Table Q3: Complex modulus of the bitumenTest temperature(°C)Complex modulus, G*(kPa)Log G*4652.21.725224.521.395810.991.04645.320.73702.550.41 Q3 A rheology test has been conducted on asphalt binder to investigate the relationshipbetween test temperature and complex modulus (G*). The results from the tests areshown in Table Q3.(a) Determine a relationship between temperature and log G*.(b) Calculate and interpret R2 in term of the model variables.(c) Estimate the failure temperature of the bitumen if the failure temperature is carriedout from the temperature when complex modulus is 2.2 kPa.

a) The regression analysis is conducted here by using EXCEL. The software procedure is given below:…

Answered: Q3 A rheology test has been conducted…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 91E

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Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".† x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651 y 334 342 355 363 365 372 381 392 400 412 420 Here is regression output from Minitab: Predictor Constant absorb S = 3.60498 Coef 321.878 156.711 SOURCE Regression Residual Error Total SE Coef 2.483 6.464 R-Sq = 98.5% DF 1 9 10 SS 7639.0 117.0 7756.0 T 129.64 24.24 0.000 0.000 R-Sq (adj) = 98.3% MS 7639.0 13.0 F P 587.81 (a) Does the simple linear regression model appear to be…
Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".t 半 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651 334 342 355 363 365 372 381 392 400 412 420 Here is regression output from Minitab: Predictor Coef SE Coef P Constant 321.878 2.483 129.64 0.000 absorb 156.711 6.464 24.24 0.000 S = 3.60498 R-Sq = 98.5% R-Są (adj) - 98.3% SOURCE DF MS F P Regression 1 7639.0 7639.0 587.81 0.000 Residual Error 9 117.0 13.0 Total 10 7756.0 (a) Does the simple linear regression model appear to be…
Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".t x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651 y 334 342 355 363 365 372 381 400 392 412 420 Here is regression output from Minitab: Predictor Constant absorb S = 3.60498 Coef 321.878 156.711 SOURCE Regression Residual Error Total R-Sq= 98.5% DF SE Coef 2.483 6.464 1 9 10 SS 7639.0 117.0 7756..0 T 129.64 24.24 P 0.000 0.000. R-Sq (adj) 98.3% MS 7639.0 13.0 F 587.81 (a) Does the simple linear regression model appear to be appropriate?…
Solve using excel or spreadsheet and show data analysis (regression table)
A study of a high-pressure inlet fogging method for a gas turbine engine analyzed a complete second-order model for y , heat rate (kilojoules per kilowatt per hour) of a gas turbine as a function of two quantitative variables • x1 , cycle speed (revolutions per minute) • X2 , cycle pressure ratio and a qualitative predictor, engine type, at three levels (traditional, advanced, and aeroderivative). We define two dummy variables 1 if advanced 3o if not if aeroderivative 3o if not X3 = and x4 = Write the model equation for advanced engine type.
A study of a high-pressure inlet fogging method for a gas turbine engine analyzed a complete second-order model for y, heat rate (kilojoules per kilowatt per hour) of a gas turbine as a function of two quantitative variables • x1 , cycle speed (revolutions per minute) • x2 , cycle pressure ratio and a qualitative predictor, engine type, at three levels (traditional, advanced, and aeroderivative). We define two dummy variables (1 if advanced X3 = 0 if not {o 1 and X4 = ここ if aeroderivative 0if not There are 67 observations in the dataset. We conduct a global F-test for overall model adequacy at a level of significance 5%. 1. Numerator degrees of freedom 2. Denominator degrees of freedom 3. If F-value equals to 30.52, then we (reject or fail to reject) the null hypothesis at 5%. There is (sufficient or insufficient) that the complete second-order model is overall useful.
A study of a high-pressure inlet fogging method for a gas turbine engine analyzed a complete second-order model for y , heat rate (kilojoules per kilowatt per hour) of a gas turbine as a function of two quantitative variables • x1 , cycle speed (revolutions per minute) • X2 , cycle pressure ratio and a qualitative predictor, engine type, at three levels (traditional, advanced, and aeroderivative). We define two dummy variables S 1 0 if not { if advanced 1 if aeroderivative X3 = and X4 if not If the engine type is traditional, then x3 = and x4 When you plug in these two values in the complete second-order model equation, you will get the model equation for traditional engine type.
A study of a high-pressure inlet fogging method for a gas turbine engine analyzed a complete second-order model for y , heat rate (kilojoules per kilowatt per hour) of a gas turbine as a function of two quantitative variables • x1 , cycle speed (revolutions per minute) • x2 , cycle pressure ratio and a qualitative predictor, engine type, at three levels (traditional, advanced, and aeroderivative). We define two dummy variables S 1 {o if if advanced if aeroderivative and x4 X3 = not 0 if not Write a complete second-order model for heat rate (y ) as a function of cycle speed, cycle pressure ratio, and engine type.
The authors of the paper "Power-Load Prediction Based on Multiple Linear Regression Model"t were interested in predicting the load on the electric power system in China using data on y = Power consumption (in hundreds of millions of kwh), x, Population (in millions), and x, = Gross domestic %3D product (in billions of dollars), for 21 years. The model equation proposed in the paper is y = -113,527 + 0.974x, + 0.057x, + e. (a) According to this model, what is the mean power consumption (in hundreds of millions of kwh) for a year if the population was 160,000 million and the gross domestic product was 600,000 billion dollars? hundreds of millions of kwh (b) Interpret the value of B, in this model. When the [ gross domestic product v is fixed, the mean increase in [power consumption (in hundreds of millions of kwh) V associated with a 1-million unit increase in [population is 0.974.
Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres
3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3- Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 a) Find out the equation describing the degradation of lycopene in tomatoes at 15°C, using the "linear regression analysis. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / n ) ΣyΣx%- Σx Σxy b = Ex² - [ (Ex)² / n) ] η (Σχ'- [ (Σx)>/ n) ] )
3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3– Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 Find out the lycopene concentration in tomatoes after 4 months of storage at 15°C. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / ) ΣyΣx?- Σx Σxy Ex² - [ (Ex)² / n) ] n (E x² - [ (Ex)² / n) ] )

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