he second phase of Exercise 14.4, the analyst 243 measured the elapsed complexit
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he second phase of Exercise 14.4, the analyst 243 measured the elapsed complexity was function of the complexity of the query. The comle time as a fno ured by the number of keywords in the query. The number of d ad operations were also measured, as shown sk- in Table 14.5. For this regression models to predict the elapsed time as a function of number of keys and interpret the results. TABLE 14.5 Database Performance as a Function of Number of Keywords Number of Keywords Elapsed Time Number of Disk Reads 0.75 0.70 0.80 1.28 1.60 4 78 92 16 46 Prepare e number of keys for the data presented in Table 14.5. verify the regression assumptions TABLE 14.6 Measured EncryptionTmes tor a regression model to predict the number of disk IO's as a func tion of th The time to encrypt a k-byte reco hown in Table 14.6. Fit a linear regression model to tuia e record using an encryption technique is tests to Various Record Sizes OhservationsExplanation / Answer
here model is given as y=0.074+0.009*x ( y=elapsed time and x=number of keys )
here the confidence interval for coefficient of slope(x)=(0.042, 0.084) doesnot contain the slope=0.009, so it is significant. it means number of keys is well explaining the elapsed time. since slope=0.009, this imply that there is change in elapsed time by amount 0.009 with per unit change in number of keys.it means if we increase one key the elapsed time will be increased by 0.009.
here R2=0.943, it means the above model is explaining 94.3% of total variation in the data and it is quite large and desirable.
but from the given data the model is estimated using ms-excel is y=0.635+0.0631*x
and interpretation can be given as explained above.
the anaysis showed following results using ms-excel
y x 0.75 1 0.7 2 0.8 4 1.28 8 1.6 16Related Questions
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