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the clients changed their banking instructions Trina told them to. She was, after all, a credit manager at Corydon Corporation so, why wouldn t they believe her The targeted clients must have assumed that the online communication, sent from her Corydon e-mail account, represented the business decisions and processes of Corydon. The e-mails she sent were short and to the point. Both Powell and Axis had normally remitted their funds via physical check; however, because she had established relationships with their accounts payable supervisors, Trina had no trouble getting them to switch to wire transfers temporarily. She just asked: Any chance of getting a wire transfer on the old invoices When the client said yes, Trina followed up with: You are wonderful and to show my appreciation, I would like to take you for lunch whenever your schedule permits. Due to the delay in payment, the only two invoices I would like sent by wire transfer are the oldest from January and February. Attached are the wire instructions that I need these funds sent to. The wire transfer form that Trina had attached was a simple Word document with only basic information:
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Height Error (RTK RAW+HA) 25 20 15 Error (metres) 10 5 RAW_4SVs
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2 tsp (2 ml) ground cardamom 3 cup (75 ml) light coconut milk 2 tsp (2 ml) sugar 8 tsp (0.5ml) salt 8 tsp (0.5 ml) pepper 4 cup (175 ml) frozen peas
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In almost all situations, we are making statements about populations using data collected from samples. For example, a factory producing packets of sweets believes that there are more than 200 sweets in each packet. To determine a reasonably accurate assessment, it is not necessary to examine every packet produced. Instead an unbiased random sample from this total population could be used. If this process of selecting a random sample was repeated a number of times, the means from each sample would be different. Different samples will contain different observations and so it is not surprising that the results will change. This is referred to as sampling error. If we were to generate many random samples, we might expect that most of the samples would have an average close to the actual mean. We might also expect that there would be a few samples with averages further away from the mean. In fact, the distribution of the mean values follows a normal distribution for sample sizes greater than 30. We will refer to this distribution as the sampling distribution, as shown in Figure 5.7. The sampling distribution is normally distributed because of the central limit theorem, which is discussed in the further readings section of the chapter. In fact, the
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It s easy to imagine them thinking This ll x him as they installed a new batch of high-security cabinets tted with the Mosler Safe Company s combination locks. This time Feynman couldn t come up with an instant solution. The locks required three numbers to be entered in sequence. There was nothing to pick, no way to hear the tumblers fall. But by experimenting with the locks, Feynman soon discovered that the combinations were less fearsome than they seemed. To enter the number 65, anything between 63 and 67 would do, reducing the possibilities on each turn of the dial to 20. This, reckoned Feynman, would make it possible to get the cabinet open with around four hours mechanical effort. But that was too much like hard work. It was a chance discovery that got Feynman past the locks. When a cabinet was open, he found that it was possible to read off the last two numbers of the combination. The lock bolt would twitch as he turned the dial past the crucial positions. Before long he could pin down the numbers by touch with his back to the cabinet, just glancing at the dial to note the result. At every possible opportunity he noted the second and third numbers of open cabinets. Then, when he needed to get past the lock, he only had to try the 20 possible rst numbers. The Mosler Safe Company was beaten. Once Feynman had a puzzle to solve, he would worry at it like a dog, coming at it from different angles, never letting up until it had given way. It was the same part of his character that made him such a great physicist. It was lucky
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