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The products on the shelves were of uneven quality, and Walgreen himself could honestly not vouch for all of them This was the store where Walgreen worked, an almost perfect example of what harmacy was like at the time, for better or for worse But it didn t have much to do with the store Walgreen would create That would be something altogether different What probably looked like a step backward at the time would prove to be one of Walgreen s smartest career moves Bit by bit, Walgreen was getting the feel for what it would be like to actually own and operate a store, taking on larger responsibilities at each stop Before Isaac Blood went on vacation, he asked Walgreen to sell his (Blood s) second, smaller store if he could When two men offered $1,250 for the shop, Walgreen knew Blood would accept the offer But Walgreen was a shrewder negotiator than his boss and told the men that the offer was too low They called Walgreen s bluff, asking him to wire his boss their offer in the belief that Blood would happily sell at that price Walgreen believed they were right, so he outfoxed them again He wired Blood their offer but added a kicker the buyers never saw: If you want to sell store for $1,250, wire [back that] you won t take a cent less than $1,500 Blood did as his underling instructed; and sure enough, the buyers blinked first Blood sold his unwanted second store for $1,500 20 percent more than what he had asked Despite Walgreen s key role in the transaction, Isaac Blood didn t give him a cent for his help By this point, Walgreen had seen enough pharmacists to know what worked and what didn t He now believed he could do it as well or better than most, and he was certain he d much rather work for himself than for someone else But when he approached Mr Blood about buying the Cottage Grove store, Blood replied that he wouldn t take a cent less than $4,000 Obviously, Blood had learned Walgreen s lessons on negotiations too well, presenting Walgreen a seemingly impossible standard to reach As always, Walgreen saved his best efforts for any achievement that might grant him more independence The only way he could ever pay off such a steep loan, he figured, would be to make the store more profitable.

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Be Careful When You Type Type all code, commands, and file names exactly as shown The compiler and interpreter arecase sensitive , so you must capitalize consistently In other words, HelloWorldApp is "This not equivalent to helloworldapp it has been available on the web and read by thousands of book stands above the rest because Java programmers The authors have received an enormous amount of feedback about which sections are good and which sections are confusing; the confusing ones have been improved I doubt that any other Java book has undergone such trial by fire uot; -Metroplex Java User Group, wwwjavamugorg/reviews/ Second, save this code to a file From the menu bar, select File > Save As In the SaveAs dialog, do the following Whether you're taking a class or learning on the job, The Java(TM) Tutorial, Third Edition , is a handson guide that lets you quickly become proficient with the Java programming language Written by members of the Java Software team at Sun Microsystems, the book uses an interactive approach to Using the Savein drop-down example help you learn the Java platform bymenu, specify the folder (directory) where you'll save your file In this example, the folder is java on the C drive Since its first online release in 1995, the material in The Java(TM) Tutorial has been updated In the to reflect text box, type "HelloWorldAppjava" Java platform This third edition continuouslyFilename reader feedback and new releases of the , including the double quotation has marks The quotation cover force the file to Platform, Standard Edition, as than as a "txt" been thoroughly updated to marks v13 of the Java be saved as a java file ratherwell as preceding text file versions as early as JDK 11 From clear explanations drop-down menu, choose Text classes, and You will findthe Save as type of such fundamentals as objects,Document data structures In addition, the book provides introductions to object-oriented programming, applet construction, and When you're finished, Other topics include exceptions, I/O, and threads To help beginners avoid many user interface design the dialog box should look like Figure 5 Now click Save and exit NotePad common mistakes, an entire chapter is devoted to programming problems and their solutions Figure 5 Saving the HelloWorldAppjava file to this edition Also new extension Convenient summaries at the end of each section are new with the correct java for this edition are "Questions and Exercises" sections to help you practice what you learn.

A last diagnostic possibility is to make use of Schruben s coverage function The algorithm can be described as follows, as hinted in the description of the coverage function Befored starting to run the (real) simulation, consider smaller sample sizes n and k values of , the rarity parameter, { j ; 1 j k} with 1 > > k For each value j , m independent blocks of data X = (Xi ( ))1 i n are then used, giving independent realizations of From those m realizations, the empirical distribution of can be obtained and compared with the uniform distribution Then one can see if there is a trend: if the empirical distribution gets farther from the uniform as j decreases, the current estimator can be considered as non-robust (unreliable), and a better one should be chosen Otherwise, the estimator is not rejected by the test An important remark is that, in order to apply this diagnostic, the exact value (or at least an equivalent as 0) has to be known for computing As a consequence, the diagnostic can only be used for small instances of the problem For example, when estimating the probability in an M/M/1 queue that the occupancy exceeds a value B (with B large), the exact value can be estimated for smaller values of B, and a trend can be derived The same applies when dealing with a Markov chain on a small state space, but looking at long simulation times T (such as in Example 5 above), by looking at smaller values of T The case of large Markov chains where rarity comes from rare transitions is more dif cult But one can try to construct a smaller instance of the model, with similar topology or properties (we do not care about the result being the same) and for which the exact value is known, and look to see whether the coverage function does not deviate as critical transition probabilities decrease Our three examples describe those three situations and are detailed now Figure 47 displays the coverage function for the M/M/1 queue, looking at the probability that B is reached before returning to 0 This is done for sample sizes n = 1000 and repeated k = 500 times in order to get the empricial distribution function (smoothed thanks to interpolation) In the numerical experiments, p = 03 and we chose p = 05 (not the optimal value, but to illustrate the behavior) It can be seen that as B increases, the coverage function gets worse and worse, so the estimator is not good here Look now at the case of the 2 2 matrix of Example 5, with transition matrices 02 08 05 05 P = , P = 02 08 05 05 Again, we take n = 1000 and k = 500 From Figure 48, it can be checked that as the length K of the simulation path increases, the coverage function gets worse and worse, illustrating the bad estimation We close our numerical illustrations with Example 1 Figure 9 displays the empirical coverage function for different values of , still with n = 1000 and k = 500 Again, as decreases, the coverage function gets farther from the uniform, denoting an undesirable behavior.

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