of Parameter Uncertainties within the ICRP 66 Respiratory Tract Model: Particle Deposition" (W. Bolch, E. Farfan, et al., Health Physics, 2001:378-394) states that body mass index (in kg/m2) in men aged 25-34 is lognormally distributed with parameters u = 3.215 and o = 0.157. a.Find the mean and standard deviation BMI for men aged 25-34. b.Find the standard deviation of BMI for men aged 25-34. c.Find the median BMI for men aged 25-34. d.What proportion of men aged 25-34 have a BMI less than 20? e.Find the 80th percentile of BMI for men agėd 25 -34. 04... Rext 田
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. The article "Variables Affecting Mist Generaton from Metal Removal Fluids" (Lubrication Engr., 2002: 10-17) 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 y 89 177 189 354 362 442 965 .40 .60 .48 .66 .61 .69 .99 a. The investigators performed a simple linear regres- sion analysis to relate the two variables. Does a scat- terplot of the data support this strategy? b. What proportion of observed variation in mist can be attributed to the simple linear regression relationship between velocity and mist? c. The investigators were particularly interested in the impact on mist of increasing velocity from 100 to 1000 (a…
Do the following plots show 1. Constant variability 2. Nearly normal residuals 3. Independent observations for SLR (conditions for linear regression)
please solve that question with calculation use numerical method