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The empirical rule works fine as long as the distribution is approximately normal. But what do you do if the shape of the distribution is unknown or distinctly nonnormal (as, say, skewed strongly to the right) Remember that the empirical rule told you that, in a normal distribution, approximately 68% of the data are within one standard deviation of the mean, approximately 95% are within two standard deviations, and approximately 99.7% are within three standard deviations. Chebyshev s rule isn t as strong as the empirical rule, but it does provide information about the percent of terms contained in an interval about the mean for any distribution. Let k be a number of standard deviations. Then, according to Chebyshev s rule, for k > 1, 1 at least 1 2 % of the data lie within k standard deviations of the mean. For example, k 1 if k = 2.5, then Chebyshev s rule says that at least 1 2 % = 84% of the data lie with 2.5 2.5 standard deviations of the mean. If k = 3, note the difference between the empirical rule and Chebyshev s rule. The empirical rule says that approximately 99.7% of the data are within 1 three standard deviations of x . Chebyshev s says that at least 1 2 % 89% of the data 3 are within three standard deviations of x . This also illustrates what was said in the previous paragraph about the empirical rule being stronger than Chebyshev s. Note that, if at least 1 1 2 % of the data are within k standard deviations of x , it follows (algebraically) that k at most 1 % lie more than k standard deviations from x . k2

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How does refactoring model methods help in improving performance 4 discussed the fat model, skinny controller approach, which proposes that business logic should be located within models, rather than controllers, wherever possible. In addition to various other benefits, following this approach can improve performance when it is used in combination with caching. As shown earlier in the section Caching Query Results, you can use caching to block the method calls on the model altogether if cached result data already exists, thereby reducing the number of lines of code that need to be executed. And fewer lines of code automatically translates to faster script parsing and execution.

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Exercise 4.15 For the system of eqn. (4.82) with a position measurement y = x: 1. Complete an observability analysis to show that the state of the system is not observable. 2. Analyze the observable and unobservable subspaces to determine the linear combination of states that is not observable. Exercise 4.16 Eqn. (4.125) is derived as in Section 4.6.5. Use similar methods to derive eqns (4.126) and (4.127). Exercise 4.17 Implement a covariance simulation to reproduce the results of Section 4.9.4. 1. Compute and Qd for T = 0.1s. 2. Implement eqn. (4.125) at a 10 Hz update rate. This step will allow the plot of the error standard deviation to show the error growth between measurement updates. 3. For the given value of L, implement eqn. (4.127) at a 1.0 Hz rate. At the measurement instant, store the two values for the covariance. First store the prior value from eqn. (4.125), then store the posterior value from eqn. (4.127). After duplicating the results from Section 4.9.4: 1. Try alternative initial values for the error covariance matrix P. Do they converge to the same steady state conditions 2. Experiment with di erent (stabilizing) gain vectors L to try to improve the state estimation accuracy. Exercise 4.18 Verify that eqn. (4.144) is the solution of eqn. (4.100). 1. Di erentiate P from eqn. (4.144), noting that = F . 2. Add and subtract the term Q to the result of Step 1, then organize the terms that match eqn. (4.100) together. The remaining terms should sum to zero.

Assuming that the speeds are approximately normally distributed, how many degrees of freedom will there be in the appropriate t-test used to determine which type of tennis ball travels faster a. b. c. d. e. 6 7 16 15 14

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Value 1,000 pF /.001 F 1,500 pF /.0015 F 2,000 pF /.002 F 2,200 pF /.0022 F 4,700 pF /.0047 F 5,000 pF /.005 F 5,600 pF /.0056 F 6,800 pF /.0068 F .01 .015 .02 .022 .033

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Dimension styles provide an easy method of managing the appearance of dimensions. This saves time and effort and helps you produce professional drawings. Note that drafting standards vary from industry to industry and even among companies in the same industry. 1. Display the Dimension toolbar (Right-click on any docked toolbar and select Dimension).

The mass of the sample is calculated from the differences between masses 1 and 2. The volume added is calculated by taking the difference between measurement 3 and either measurement 4 or 5. A plot of pH versus the volume added is made. This graph or the difference between measurements 3 and 5 gives the volume of titrant. The volume of titrant is converted to liters. The mass of the sample is converted to moles by using the molar mass. The moles of titrant may be calculated from a consideration of the moles of sample and the balanced chemical equation. The moles of titrant divided by the liters of solution gives the molarity of the solution.

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