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he projects presented so far have all been based on having the new brains of Roomba located remotely to it. From an RS-232 serial tether to a Wi-Fi connection, in all cases, Roomba is more of a telepresence device than a true robot. The term robot has come to have a flexible definition with the rising popularity of various telepresence/remote-control devices. The most egregious use of robot to mean remote-controlled machine are the battle bot programs on television. The contestants are billed as robots, but they re all controlled by humans through radio-control transmitters. Perhaps in a few years we ll have true robot battle shows where the machines are programmed, given a strategy, and let loose to fend for themselves. This chapter shows a few ways to sever the connection with a controlling computer and instead replace the microcontroller brain of Roomba with one you can program. You ll turn your Roomba into a truly autonomous reprogrammable robot. Like all projects in this book, this chapter doesn t harm Roomba and its original brain is available when you remove the new one you create. In theory you could reprogram the microcontroller chip inside the Roomba, as that s what the OSMO//hacker device does to upgrade a Roomba s firmware. iRobot has yet to release specifications on the protocol used to upgrade firmware. They likely never will. Allowing anyone to re-program Roomba is a sure way to brick it, resulting in unhappy customers. There has been some effort in reverse engineering the upgrade protocol, but with the release of the ROI it s become less of an issue, since it addresses most of what was wanted once you add your own microcontroller that speaks the ROI protocol.

Normality of Individual Variables (or the Residuals) Several statistical and graphical methods are available to assess the normality of raw scores in ungrouped data or the normality of residuals of analysis The next section contains guidelines for normality in grouped data Recall that normal distributions are symmetrical about the mean with a well-de ned shape and height Mean, median, and mode are the same, and the percentages of cases between the mean and various standard deviation units from the mean are known For this reason, you can rescale a normally distributed continuous variable to a z score (with mean 0 and standard deviation 1) and look up the probability that corresponds to a particular range of raw scores in a table with a title such as standard normal deviates or areas under the normal curve.

Private Function WorkbookIsOpen(wbname) As Boolean Returns TRUE if the workbook is open Dim x As Workbook On Error Resume Next Set x = Workbooks(wbname) If Err = 0 Then WorkbookIsOpen = True _ Else WorkbookIsOpen = False End Function

would engender in others although this certainly counts for something but also from a basic desire to act consistently with one s point of view A commitment that is expressed publicly, whether in front of a crowd or to a single individual, is especially effective in locking in a person s opinion or promise, making it resistant to change despite the availability of good reasons for reconsideration (cf Deutsch & Gerard, 1955; Schlenker, 1980) Agents of in uence play on this seemingly noble tendency, often for decidedly nonnoble purposes of their own Several speci c techniques have been observed in real-world settings and con rmed in research (Cialdini & Trost, 1998) Perhaps the best-known tactic is referred to as the foot-in-thedoor, which is essentially the mirror image of the door-in-theface tactic.

The legitimacy of using the z-score transformation and its associated probabilities depends on the normality of the distribution of the continuous variable Although it is tempting to conclude that most inferential statistics are robust to violations of normality, that conclusion is not warranted Bradley (1982) reported that statistical inference becomes less robust as distributions depart from normality and rapidly so under many conditions And even with a purely descriptive study, normality of variables (as well as pair-wise linearity and homoscedasticity, discussed in the section titled Multivariate Assumptions ) enhances the analysis, particularly when individual variables are nonnormal to varying degrees and in varying directions Skewness and kurtosis are statistics for assessing the symmetry (skewness) and peakedness (kurtosis) of a distribution.

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