Refer to the Journal of Engineering for Gas Turbines and Power (Jan. 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 11.25. Consider, again, the first-order model for heat rate ( y) as a function of speed (x₁), inlet temperature (x₂), exhaust temperature (x₃), cycle pressure ratio (x₄), and air flow rate (x₅). A SAS printout with influence diagnostics for the 67 observations in the GASTURBINE file is shown above. Interpret these results. Do you detect any influential observations? 

Data from Exercise 11.25

Refer to the Journal of Engineering for Gas Turbines and Power (Jan. 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 8.29. Recall that the heat rate (kilojoules per kilowatt per hour) was measured for each in a sample of 67 gas turbines augmented with high-pressure inlet fogging. In addition, several other variables were measured, including cycle speed (revolutions per minute), inlet temperature (C°), exhaust gas temperature (C°), cycle pressure ratio, and air mass flow rate (kilograms per second). The full data set is saved in the GASTURBINE file.

Data from Exercise 8.29

During periods of high electricity demand, especially during the hot summer months, the power output from a gas turbine engine can drop dramatically. One way to counter this drop in power is by cooling the inlet air to the gas turbine. An increasingly popular cooling method uses high-pressure inlet fogging. The performance of a sample of 67 gas turbines augmented with high-pressure inlet fogging was investigated in the Journal of Engineering for Gas Turbines and Power (Jan. 2005). One measure of performance is heat rate (kilojoules per kilowatt per hour). Heat rates for the 67 gas turbines are listed in the table on the bottom of page. Suppose that a standard gas turbine has, on average, a heat rate of 10,000 kJ/kWh. Conduct a test to determine if the mean heat rate of gas turbines augmented with high-pressure inlet fogging exceeds 10,000 kJ/kWh. Use α = .05.

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HEATRATE DFF I TS PRED 14262.043775 RES ID 359.95622475 RSTUDENT 0.8675525828 1 14622 0.1855911418 0.4141457267 13196 12527.866128 668.1338724 0.095127268 1.5482334503 0.5019901643 3 11948 12303.859631 -355.8596307 0.0519439992 -0.794131791 -0.185884543 4 11289 12038.477619 -749.4776187 0.0521260193 -1.703738164 -0.399535001 5 11964 12229.510458 -265.5104583 0.0495908163 -0.59039758 -0.13486209 6. 10526 10680.392768 -154.3927685 0.0247893211 -0.338260917 -0.053930558 10387 10519.725593 -132.7255935 0.0287913306 -0.291316918 -0.05015801 10592 10367.641131 224.35886877 0.0626537807 0.5019535902 0.1297738503 10460 10566.32604 -106.3260395 0.0403612504 -0.234717477 -0.048136426 10 10086 10232.796663 -146.7966633 0.0638701019 -0.328244987 -0.085739086 11 14628 13214.015064 1413.9849358 0.0660538381 3.4645251575 0.9213652471 12 13396 12563.017765 832.98223507 0.0569040646 1.9095424681 0.4690542002 13 11726 11963.551915 -237.5519147 0.0807474376 -0.536836461 -0.159106878 14 11252 11310.001708 -58.00170759 0.0457271404 -0.12835846 -0.028097989 15 12449 12087.066769 361.93323148 0.0979474326 0.8284045229 0.2729751205 16 11030 11313.295597 -283.2955965 0.0625950725 -0.63458402 -0.163981777 17 10787 11134.121831 -347.1218314 0.0530684 167 -0.774899499 -0.183444267 18 10603 10982.776871 -379.7768706 0.0603576289 -0.851959048 -0.215925289 19 10144 10331.066242 -187.0662422 0.0480282163 -0.415017328 -0.093218567 20 11674 11509.933417 164.06658345 0.0509387171 0.3644294875 0.0844287547 21 11510 11025.244807 484.75519289 0.0994655353 1.115553658 0.3707461139 22 10946 10780.076994 165.92300575 0.0278163559 0.3641431984 0.0615953836 23 10508 10705.096399 -197.0963995 0.0352719536 -0.434428773 -0.083067452 24 10604 10896.236335 -292.2363354 0.0513484991 -0.650831939 -0.151418655 25 10270 10345.90378 -75.90377957 0.0360856225 -0.167149718 -0.032341006nd