3.0 Using DINDE. DINDE is meant to be helpful to both the statistician doing research in data analysis and the statistician engaged in an actual analysis. Here, we discuss the latter situation: how a statistician is expected to use DINDE in a practical setting. Although we concentrate mainly on the visual screen and mouse interface to DINDE, it is important to remember that all the operations we describe can be accomplished in a Lisp program by calling functions or sending messages. By this mechanism the user can pursue analyses beyond those provided in the existing menu arrangements. The basic idea to keep in mind is that, with DINDE, an analysis consists of creating, examining, and performing operations on DINDE objects. A DINDE object represents either an element or stage in a typical analysis; each object encapsulates some information generally considered to be relevant for that item or stage. (For example, a data vector or a scatter plot would be an element, while consideration of the regression of one variable on another, or the exploration of the structure of a two-way table, would be a stage.) As such, these objects are meant to constitute building blocks of the analysis. Each DINDE object is created from a template called its class. Classes are organized hierarchically, that is, a class may have one or more parent classes, each of which is itself a class. DINDE gathers all these classes together and displays their hierarchy as an inheritance network in a window called the toolbox. Moving left to right in this display follows the inheritance from parent to child. (Section 4.1 displays several branches of the inheritance network which can be seen in the toolbox.) DINDE objects are instances of DINDE classes and may be created by selecting the class from the toolbox (i.e. by pointing at a label on the network), clicking a mouse key, and perhaps identifying components to be incorporated in the new object. Each class represented in the toolbox can briefly describe its intended use and the user has access to this toolbox at all times. At the time a DINDE object is created, a visual symbol representing that object is placed in another window called an analysis map. There it can be examined in detail and directed to perform operations peculiar to its own kind. An object can be given a unique name and can have user supplied commentary (editable text) attached to it. DINDE objects often know how to spawn additional objects of like and unlike classes (e.g. the LOG transformation of an Array object delivers a new Array; the BivariateRegression object can produce specialized scatter plots) › our notion of software guidance rests largely on this ability. The analysis proceeds by creating more objects, examining them, and linking them together. At any time the current state of the analysis is visible in the analysis map as a collection of statistically interesting objects some or all of which are linked together in one or more networks. Before examining the details of the toolbox and the analysis maps (Sections 4.1 and 4.2), it may be best to consider a small example of their use. Assume that the user wants to fit a bivariate regression of Y on X. For brevity, also assume that the user has created two FloatVectors (e.g. using the function C › as in the S language, C concatenates small objects like numbers into a vector object ) and has named them BrainWts and BodyWts. The data we will use in this example represent the average brain and body weights, respectively in grams and kilograms, of 62 mammals (Weisberg (1980, pp. 128--129)). These two objects appear as named FloatVectors of 62 elements in an Analysis Map window as shown below. (This map, and subsequent displays are actual screen images › "bitmap" objects which have been copied directly into this document.) LÌy6ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÏÿÿÏÿÿ?ÿÏÿããÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÏÿÿÏÿÿ?ÿÏÿããÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ‡ÿÿ‡ÿÿ?ÿÿÿáÃÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ‡ÿ‡‡'0Ì?áÃÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ·9ÿ·93'&IŸä’fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ39ÿ39ó3#Èÿä“æsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ9ÿ9ƒ2pÌ?ä“sÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ39ÿ393:|Oæ2fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþy9þy938fIŸæ2fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþy9þy9‰<ðÌ?çsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿüÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿäÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿñÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ`Ä`ì`<<`À`Œý€Ç‡Œ€Äf`ff0`ŒÀÁ€cŒÌÆž>ÆnÏžñÇÌÇ>yð`0oœoÙóÀÁž8ù˜ñçÏ>ŒÆ3fÆnÆ2Í˜cÌÙ˜Í°|Í†lÍØÆ@ù³a™›3¶ƒ³fljÆ8 ™ãØÍ€fllÍXÇÁ³<`‘û°Ã³f(;† ›cŒÍ€f0l6lÇpÁÀÁ³l`ñƒ°Æ³f8;†& ›cÙ˜Í€f`l6lÇpÄÀÁ³l`a›3°Ì>¼ññÃy€<~ÌlÂ w€Áž>8`ñáÏ0Ã°0€`0€`Àÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ All DINDE windows are sensitive to mouse operations. In general, mousing in the title region of the window produces a menu whose entries offer operations relevant to the entire collection of objects displayed, while mousing on a particular object in the window produces menus allowing interaction with that object. There are a common set of housekeeping functions for all windows, and each DINDE window can briefly describe the way it may be used. Data analysis using DINDE consists of building a network whose nodes are DINDE objects (for more detailed discussion of these networks and their display, see Oldford and Peters (1986b)). We begin the example analysis by moving the mouse to the title region of the map above and pressing the middle mouse button. This produces the following menu (arrows indicate that a further menu may be obtained by sliding the mouse to the right), ff`bÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ@@€HÄ€€@HHÄ€@@HÄŽŽ.\8ˆâ"E(‘‘1bD‰"E(Ÿ!B| ñTE(!B@ TB‘‘!BD ˆBŽŽ!B8àˆ@@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿûÿÿÿÿÿÿÿÿÿïÿÿÿÿÿ~ÿßßÿÿÿÿýûÿûÿãûÿÿßÿïÿÿÿûÿ~ÿßßÿÿÿÿýÿÿûÿïûÿÿßÿÿÿÿÿûÿ}|\_Ñã»½ÛG‹ðÃõèñÛ¸î?G‡‹}{›ŸwÎÝ»½»;sïoõçnÛ·mß;{sw{»ÛßÇÞÁÕ}{{{ïoîïxÝxî?{{{ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø;Ûß7ÞßÕ|»{{ïoàïfÝoß{{{ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ÷Û›ŸwÞÝîýÛ{sïoßonÞ÷mß{{swÿÿÿÿÿÿÿ÷ÿÿÿÿÿÿÿÿ÷÷Ü\_‹Þãîýë{‹ðïßoq^øî?{‡‹w;ÿû¿ÿï·ÿÿÿÿÿÿÿÿûÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿw;ÿÿû¿ÿï¿ÿÿÿÿÿÿÿÿõÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿùÿÿÿÿÿÿÿw;qqÑð£Ç÷wÝÿÿÿÿÿÿúÀz×nnÎû»÷víÝÿÿÿÿÿÿõ@€z×o`Þû½ƒúö«ÿÿÿÿÿÿúÁ € €z×ooÞû½¿úöþ«ÿÿÿÿÿÿõÁ }ïnnÞû½»ýöïwÿÿÿÿÿÿûÂƒ£ ³‡—ƒÏž-fˆŽ$#ž}ïqqÞü½Çý÷wÿÿÿÿÿÿ÷Â„d`ÔH’$‚!5™H‘$$H˜€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÄD$ ‡Ç’$‚!!Å$!È€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÇÄ$ „’$‚!!$&H€@H$d`„H‘’$‚!!B$dH€@ H#£ ƒ‡‘ƒÄB!‚££¦€H A @L A @J#‹Yâ"=pà"#ˆˆÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿI$Mj"‰"$Hˆ I!ÈBT ð'ÅP H¦HBT $P HdHBˆ $B H#¨Aàˆ à#‚ @ @ @ @ H A L A! J#‡<áÐ"#ˆˆ@—,ç£ÅÃ… I$H‘0"$Hˆ@˜‘5$&$J I'Çò'ÅP@Ÿ r$$#… H¤‘$P@!’$$ J HdH‘0$B !‘!$$$D H#‡áÐ#‚ é£Ä#ˆ @ @ @ @ H H! H q@—,ç£ÅÃ€ H ‰ @˜‘5$&$@ H @@Ÿ r$$#€ H @!’$$ @ H !‘!$$$@ OÉ é£Ä#€ @ @ @ @ OÀ H H#‡bâœ H$H™“" OÇÈ‘> H$‘ H$H‘2" H#‡áÑœ @ @ @ @ GÀ € H € HˆŽ$#€ G„H‘$$@ @AÅ $'À @&E $$ H$B$d@ GÃ¢££€ @ @ ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿà from which we choose to widen the view of this DINDE window by selecting the item "Add a new kind of Analysis node". DINDE prompts for the kind of node to be added (i.e. asks its class), which the user then selects from the toolbox. Suppose that the user selects the BivariateRegression class. Each instance of BivariateRegression incorporates a Y vector and an X vector. As part of the initialization for the new BivariateRegression object, the user is asked to identify a Y and an X. The user will typically respond by mousing the appropriate vectors already visible in the map, here designating BrainWts and BodyWts as Y and X respectively. At this point, the user is offered the opportunity to connect the newly created BivariateRegression object to existing nodes in the map (including none). In this example, we attach it to both existing vectors resulting in the following map. LÌw|?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ŸÿÿŸÿþÿŸÿÇÇÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ŸÿÿŸÿþÿŸÿÇÇÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿþÿÿÿÃ‡ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿNa˜Ã†ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?nsÿnrfNL“?É$Ìçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü>fsþfsæfG‘ÿÉ'Ìçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü>sþsdá˜É&çÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü>fsþfrftøž?ÌdÌçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü<òsüòrfpÌ“?ÌdÌçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü<òsüòsyá˜Îæ$ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿùÿÿÿÿÿüÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÉÿÿÿÿÿüÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿãÿÿÿÿÿüÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü000000000000000À0>ˆÀØ0ÀxxÀ€À1û03ˆÌ0À0ÌÌ`À1€ƒÆ™Œ03<}ŒÝŸ<ã™|óàÀ`ß8ß³çƒfÍŒÝŒd›0Æ™³1›`0ø0›Ù›±ŒófÃ36f3l03fÌØÕŒp3Æ °1›0Ì00Ø<Ùš±ŽƒfxÁ#ö3`†03fÌPw6Æ01›0Ì`0ØlÙŽáƒƒfØÁã3`Œ03fÌpwL6Æ³1›0ÌÀ0ØlÙŽá‰ƒfØÀÃ6f3`˜0><| "xãã†ó0xü0˜>Ù„@ïƒ<|pÁãÃž`†0``0À`0À€00000à0 0 à0@<0@À0@|0€€0€x0€€0ð00ð00à0>0À0<0À0x0€0ø00 ð0 0@à0@>0@À0€<0€À0€x0€0ø00ð00à00à0<0À0|0€0x0 €0 ð0O00000000À0˜>À0À30ÛÇÙÇÏ3<}óÇžÏ>0›fØc³fÍ¶l²Ù³0Ù±æã¾~Í‡î8Ù³0Ù³fc3`Í†ŽÙ³0Øãfc³fÍ†i¦Ù³0˜áöñÏ3<}ƒÏ<Ï300Ì0x00000?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü Here we might give the BivariateRegression a unique name and perhaps add some notes to it, possibly explaining why we are considering a bivariate regression analysis at all. Selecting the BivariateRegression with the middle mouse button depressed produces the following menu of items. ffÿ/ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿû÷ÿÿÿÿÿÿÿýÿÿÿÿÿÿÿÿÿÿÿÿÿø|;ÿÿÿÿÿÿûÿÿÿîÿÿ÷þð{Ûÿÿÿÿÿÿÿÿÿÿîÿÿ÷ýpwû?Ž:¦xÂ± @wûvíßvvíÛvï™·nvöæíQ @ˆwûÇ?Žö ûvÿ»°nöö÷±.\8sÇ®G<áHwû7ïßööýûvÿ»·îöö÷íq1"bD‰1"È‘¨{ÛvíßvvíÛvï»·nööæîñ!B|!>CqH|;‹?Ž;w»¸ò÷ñ!B@! L‘’ˆÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿñ!"bD‰!"È‘ÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿñ!\8pÁ¡GLê@@@@CÂ D" @H®XY‡=pðèàòD@ƒD H1"hfH‘‰ DƒD ˆH!>@DO‘ àÿK‡AEõÃÃ£HH! @DH âDLHÁE"F$$dB¨D"!"@DH‘ DHHA)>D$$#HCÁ¡@DG ðèàDHHA) D$$ Bˆ@DHHÁ"D$$dA@3HGA4#Ã£‚ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð@€ÿÿÿÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿ¿ÿÿÿÿýøÿÿÿÿû¿ÿÿÿÿþø÷ÿÿÿÿûÿÿÿÿÿýxþ÷0¸txÿÿþ¸öææíÛ·³·ÿýXîööö;··¸ÿÿþ¸ööööÿÛ··¿ÿýxöæææíÛ···ÿþø<¸w¸ÿÿýøÿþö÷ÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿø The leftmost stack of boxes provide access to two different collections of operations. "Class specific methods" represent those that are defined specifically for the class of BivariateRegression objects. "Other methods" are those which the BivariateRegression class has inherited from the classes that are its parents. We expect the user to examine "Class specific methods" first and thereby reveal the operations suited specially to the local context. Section 4.1, on the toolbox, discusses inheritance issues further. Sliding over the "Class specific methods" yields a menu where, given that we are interested in performing a bivariate regression, the next steps typically taken in the analysis are organized. Selecting Suggestions from this menu will produce some general suggestions about how one might perform a regression analysis. In this case, the suggestion is to visually inspect the data before doing anything else. More detailed suggestions such as how to inspect the data, what fitting methods might be used, and so on, are found by sliding the mouse over the Suggestions to get a more detailed menu. Note that these suggestions are data-independent: they remain the same regardless of the values attached to Y or X. (Data-dependent methods are possible as well, and are an open research question. See Section 4.3 for further discussion.) To inspect the data, the mouse is slid over "Inspect the data" to reveal the extended menu, ff[Hÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ¿ÿÿÿÿÿÿÿÿÿÿþßÿÿÿÿß»ÿÿ÷ýÿßÿÿÿÿÿÿÿÿÿÿþßÿÿÿÿß»ÿÿ÷ýÿ¯ÿÿÿÿÿÿÿÿÿÿþÚ÷ñ×¿ãÆŽ—·?ÿþÛß··ïß»ÛþömÍ¯ÃÝ»wvW¶öÿÿþÛÛ³·nß»Û¾æíÝßýßãwðvöÿÿþÛÜtxñçËÜb¿þß›w~÷ööÿÿþÿÿ÷ÿÿÿÿÿÿÿÿÿÿ¾Ý»wv÷ööÿÿþÿÿ÷ÿÿÿÿÿÿÿÿÿÿÁãÅ™Ž÷÷?ÿþÿÿÿÿÿÿÿÿÿÿþ@Cÿÿÿÿÿÿÿÿÿÿþ~Hˆ0`€"@€0`€R ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ@_épè(£ž¸xtp*A xtÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ|H‰‰(¤HÄ„ŒˆVA„B"ÿÿÿÿÿÿÿÿ~ÿÿÿÿÿÿÿÿ¾@H‰ %'È„„„p*A#žxt³‹1ò"‡ÿÿÿÿÿÿbÿÿÿýßÿþÿÞ@H‰ %$„„„R$H„ŒÔLÉAB{ÿÿÿÿÿÿïÿÿÿýßÿþÿ®@H‰ "$H„„Œˆ"A#ˆ„„È‰@‚ÿqãÇñÑãÇBãôÏQáâãÖ@Fiè"#†„xtpBA H„„†H‰@‚ÿnÝ»îÎÝ»nÝó6íÎÞÜÝªA$H„Œ„Hˆ„@‚ÿxãÇñÞÁ¿nß÷v ÞÞÞãÖðA#†xtƒ¨ˆx@‚ÿfýûþÞß¿nß÷výÞÞÞý®„{nÝ»îÎÝ»nÝ÷víÞÞÜÝÞBx‡qcÇñÑãÇnã÷w^áâã¾>ˆ"ÿÿÿÿÿßÿÿÿÿÿÿÿÿÿÿÿþA€R ÿÿÿÿÿßÿÿÿÿÿÿÿÿÿÿÿþ@!9èñpà*A xt8‰ à*A#žxt³‹1ñBxD !!! ‰ R$H„ŒÔLÉ@‚„D A###"D‰ "A#ˆ„„È‰@‚ õÃ‹0ç®>8hñàBA H„„†H‰AB F$MÉ1!#"!!A$H„Œ„Hˆ„B" D'È‰ò!!!A#†xtƒ¨ˆxD D$‰!!!ÿÿÿÿÿÿÿÿÿÿÿÿþ„„D$H‰!!#"xx4#ˆˆá¡~|@<:ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿAB@B!A"BGžy~!BHH A!|HH @A!@HH @A!@HHB @~"@G†< @~|@<:AB@B!A"BGžx ~!BHH @A!|HH @A!@HH A!@HHB!~"@G†<"ÿÿÿÿÿÿÿÿÿÿþ from which the ScatterPlot item is selected. The user is requested to use the mouse to size a window region for the plot and the scatterplot below is produced. LÌ7?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø0000000000000000?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<þ“Îü÷’yàç¼ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<äüçÏç‘Ìóü÷’99Îg¼ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<äùóÏç‘Éóþc29<Ÿ3ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<äùóÏçÉÿþc2<Ÿ3ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<äùóÏçÉÿþc2<Ÿ3ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<ùóÏç’IÃÿArI<Ÿ:ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<üùóÏç’IóÿrI<Ÿ8Cÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<üùóÏç“ óÿra<Ÿ8ãÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<üüçÏç“ãÿœòa9Î|çÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø<üÏç“Žÿœòqàüçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø00000000000000~ð€0 €0 €0€ppñtŽ0€„# „D0€ „#ùðD0 „"D0„! D0~F0ñ†0À000€0€0€0000ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü0€0€0000€0€0€0€000€0€0€000000000000000000000000000000>0000€0€0€0000À000€0€0€0000ÿü0€0€0000€0€0À€0 0 0 0 €0@ 000 0à0000800€0`0€00800€0`0€008000à000000à0007à00€0€0 €0À0 0 0 À0 0@0€0€0€000à0ÿü0€0À€0 0" 0" 0" €0" €0" €0" 0?à0€0€0€0€0 €0000€0€0à0€0€000000000000000000>0>000€0€0ƒà 0‚€ 0ï€ø0Ï€ 0Â 0ò0@0@0Š@@0€ª@@0€ÿ€@@0€ú@@0ê@@0à@@0À@@0Áÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü0@@0@@0€@@0€@@0€@@0@@0@@000000000p8qÇ€Ž8àpqÇqÀàóŽ8qÇ€çp80ˆD8Š(„@QEˆŠ(¢"Š!QD""Š(„@H¢ˆD0ˆDŠ(„@QEˆ2(¢"Š!äQD"<Š(„@ˆ¢ˆD0ˆDŠ(„@‘Eˆ (¢ "Š!QD""Š(„@¢ˆD0ˆDŠ(„@EˆŠ(¢¢Š!QD""Š(„@¢ˆD0r8qÇ#€Î8äpqÇŽqÈàãŽ9qÇ#€r80 € 00000000þ @0 D0 D0‡¤"(Ž<0ÿHd"(„B0€H"AE<0€H"AE0€Ha€‚B0ÿ‡¡€‚<000000000000?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø DINDE then attaches a ScatterPlot node to the BivariateRegression in the analysis map. LÌyíÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿçÿÿçÿÿŸÿçÿññÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿçÿÿçÿÿŸÿçÿññÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÃÿÿÃÿÿŸÿÿÿðáÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÃÿÃÃ“˜fðá‡ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÛœÿÛœ™““$ÏòI39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ™œÿ™œù™‘äòIó9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿœÿœÁ™8fòIƒ9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ™œÿ™œ™>'ó39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ<œÿ<œ™œ3$Ïó39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ<œÿ<œÄžxfó¹‰ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿòÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ0€b0ö00à0F~À†cÃÆÀb3Œ0330F``À1†€ÆfcÏc7gÏxãæcÇŸ<ø07Î7Æìùà`Ï|ÌxóçŸÆc™³c7cæÌ1†flÌfØ>æÃ6fìc |Ù†0ÌÍ™ŒÛ‡ÀÁ€Ù³65cÌñ‚GìfÀ366f¬c€`Ùž0HýŒØ†a€Ù³ÃÍ±ƒÆfÀ366c¸`à`Ù¶0xÁŒØ†c€Ù³ÃÍ±†lÌfÀ3066c¸b``Ù¶00Í™ŒØ†f€ÞxøáƒÇ‡<À?æ¶a;À`Ï0xðç˜ƒÇá€XÀ0À0`x€øð à à@<@À€|€€€x€ððà>À|€x€ð ð @à@>€À€<€Àx€øðàà<À0æ€00ÀÀ6ÆqöqóÌÏ|ñç³Ï€æÆ¶ÆlÙ³m›,¶lÀ6ly†xÇïŸ³aûŽ6lÀ6lÙ†ØÆØ3a€ã¶lÀ68Ù†ØÆlÙ³aši¶lÀæ8}†|sÌÏ`óÏ3ÌÀ3 @@@€€€ @@@€€€ @@?ÿÿÿÿÿÿÿÿÿÿÿ ÿÿÿÿÿÿÿÿü D D ô D D ‡ € @A ŽxðâÄA< A$A Aò " A ‘"$I 0â € € À ÿÿÿÿÿÿÿÿü ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ Two things have happened. First, the usual scatterplot has been produced for the user to view. Points that are selected may be deleted or labelled, axes may be scaled, and so on. (Jan Pedersen's code is used to display these plots.) Second, a ScatterPlot object was created and its presence noted in the map. This ScatterPlot can be named, can be documented, and can respond to methods applicable to scatterplots. These methods are accessed by depressing the middle mouse button over the ScatterPlot object in the map or, alternatively, by depressing the same in the title region of the plotting window. They include adding to the plot either an arbitrary straight line, the least-squares fitted line, a resistant line, or a running linear smooth. At this point in the analysis, an experienced statistician would be uncomfortable about fitting a straight line to the data as they are. Points might be deleted from the plot to investigate those clumped in the left-hand corner, or various transformations might be applied to the data. For brevity, we note that taking the natural logarithm of Y and X works well on these data. Transforming to logs is accomplished by pointing in turn at each of the FloatVectors BrainWts and BodyWts, and pressing the middle mouse button. This gives us access to the reexpressions possible for FloatVectors through the following menu system. ff„Èÿÿÿÿÿÿÿÿÿÿÿ€€€A ‚Ä$ø€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþA0‚"#& €A(…""Š% €BA$…!BŠ$’ð€x"A$ˆÀ‚R$’€„RA"ˆ ‚R$R€ x¸sÖ„sÏ*A!H ‚"$2 € „Ä‰„‰šV> H ‚"$ðø€ „„q„*€ „„ „R€„„„‰Œ‰"€xx„pÐtpÏB€~ CÀ€A ˜D €A ”H€ BA ’H€ø€€ø "~ ’H€ €€„RA ‘H€ ëB9èK8‚8pâÒç£ÅÀ*A Ä €ðBDˆMD‚D‰S$& VACÀ€ˆB@ˆH|‚|q$$ *€ˆB@ˆH@‚@ $$ R€ ˆFDˆÈD„D‰$$ "€øh:8gH8ø8pâá£Ä B€>Çð€Aˆ €€@ H €€B< H €€ AH" OÀ€€ „A@R"H €€ D(A\:Ipè*Aˆ €€ $("bFI‰V>ˆ €€ $DABBI *@€ |A2BBI R€ ‚A"BFI "€ ‚AB:IèB€>€ðA€@ˆ€€B<H€ ø€"H€ €R(€ qpèñf ,ÅÄ—€*A€ ‰‰ ™3&$˜‘€V>€ ø9 "$$€*€ É ™"$$€R€ ‰ ‘"&$‘€"€ uèñ¢%ÄŽ€B€€@À‡€@!!ˆ€€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ@@ˆ€€ÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿ¾@@ˆ€€ÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÞ@@‘àˆ€€ûÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ®@@ ˆ€€ûÚ:cÇŽáÑÿÿÿÿÿÿÿÖ@!`ˆ€€ûvî¹Ù]»vÞÎÿÿÿÿÿÿÿª~ ‡€{ÛÁÇŽÞÞÿÿÿÿÿÿÿÖ€û~ÿ{ÛßûöÞÞÿÿÿÿÿÿÿ®€ûvî¹ÛÝ»vÞÞÿÿÿÿÿÿÿßÿÿÿÿÿÿÿÿÿÿÿ€ûÚ;ãÇŽáÞÿÿÿÿÿÿÿ¿ÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿûÿÿÿÿÿÿÿÿÿÿÿÿÿÿ¿áø?ÿÿÿÿÿÿÿ€ÿÿÿûÿÿÿÿÿÿÿÿÿÿÿÿÿÿ¿Þ÷ßÿÿÿÿÿÿÿ€¿¿oÿÿÿÿÿÿÿÿ€€C¿¿oÿÿÿÿÿÿÿÿ€ ø€ð#¿¿nÿÿÿÿÿÿÿ€ S¿¿oßÿÿÿÿÿÿÿ€ —-¸qg=.+¿Þ÷Ÿÿÿÿÿÿÿÿ€ ø˜‘5Ä‰¨‘!1Wáø_ÿÿÿÿÿÿÿ€ ‡ ¢„ù‘!!+ÿÿÿÿÿÿÿÿÿÿÿ€ ™ ¢„‘!!Sÿÿÿÿÿÿÿÿÿÿÿ€ ‘ AÄ‰‘!!"€ øŽ @ð¸qM!B€@€@ƒ€€€@ ‚ƒ€@@ )E €€B@@ )E € ø€ð"@Gˆ‰I)€ R@@‰I)€ —-ñf.Z8x¸*@!H‰ €€ ø˜‘5 ™1"jD„ÄV~H‰ €€ ‡ ¢ !B8„„*€ ™ ¢ !2B„„R€ ‘ A 1"BD„„"€ øŽ @ðñ.B8x„B€@ ƒ€€ ‚ƒ€@ )E €@B@ )E €ø> @"Gˆ‰I)€ A R@‰I)€ „³aÅ¢@yÏG=*!H‰ €€ð„Ì™’&¢<"$H‘"VH‰ €€„ˆ‘ä äG *€?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø„ˆ‘$#$@‘ R€ Œˆ‘$A"$H‘""€ øtˆ‘Ô>ÓG B€!ât€ˆ~@‡à€"@€H0@@!D€$8pà8¸pâôp,Ãž¸xtp¨@@@$€$D‰DÄ‰Dˆ3$HÄ„Œ‰X@€B|@@$€$pà8„ùD€"'È„„„p©ðH€<D€"@@@'à€$d„D€"$„„„ IB@€R@@@$€"D‰DÄ‰Dˆ"$H„„Œˆ‰ g‹‡^Ž!\ôŽ*@@!D€!â:pà8¸pâDp"#†„xtq ¨LH˜’H‘"!"bD‘"V@~„€ € ñHC’HŸ!BDŸ*€ € HL’H ‰!2BDR€?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿùLH˜’H‘"@F#"BD‘""€?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþù‹‡WFŽ@>B4ŽB€>ïÿÿÿÿ÷ÿûÿÿÿÿÿÿÿx Ÿ€=îïÿÿÿÿ÷ÿûÿÿÿÿÿÿþ¸@€€;ô(ñÓÓ/qßÝÜy{‡‹ÿÿÿÿÿþù‰‰"bFxBj&$`V €?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿùø9 BBBBB$$ *€?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿùÉ ¢BB BB$$ R€?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿù‰ BF@‚BB$$`"€ u B:@|@€A À€A @€€€€€ € ‰„€ D€ $€" $€>‰€A ‰€A €€€€€ƒà€ˆD€ $€ #À€" €> €AˆD€Aƒà€€€€€?€€ @€ €?€" €€€> €@€A @€A?€€€€ÿÿÿÿÿÿÿÿÿÿÿ€ Selecting LOG, as indicated, spawns a new FloatVector from BrainWts, say, that has as elements the natural logarithm of the elements of BrainWts. This vector is automatically attached to BrainWts in the map. We have done this for both BrainWts and BodyWts and have named the resulting FloatVectors LnBrainWts and LnBodyWts, respectively. The scatterplot of these two variables is the following. LÌƒ[?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ0À0À0À0À0À0À0À0À0À0À0À0À0À0À0À0À?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<þ“Îü÷’yàç¼ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<äüçÏç‘Ìóü÷’99Îg¼ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<äùóÏç‘Éóþc29<Ÿ3ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<äùóÏçÉÿþc2<Ÿ3ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<äùóÏçÉÿþc2<Ÿ3ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<ùóÏç’IÃÿArI<Ÿ:ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<üùóÏç’IóÿrI<Ÿ8CÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<üùóÏç“ óÿra<Ÿ8ãÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<üüçÏç“ãÿœòa9Î|çÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ<üÏç“ŽÿœòqàüçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ0À0À0À0À0À0À0À0À0À0À0À0À0À0À À0 „ À0 „ À0ááÎ.¼#ã€À0Â„a0Â$À0$ð„ ‚$À0$„@ ‚$À0"„! Â$À0ÁáèÆ ¼#á€À0€€À0€€À0€€À0À0À0À0ÀÀ0À0À0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿàÀ0€ À0€ À0 À0 À0 À0 À0€ À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0> À0 À0 À0 À0> À0 À0€ À0€ À0€ À0 À0 À0 À0Áÿü À0 À0 À0€ À0€ À0€ À0 À0 À0 À0 À0 À0 À0 À0 À0 À0ø À0 À0 À0 À0 À0 À0 À0 À0 À0 À0€ À0€ À0à À0à À0€ À0€ À0€ À0€ À0€ À0| À0 À0À€ À0ÁÿüÀ À0 À0€ À0€€ À0€à À0À€€ À0 € À0 À0 À0 À0@ À0 À0 À0 À0à€ À0||€€ À0à~ À0€ À08€| À0 À0€ À0`@ À0€ @ À0 ð À08ø>@ À0 @@ À0€ @ À0`ð À0€@ À0@ À08ø À0€ À0€ À0à€ À0 À0ø€ À0 |€ À0Áÿü à À0€ À0à|€ À0€ À0 Ð À07à À0 À0| À0 À0 À0À À0 À0 À0 À0 À0@À À0€ À0€ À0à À0€ À0€ À0à€ À0àø À0€ À0À€ À0 À0" À0" À0" >ï À0" "ø€ À0" ú € À0" #à À0?à€ € À0€€ À0€ À0à À0À À0 À0Áÿü! À0 À0€ø À0à€ | À0€ À0 €ø À0 € À0 À0 À0 @ À0 @€ À0 ð€ À0?à@à À0@€ À0€ À0 À0 À0>€ À0(€ À0(à À0ø€ À0 À€ À0 À0 À0 À0| À0 À0 À0 À0 À0 À0€ À0€ À0€ À0> À0ˆ À0ˆ À0Áÿüà> À0€ À0€ À0€ À0€ À0€ À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0 À0À À0 À0 À0 À0> À0 À0À À0€ À0€ À0à À0€ À0€ À0 À0€ À0€ À0€ À0 À0 À0 À0À À0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿàÀ0ÿü À0€ À0€ À0€ À0 € À0 À0 À0À0À0À0À0À0À0À0À0>àà€p|ƒ€ø|àpÀ0"„@ˆ@@@qˆÀ0Á„@x@ xˆÀ00!„@p„@@ˆÀ0! „@€D„@@DˆÀ0ŽÈà>Ž#€ú8#€B8äpÀ0€€ À0À0À0À0À0À0À0À0 àAÀ0 A@À0 A@À0 ˆøzB"ˆãÀÀ0 Oñ†B"ˆD À0 H ‚$PCÀÀ0 H ‚$P@ À0 H † D À0?ÐOðøz cÀÀ0À0À0`À0À0À0À0À0À0À0À0À0À?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ Now, we might consider fitting a straight line. As before, we add an analysis node to the map, again choosing a BivariateRegression from the toolbox but now with Y and X identified as LnBrainWts and LnBodyWts, respectively. The analysis to date is summarized in the map given below. LÌ*/ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÏÿÿÏÿÿ?ÿÏÿããÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÏÿÿÏÿÿ?ÿÏÿããÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€‡ÿÿ‡ÿÿ?ÿÿÿáÃÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€‡ÿ‡‡'0Ì?áÃÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€·9ÿ·93'&IŸä’fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€39ÿ39ó3#Èÿä“æsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€9ÿ9ƒ2pÌ?ä“sÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€39ÿ393:|Oæ2fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€y9þy938fIŸæ2fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€y9þy9‰<ðÌ?çsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿüÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿäÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿñÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€€€€€€€€€€€€€€€€À€1ûø€°a€ðñ€€`€1€ƒÆ™ŒÌ˜0a€`1™˜À€g±›³çƒx3Ç3<ùçÀ1€À€ÌÙ±›±ŒófÃ36f3lù°Í™»È6aŒ33fc6Àað0`€lÙ›±ŽƒfxÁ#ö3`†ÍƒÍ™«à6gŒ?`c6a˜``€lÙŠáƒƒfØÁã3`ŒÍ†Í˜î86mŒ0`c6a˜À`€lÙŽá‰ƒfØÀÃ6f3`˜Í†Í˜î˜6mŒ3fc6a™€`€Ç„@ïƒ<|pÁãÃž`†ùƒí˜Dð3ÇÇ<9æ`ñø`€,0À€0À€€€€€€€€€À@€0ð@€`@€€p@€À€@€0x@€€@€`€<@€€`À@€<@€À@€€@€``à@€€@€ð@€À@€0ð@€`@€€x@€À€@€0x@€€@€`<@€€àÀ@€@€à@€€@€``à@€€@€ð@€À@€0ð@€`@€€x@€À€@€08@€À@€`<@€€àÀ@€@€à@€€@€``à@€€@€ð@€@€P@€€€€€€`€|€°a€ðñ€Ì``ð#?`Ã1áã€f˜0a€`1™˜À`€€`˜#00`ÃÀc31€€>fxû»>x3Ç3<ùçÀ1€ÀmŒãìãç™ž>ùãÏgŸ`ù›çãv|ð0gŽ>flÃffÌÆm€Ãà`À€3fÍ™±«à6gŒ?`c6a˜``lØóñß?fÃ÷lÙ€`Í››3V1À0lÏ$~ÀÆlÃ0ÀÀ€3fÍ˜ î86mŒ0`c6a˜À`lÙ³ ±Œ°fÃÇlÙ€`Í› ›1Ü0p0lÛ<`ÀÆlÃ1€À€3fÍ˜àî˜6mŒ3fc6a™€`lq³ ±ŒÙ³fÃ4ÓlÙ€`Í› ›1Ü100lÛfÌÆlÃ3À€³|xø@Dð3ÇÇ<9æ`ñø`Ìpûøç™ž>Áçžg™€~ÍóÛ0ˆà0gŽ8hâ€@ >€€@ €p€@ €€@ €D€@ €>{€@ € À€@ €€@ €|F€@ €ð€@ €€8€@ €€DÇ€@ €à„€@ €€‘<€@ €€€ €@ €€ €@ €€@€€Ã€€@€@ €€@@€‚ €G7gÙž>mŒãìãç™ž>ùãÏgŸ€37c³fÍŒ3l1ŒÙ³fÛ6YlÙ€€35c?flØóñß?fÃ÷lÙ€€3Ã°flÙ³ ±Œ°fÃÇlÙ€€3Ã³flq³ ±ŒÙ³fÃ4ÓlÙ€€³Ùž>Ìpûøç™ž>Áçžg™€€€ff€<<€€€€€€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ Here, the ScatterPlot of the logged data and the new BivariateRegression have been given names. For the most part, the actual arrangement of the nodes and links is determined by the user. (It has been our experience that this arrangement is often a matter of personal style. Hence, it has been left to the user's discretion to produce a meaningful network description of the analysis.) Depressing the middle mouse button on this new BivariateRegression produces the same extended menu as before. Having already looked at the relevant scatterplot, we slide over to select a fitting method and choose least-squares. ffx4ÿÿÿÿÿÿÿÿÿÿÿÿÿA D! DQ%Ã‹‡xõÃ€èçœ)&$LH‘ F$A"U$#ˆO D'Ár)$ HH D$ ’2Q$$LH‘ D$A"#ÿÿÿÿÿÿÿÿÿÿÿÿÿÿü$#‹‡4#€èéB@ |?@ ÉÿÿÿÿÿÿÿÿÿÿÿÿþB € ÿ¿÷ÿÿÿÿÿÿÿÿÿ¾A ŽG<âç %ÃˆÄ·wÿÿÏŸ÷ÿûÿÞA! ‘"H‘ &$O‰¿÷ÿÿÏŸ÷ÿûÿ®A!?Gr $'È ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ¿ ×\aG‡‹ÖA! @‘’ $$ ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿƒ·vvç×[·;{swªB! ‘"H‘ $$H ÿÿÿÿÿÿÿÿ~ÿÿÿÿÿÿÿÿ¿¿·vö÷ÚØ7{{{Ö| ŽGê¿$#ˆÄ‡ÿÿÿÿÿÿbÿÿÿýßÿþÿß¿·vö÷ÚÛ÷{{{÷®{ÿÿÿÿÿÿïÿÿÿýßÿþÿ¯¿·vöçÝÛ·{{swÞÿqãÇñÑãÇBãôÏQáâã×¿¹–÷ÝÜy{‡‹¿ÿÿÿÿÿÿÿÿÿÿÿÿÿÿüÿnÝ»îÎÝ»nÝó6íÎÞÜÝ«ÿÿÿþ÷ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿýüÿxãÇñÞÁ¿nß÷v ÞÞÞã×ÿÿÿÿÿÿÿÿÿÿÿÿƒÿßÿÿþðÿÿÿÿü ¼ÿfýûþÞß¿nß÷výÞÞÞý®½ÿßÿÿþï¿ÿÿÿÿýÿ¼{nÝ»îÎÝ»nÝ÷víÞÞÜÝÞC¾áßãÇŒ/ø·¸éý‡qcÇñÑãÇnã÷w^áâã¾>ˆ#¾ÞßÝ»vð÷7·evì¼ÿÿÿÿÿßÿÿÿÿÿÿÿÿÿÿÿþA€S¾ÞßÁãŽÿw·¼oý¼ÿÿÿÿÿßÿÿÿÿÿÿÿÿÿÿÿþ@!9èñpà+¾Þßß›öÿ··³oíý¼8‰ à+ƒáÀãÅ0x¸¸¯ýÌxD !!! ‰ Sÿÿÿÿÿÿÿÿ¿ÿÿÿÿÿü„D A###"D‰ #ÿÿÿÿÿÿÿÿ¿ÿÿÿÿÿü õÃ‹0ç®>8hñàCÿÿÿÿÿÿÿÿÿÿÿÿÿÿü F$MÉ1!#"!! D'È‰ò!!! D$‰!!!ÿÿÿÿÿÿÿÿÿÿÿÿÿ„D$H‰!!#"x4#ˆˆá¡ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ This causes a BivariateLeastSquares object to be produced and attached in the map to the BivariateRegression which spawned it. LÌ)sÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€çÿÿçÿÿŸÿçÿññÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€çÿÿçÿÿŸÿçÿññÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÃÿÿÃÿÿŸÿÿÿðáÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÃÿÃÃ“˜fðá‡ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÛœÿÛœ™““$ÏòI39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€™œÿ™œù™‘äòIó9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€œÿœÁ™8fòIƒ9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€™œÿ™œ™>'ó39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€<œÿ<œ™œ3$Ïó39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€<œÿ<œÄžxfó¹‰ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿþÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿòÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿøÿÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€€€€€€€€€€€€€€€€àÀŒý€Ç‡Œ|ˆÀØ0ÀxxÀ€0ÀŒÀÁ€cŒÌÆfˆÌ0À0ÌÌ`€3ÇØÍÙóÀÁž8ù˜ñçÏ>ŒÆfùÆøÝŸ<ã™|óàÀ`€ælØÍØÆ@ù³a™›3¶ƒ|ØfÌÝŒd›0Æ™³1›`0ø0€6lÍXÇÁ³<`‘û°ÃfÁæÌÕŒp3Æ °1›0Ì00€6lÅpÁÀÁ³l`ñƒ°ÆfÃfÌw6Æ01›0Ì`0€6lÇpÄÀÁ³l`a›3°ÌfÃfÌwL6Æ³1›0ÌÀ0€ãÇÂ w€Áž>8`ñáÏ0Ã|ÁöÌ"xãã†ó0xü0€€`€€`€ÀÀ€€€€€@€` €x €0€ €À€8 €`À €< €À €0À €À0à € €à €À €00ð €À €x €à€ €x €0€ €À€< €`À €< €À €0€ €Àpà € €ð €À €00ð €À €x €à€ €x €0€ €À€< €`À € €à €0€ €Àpà € €ð €À €00p €À€ €x €Ç€ €( €€€€€€0€>ˆÀØ0ÀxxÀæ€00ø€°a€ðñ€€3ˆÌ0À0ÌÌ`0ÀÀ0Ì˜0a€`1™˜À€3<}ŒÝŸ<ã™|óàÀ`6ÆqöqóÌÏ|ñç³Ï€0|Íóñ»>x3Ç3<ùçÀ1€À€¾fÍŒÝŒd›0Æ™³1›`0ø0æÆ¶ÆlÙ³m›,¶lÀ0fù°Í™»È6aŒ33fc6Àað0`€³fÌØÕŒp3Æ °1›0Ì006ly†xÇïŸ³aûŽ6lÀ0fÍƒÍ™«à6gŒ?`c6a˜``€³fÌPw6Æ01›0Ì`06lÙ†ØÆØ3a€ã¶lÀ0fÍ†Í˜î86mŒ0`c6a˜À`€³fÌpwL6Æ³1›0ÌÀ068Ù†ØÆlÙ³aši¶lÀ0fÍ†Í˜î˜6mŒ3fc6a™€`€Ù¾<| "xãã†ó0xü0æ8}†|sÌÏ`óÏ3ÌÀ?fùƒí˜Dð3ÇÇ<9æ`ñø`€``0À€À`30À€À€€€€€à€þ€ þ€ ÿ€@€@€€@?€€€?À€€À€€à€ð€ð€ø€ø€ü€ü€þ€þ€ÿ€€€€?€€?À€ À€/à€°€ð@€ø@€ø€€ü€€ü€€þ€þ€ÿ€€€€?€€?À€À€à€ð€ð€ø€ø€ü €ü €þ@€@€€€ÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿ€@ €@ €@ €@ €@ €ÿÿÿÿÿÿÿÿÿ@ ÿÿÿÿÿÿÿÿü€@ D€@ D€@ ô€xpƒ@ D€DD@ D€DãÄO4q@ €DD"L‰@ €xD"Da@ €@D"D@ €@$D"L‰@ €@àÃ„4q@ €@ €8@ €@ €"@ €=€@ €à@ €@ €>#@ €x@ €@ œ@ €@"c€@ €ðB@ €@Hž@ €@@@ €@@ €@ €ÀáÀ@ ‡€ @@ € @A€#‡<8±C@ ŽxðâÄA<€„@ˆDÉDD@ A$A€Dˆ|àDD@ Aò€$ˆ@DD@ " A€$H‰DDD€@ ‘"$I€Ã‡†8Cƒ@ 0â€@ €@ €@ €‘@ €ì@ €€€@ €€ @ €O@ €IÀ@ À€y@ €I@ €ÿÏÿÿÿÿÿÿÿ@ ÿÿÿÿÿÿÿÿü€@ €@ €@ €@ €@ €ÿÿÿÿÿÿÿÿÿÿÿÀ?ÿÿÿÿÿÿÿÿÿÿÿ€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€€0€1€æ€0€1ŒÀ0ÀÀ€³ìÏ6ÆqöqóÌÏ|ñç³Ï€€›±ŒÙ³æÆ¶ÆlÙ³m›,¶lÀ€š±Ÿ³6ly†xÇïŸ³aûŽ6lÀ€ŽáŒØ36lÙ†ØÆØ3a€ã¶lÀ€ŽáŒÙ³68Ù†ØÆlÙ³aši¶lÀ€Ù„@ìÏæ8}†|sÌÏ`óÏ3ÌÀ€€33€€€€€€€ € € €@€@€@€€€€€€€€€€€€€€€€€€€€€ € € €@€@€@€€€€€€€€€€€€€€€€€€€€€€€€€€€æÀ€0ÀÆ`€6ÆqöqóÌ=÷Ùœ|ñà€æÆ¶Æl†dÃÌÙ†m› €6ly†xÇìžpÁìÙžaû€€6lÙ†ØÆ6ÀlÙ¶a€à€68Ù†ØÆl¶LÆlÙ¶aš`€æ8}†|sÏÏxwÇÏŸ`óÀ€À€À€À€€€€€€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ Pressing the middle mouse button on this object produces a collection of methods which a BivariateLeastSquares can handle. ff<ÿÿÿÿÿÿÿÿÿÿÿü€ü„€Oÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ€¼¯ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþø_ÿ÷ÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿþ€®÷þà?îÿÿ÷ÿÿ¿ÿöïþ€N÷ÿþïÿïÿÿ÷ÿÿ¿ÿ÷Þûïþ€Žö–Œ/øÂÓø>1ÆÌð€À|É™†1˜1˜6Ø0fÍ˜€À`á™ž1ø1˜0À!æÍø€À`9™¶1€1˜0ÀãfÍ€€À`™™¶1˜31˜0ÀÃfÍ˜€À~ðí™Ÿð>ø°ÀÁö|ð€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€Ìpƒ‡AÃ‡0áÃ€Ì€€ˆ„H‘"Â$H‘"Q$H€ÍóçŸ8pà€ÌØQ$@"Da $@ D‰€ÎçÇ€P$À&¡ @ @‰0€Ç›6lÀàE@*8!ãÀC€x‰P€ÃÛ6oÀ…@*! €@DyP€ÀÛ6lø@2! @D €ÌÛ6lÀ @"D! BD‰€Ï™çÇ€ ãã€8øáÀã‚8pà€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€À€ÀÀ€Ï€Á€Àx€€çÇ€ÌÀ€ÀÌÀ€ HŒ"€ÌÏÏ³8Àáóœ|ø>ùçÀ|@ˆ”"€ÌÙ²Ù³ÀxÀÌÙ†m˜6Û6À|@€ÏŸ¸Ù³<À<ÃÌÙža˜0Ã6"€ÌØÙ³lÀÆÌÙ¶a˜0Ã6|"€ÌÙ¦Ù³lÀÌÆÌÙ¶a˜0Ã6|A"€ÌÏ<ÏŸ>ÀøsìÏŸ`ø°ÁæAŸ€À€À€À€À€À€Î8ÀðÀÀpàƒ‡€Î8`À˜`€`‰„H€€Ï{6û|Ï˜Ùœ|ð„Ž‘ É „ˆ ‰"„‘ˆ‘ u„Žg € ü t¼.yÃ€ øŒ1!"$@ „!!#ã€ „!!"@ Œ!!"$@ ütŒ!Ã€ ˆ@| H@‚ P‡‹ð€BY‹0âÑ PHM@xBfLÉQ ˆˆHH@BDHˆr øHHH@BDH‰’ HHH@‚FDH‰ ˆGˆ0|:DHˆê ÿÿÿÿÿÿÿÿÿÿÿÿÿàÿÿÿÿÿÿÿÿÿÿÿÿÿàÿÿÿÿÿÿÿÿÿÿÿÿà÷ÿÿÿÿÿÿÿÿÿÿÿÿàï‡‡Lÿÿÿÿÿÿÿÿÿàß{{3ÿÿÿÿÿÿÿÿàß{{wÿÿÿÿÿÿÿÿà¿{{wÿÿÿÿÿÿÿÿà{{wÿÿÿÿÿÿÿÿà‡‡wÿÿÿÿÿÿÿÿàÿÿÿÿÿÿÿÿÿÿÿÿÿàÿÿÿÿÿÿÿÿÿÿÿÿÿà €€ €€ —.9à ˜‘1"D€ Ž!>@€ ! @€ ‘1"D€ Ž.8` ÿÿÿÿÿÿÿÿÿÿÿÿÿà from which we select the item Zoom. (By the way, this is the menu that we've used throughout to name the various objects in the analysis.) This selection causes a new kind of DINDE window, a "Microscopic View" of the selected object, to be opened which displays the details of the object that has been zoomed-in on. For this BivariateLeastSquares, the new window looks like LÌÀ%?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü<|OÿÿÿÿÿÿÏùäÿÿÿÿÿÿÿþgòÿÿŸÿÿÏÿÿÿþÿÿÿÿÿÿùÁàÿþÇãÏ?ü<|OÿÿÿÿÿÿÏùäÿÿÿÿÿÿÿþg'æfÿÿŸùÿÏÿÿÿœçÿÿÿÿÿÿóœçÿÿüÎO?“ÉçŸü<8ÿÿÿÿÿÿÿùçÿÿÿÿÿÿÿüÏ'ægÿÿÿÿùÿÏÿÿÿœçÿÿÿÿÿÿóœçÿÿùÌç?9œçŸü<8L0pààÀÌÌÁÏÿð`ð¯ÎfO0à˜p`ÏÁÃÃ>æ8çüÀÿÿùüæ9œóÏü<’IÉ'&NNNIÌÌœÏÿç'?ùŸÎO&d“9ÎOœ™™žääÌ““?çñÎÿøæs9œóÏü<’Iùç#ÏÎNIüÌœÈŸÿç'?ùŸÎrf~gŸ9ÎOœùŸ„äçÌó‘ÿçüÎÿùÌæs9œóÏü<’Iùç0ÏÎNIþÜ€ÈŸÿç'?àqÎrfpg˜9ÀO€ÁÃœääæðçüþÿùÌçó9œóÏü<ÆIÉçsÎrvæg“9ÏÏŸ™ñœäääÌóþ?çœÎÿùÌæs9œóÏü<ÆIÉç&NNNIÏ<œç?ÿç'?æ~aÎrpæg“9ÎOœ™™œäääÌó“?çœÎÏùÎNs“ÉóÏü<îLðpààÀÌ<Áç?ÿðg?ææyñ'˜œ`ÀÁÄÃÆ$ø8óÁàÿÏüÇãçŸü?ÿÿÿÿÿÿÿÏÿÿÿÿÿÿÿÿÿÿÿÿÿçÿÿÿÿÿÿÿÿÿÿÿÿÿçÿÿÿÿÿóÿÿÿÿÿÿÿÿÿÿçŸü?ÿÿÿÿÿÿÿÏÿÿÿÿÿÿÿÿÿÿÿÿÿóÿÿÿÿÿÿÿÿÿÿÿÿÿçÿÿÿÿÿùÿÿÿÿÿÿÿÿÿÿÏ?ü?ÿÿÿÿÿÿÿÏÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿçÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü0000000000000000~>øà0cc1€3300c<}óÇc}ƒç<÷Œ0300cfÍ¶lÙc1ólÙ³fÌÌ>300c~Í‡ïÜc1ƒß³fÌÀ3300c`Í†c1ƒ3fÌÀ3300cfÍ†lÓc1ƒÙ³fÌÌ33300 ~<}ƒÇž>1ƒ<ÌÌ1à0 0 Ì0@x0@0€0€0€000000|x€0fÌ™‡™€0fàû3ž>`™ƒ0fy›0Í³f`™ƒ0|=›3Ì?f™ƒ†0f ›6Ì0f™ƒŒ0fÍ›6Ì3f`™™ƒ˜0 føùóì>`Œ0 0 @0@@0@€0@€00000000<ÀüðxxÇ‡€0fÀ1™€ÌÌ€ÌÌÀ0pÏ><1Ãç0ŒÀ0<Ù³f0ñŒ08 ŒÀ0 Ù³~0y‡Œ0 ƒÇÀ0 Ù³`0Œ`ÌÃÀÀ0@ fÙ³f1™Œ0ÀÌÁ†ÌÀ0@ |Ï><1ðçÇ0üxÁ†‡€0 €@00 €€00!€00A0B0‚0„0„00000 a€ó<<<<<<<0 @a€›fffffff0 @3}Ã<øñ€ff0 6`fÍ€ffff0f0‚ 6a›fÍ™€ffff`ff0@aó<øñ€<<<<~<<0€À0"À0"À0D0D0D 0ˆ 0ˆ@0@ 0€@0!€0!0B`~x<<ÃÃÃÃà0B`ÌÀffÆffc0„oŸ<ùãÏŸáó˜fÀf`c0„lÌfÛ6lÌxÀÆfÁÆaÇÀ0 lÌ~ÃìÌ<ÃÆfÀcàf`0 lÌ`ÃÌÆÆ0fæ`f``0 ÀlÌfÃ6lÌÌÆÆ`f`Æfff`0 @lÇ<ÁãÏ‡øsã˜~<`ÃÃÃÃÀ0 €0$@€0$0(‚0I`0Q€0’0¤0¤ 0H@0H@ 0€@0‘€a€€?0"a€€0033330D3}¾|óç>|À33330D3m³1›lÙ³0À363330 ˆ`a³1û³03&30 €6a³1ƒ303?30 @6a³1›Ù³0À33330 €0a³ó>À0AÀ00,†00500:`0r €0t@0)€p0ò€0d0è80ÐÀ0£À0˜0Ü<À`<Ç‡‡‡‡ƒ€0À0€`f`fÌÌÌÌÇ€0ÛÇÙÇÏ0}ƒÏ£|Ï³3ìgÇ||ùö0Ç‡‡ÇÁ€0ÐÀ€0è8€0d0ò€0)€p0t@0r €0:`050,†0AÀ0 €00 @<À‡0 €fÀ‡™0 ˆ`pÏ><`™‹0D<Ù³f`0DÙ³~ƒ™“0"Ù³`™ƒ™Ÿ€0‘€fÙ³f`Œƒ™ƒ0€@|Ï><`Œ0H@ 00H@00¤ 00¤0’0Q€0I`0(‚0$0$@€0 €0 @0 À`x‡‡‡‡€0 `ÌŒÌÌÌÀ0 oŸ<ùãÏŸ0ŒÀÀÌÀ0„lÌfÛ6lÌ0‡ƒ‡€0„lÌ~ÃìÌ0ŒÀÃÀ0BlÌ`ÃÌ`ŒÌÆÀ0BlÌfÃ6lÌ0ÀÁŒÌÌÀ0!lÇ<ÁãÏ‡0üÁ‡‡Ç€0!€0€@0@ 0ˆ@0ˆ 0D 0D0D0"0"0€0@0‚ |~À†cÃÆ0 f`À1†€Æfc0@€fxö}™Æy€`Ï|ÌxóçŸÆc0@€fÍ–Í˜fÉ€|Ù†0ÌÍ™ŒÛ‡ÀÁ€0 @|ýÆÍ™æà`Ùž0HýŒØ†a€0 @fÀvÍ›f8`Ù¶0xÁŒØ†c€0 fÍ6Í›f™€`Ù¶00Í™ŒØ†f€0fyæ|ùöñ€`Ï0xðç˜ƒÇá€0À0À0`0„0„0‚0B0A0!€0 €€0 €@0@ À0@ ~À6û0 `06ƒÆ™Œ0 `ß|ñó1ÆÌñæƒfùÆøÝŸ<ã™|óàÀ`03|ØfÌÝŒd›0Æ™³1›`0ø003fÁæÌÕŒp3Æ °1›0Ì0003fÃfÌw6Æ01›0Ì`003fÃfÌwL6Æ³1›0ÌÀ00³|ÁöÌ"xãã†ó0xü00`0`0À00€0€0€0@0@0 0 0 0f`ð`F~À†cÃÆ0f`˜`F``À1†€Æfc0<``ù™ãìfìùà`Ï|ÌxóçŸÆc0``Íó6lfìc |Ù†0ÌÍ™ŒÛ‡ÀÁ€0`Í›6fÆ¬c€`Ùž0HýŒØ†a€0<`Í›6bƒ¸`à`Ù¶0xÁŒØ†c€0f``Í›6cƒ¸b``Ù¶00Í™ŒØ†f€0f`~Íñãá;À`Ï0xðç˜ƒÇá€0À0À0`00000?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿü which is again a network of sorts. All DINDE objects can be zoomed-in on to reveal their inner detail. An important point to note here is that each label in this network again represents an object which can be examined, named, or commented on, and which responds to its own set of methods. For instance, we might now construct a ScatterPlot in the analysis which used the Residuals for its Y coordinates and the FittedValues for its X. In the present case this is not necessary, since BivariateLeastSquares objects are equipped to produce such important plots easily. By getting the extended menu for this object we may produce a variety of residual plots. ff‚iÿÿÿÿÿÿÿÿÿÿÿø ù ˆ xH ñ ¨ H ˆ øù! !"ˆ „³aÅ¢@½yHð„Ì™’&¢@‘ñ"¨„ˆ‘ä@‘!H?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿø„ˆ‘$@‘"ˆ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿùŒˆ‘$!!?ÿÿÿÿÿÿÿýûÿÿÿÿÿÿÿþøøtˆ‘Ô>ÿÿÿÿÿÿý‹ÿÿÿ÷ÿûÿx=íÿÿÿÿÿÿÿ¿ÿÿÿ÷ÿûþ¸0;ýÇÇGÓÅÇG»ÝÜy{‡‹ŽþöÞÝöíÛs{vÝýP?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÞÜ;{{Æãþ°?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþþÞÝöÿÛ{{6ýýsðA þÞÝöíÛssvÝþòA@ þáå÷;‹‹ŠãýòáÄt„q"YÄ#‚€!á€ÿÿÿÿÿÿÿÿÿÿÿò $Œ„‰"""j$$A"€OÿÿÿÿÿÿÿÿÿÿÿóññÄ„„9>AÄ#$×,,Ãž¸xtp¯ÿÿÿÿÿÿÿÿÿÿÿò $„„É @$ B€$ ‘43$HÄ„ŒˆT $ŒŒ‰""B$dD@$ Ÿ "'È„„„p¤áÄttuAÃ£ˆ $ "$„„„D"‘ "$H„„Œˆ„!àÐŽ "#†„xtp ðA?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿüAáÄt„q"YÄ#„¸tq $Œ„‰"""j$$DÄŒˆ¢ññÄ„„9>AÄ#„„„øB $„„É @$ D„„€B $ŒŒ‰""B$dD„Œˆ¢áÄttuAÃ£„„tqÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿûÿÿÿÿÿÿÿÿÿÿ÷ÿÿþÿûûÿþÿ¾ÿÿÿÿð6ÿÿþ÷ÿÿûÿþÿ¾ÿÿÿÿ÷þÿÿþ÷;‹{ŽãÝã¦;Üwô?ÿþöíÛs{vÝÝÝ•ÛÛ°vÿÿþ;{{ÆãëÁ¾;ÜwöÿÿþöÿÛ{{6ýëß¿Ûß·öÿÿþöíÛssvÝ÷Ý½Û›·öÿÿþ÷;‹‹Šã÷ã¾<\w÷?ÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ ðA AáÄt„qA#žxt³‹0 $Œ„‰"$H„ŒÔLÈññÄ„„9A#ˆ„„Èˆ $„„ÉA H„„†Hˆ $ŒŒ‰"A$H„Œ„HˆáÄttuA#†xtƒ¨ˆ„xð<> B! !#Ï!$$>$$$‰ $$F $$ø> #Ãÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþ Each of these produces its own kind of DINDE object having its own specialized capabilities. For example, the interpretation of a QQplot is quite different from an ordinary scatterplot and from these other residual plots. It therefore makes sense to separate the different kinds of plots. Let us suppose that the analyst has elected to see each of the available residual plots and has explored them to his or her satisfaction. The analysis to date appears as the following map. LÌ“wÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÏÿÿÏÿÿ?ÿÏÿããÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÏÿÿÏÿÿ?ÿÏÿããÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€‡ÿÿ‡ÿÿ?ÿÿÿáÃÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€‡ÿ‡‡'0Ì?áÃÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€·9ÿ·93'&IŸä’fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€39ÿ39ó3#Èÿä“æsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€9ÿ9ƒ2pÌ?ä“sÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€39ÿ393:|Oæ2fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€y9þy938fIŸæ2fsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€y9þy9‰<ðÌ?çsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿüÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿäÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿñÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€€€€€€€€€|ˆÀØ0ÀxxÀ€|€°a€ðñ€fˆÌ0À0ÌÌ`€f˜0a€`1™˜ÀfùÆøÝŸ<ã™|óàÀ`€fxû»>x3Ç3<ùçÀ1€À|ØfÌÝŒd›0Æ™³1›`0ø0€|Í›»È6aŒ33fc6Àað0`fÁæÌÕŒp3Æ °1›0Ì00€fÍ™±«à6gŒ?`c6a˜``fÃfÌw6Æ01›0Ì`0€fÍ˜ î86mŒ0`c6a˜À`fÃfÌwL6Æ³1›0ÌÀ0€fÍ˜àî˜6mŒ3fc6a™€`|ÁöÌ"xãã†ó0xü0€|xø@Dð3ÇÇ<9æ`ñø``€À0À`€€0ÀÀ€€€€€€à!þ€ ÿà ÿ€ ÿà ÿ€€ ÿð €€ ÿð À€ ÿð ?À€ ÿø ?à€ ÿø ð€ ÿø ð€ ÿü ø€ ÿü ø€ ÿü ü€ ÿþ þ€ ÿÞþ€ !ÿþÿ€ ÿÿÿ€ ÿÿÿ€€ ÿÿ€À€ ÿ€?À€ ÿ€?à€ ÿÀà€ ?ÿÀð€ ?ÿÀø€ ?ÿàø€ ÿàü€ ÿàü€ ÿðþ€ ÿñÿ€ ÿ€ € € € €€€€óÀ€0ø€°a€ðñ€˜``€0Ì˜0a€`1™˜À›c8û8ùæg¾xóÙçÁ€0ø0#?`Ã1áã0|Íóñ»>x3Ç3<ùçÀ1€ÀócÛc6lÙ¶Í–[6a€0Ì0#00`ÃÀc31€0fù°Í™»È6aŒ33fc6Àað0`›6<ÃflÃffÌÆm€Ãà`À0fÍ†Í˜î86mŒ0`c6a˜À`›lÃlc6lÙ°Í4Û6a€0fÍ›3cV1À0lÏ$~ÀÆlÃ0ÀÀ0fÍ†Í˜î˜6mŒ3fc6a™€`ó>Ã>9æg°yç™æa€0fÍ›1AÜ0p0lÛ<`ÀÆlÃ1€À?fùƒí˜Dð3ÇÇ<9æ`ñø`€€0fÍ›1ÁÜ100lÛfÌÆlÃ3À0À€€?føñð€ˆà0gŽ€àÀ€ð€ø>€ÀÀ€<ð€à~€€€øð€À|€<€€àà€ü€ø€Àà€<ü€à€à€Ø€€€ÿÿÿÿÿÿÿÿÿÿÿ€€€€€€€ÿÿÿÿÿÿÿÿÿÿÿà€€ €€ €ÿÿÿÿÿÿÿÿþ€ €€ €€ €€ÿÿÿÿÿÿÿÿÿ€ €ðà €€ €ˆ ˆ €€ €ˆ!Çˆž 8hâ€>€ €ˆ""ˆ D™€€ €ð""ˆ DˆÂ€€ €€""ˆ Dˆ"€€ €€""Hˆ D™€€ €€!Á‡>8hâ€€ €€€€ €p€€€ €€à€ €D€€€ €>{€€€ € À€€ €€€ €|F€€ €ð€€ €€8€€ €€DÇ€€ €à„€€ €€‘<€€ €€€ €€ €€ €€ €€€ €€Ã€€€ €@ €€€ €G@ð"" €€D @€"" €€$D‰"@€""@ €€áÃÃ@€!Á€ €"€ €Ø€ €€ € €€ €ž€€ €“€€€ €ò€€ €’€€ €ÿŸÿÿÿÿÿÿþ€€ €€€ €€`€ €€Ÿÿÿÿÿÿÿÿÿ€ €€ €€ €ÿÿÿÿÿÿÿÿÿÿÿ€ € € € ÿÿÿÿÿÿÿÿÿÿÿà€ € € € € € € € € € € € € € € € € € € € € € € € € € €€€€€€`€b0Ì`€b3€`€€€>7gÙž>mŒãìãç™ž>ùãÏgŸ€37c³fÍŒ3l1ŒÙ³fÛ6YlÙ€€35c?flØóñß?fÃ÷lÙ€€3Ã°flÙ³ ±Œ°fÃÇlÙ€€3Ã³flq³ ±ŒÙ³fÃ4ÓlÙ€€³Ùž>Ìpûøç™ž>Áçžg™€€€ff€<<€€€€ € € € € € € € € € € € € € € € € € € € € € € € € € €€€€€€€Ì€€`€ŒÀ€mŒãìãç˜8{î³8ùãÀ€ÍŒ3l1ŒØ3É‡™³Û6@€lØóñØ?<áƒÙ³<Ã÷€lÙ³ ±Œ0l9€Ù³lÃÀ€lq³ ±ŒØ3l™ŒÙ³lÃ4À€ÌpûøçŸž>ðïŸ>Áç€€€€€€€€€€€ó€?ÌÀ€ÁçÏð€ðÏü€üÀ?ƒÿ€? <ÿÀ€ÀÀ€þ?ð€ðpüü€~ðÿ€€ààÿÀ€à À?ð€øÀÀ?€ü€~<ÿ€€€üÿÀ€àxø?ð€ü ðü€?Àðàÿ€ÀÀÿÀ€ðà??ð€üþü€ Àüÿ€àÀ8øÿÀ€øð?ð€~ðÀü€€?€ÿ€à àÿÀ€øÀþ?ð€Àüü€0ðÿ€àÿÀ€ÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÀ?ð€?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð<À€ € € ÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿÿ€ €ÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿà € ÿÿÿÿÿÿÿÿÿÿÿÿþ€ € €ø ÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿø€ À€ € €ü øÀ€ €$ €DÀ €Œ ø€ €@€$ø €@€D$ àð€Œ $ €$@€ €$@€$@ø ø€€D@ €Œð € €€ÿD~ Àà€à€¢$Dˆ Ÿ!€à€@€ÆDGB d> €€€@€ŠŒLB@ @ ’2À€ ð€“ôôGÀ@ @€ørÄ'à@€¢$@D!ð ð€>ø€ € ø€@€ @à € $ €€ €‡ @0ü € €ˆ€ @ ð€€8ˆÀ€qÐ'Dø€#‡4°ãhŽ8iÀˆŽxðâÀ ˆ€ˆ€€$@$|€àˆˆ"LÈ¨"™ † A ˆáÃ†‘ˆà…†Ž"€ †"D€ò¨ ‰à Aò ñ ‰‘Q†I‘Ã‡Àâ ‚€ "D¨ ‰ ˆ" A ¡ñ€ˆ‘PÀ„HŸ$H€ " € ˆ‰"L¨‘"™2 ˆ‘"$I ‘@ˆ‘"P „H$HŽ"8p#‡Dp"#ˆ<€ ‡4€ò¨ŽhÑà‡0â ‰ ‰“"!„I‘$H"DˆÄH‚&D!DO€ ˆáÀ† áÄFŽ"$K"0`‡Æ"Dx!C€8 $H‘"D"Dˆ!@ˆ€ $H‘&Dˆ$H‚&Lˆ „H€ÆDT` Ã‡8p#‡4x ƒˆ€ŠŒ˜ € €“ À`À€¢&2 €ÆDT` A€ŠŒ˜¿ÿ r|€Q“1‘€ €â¢&2"£ø |€ÿÿÿÿÿÿÿøÿÿÿÿÿòÿÿÿÿÿà € ÿÿÿÿÿ“ÿÿÿÿÿÿþ € €ø€ € ÿÿÿÿÿÿøÿÿÿÿ'ÿÿÿÿÿø€ €ÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ €?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð€€€ÿÿÿÿÿÿÿÿÿøÿÿÿÿÿÿÿÿÿÿÿÿÿ€€€€€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€ Note that the logical flow of this analysis can be easily seen in this display. For much larger analyses, the logic of the analysis will not always be so transparent. Therefore, a number of tools are made available to the analyst which allow information on the analysis to be added to its display. Minimally, the analyst may add notes to any node (to describe what the relevant features are at that node, why such and such a decision was taken, and so on). Additionally, DINDE objects called Memos (objects which contain nothing but commentary) can be usefully inserted as nodes at key decision points. More direct methods exist which allow the analyst to alter the display so that it best reflects the logic of his or her analysis. These include the ability to make and break links between nodes (N.B. causal relationships between nodes cannot be rearranged in DINDE, see Oldford and Peters (1986b) for further details). In this way the network itself can be rearranged. Perhaps the most important analysis management tool is the ability to compress parts of the analysis into smaller sub-analyses. This is achieved by selecting the title bar of the analysis map with the middle button depressed and selecting the "Create a View" item from the resulting menu (as below). ffMÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿà@ @€ HÄ€€@H HÄ€@@ ÿÿÿÿÿÿÿÿÿÿÿþHÄŽŽ.\8ˆâ"@E(‘‘1bD‰" À E(Ÿ!B| ñTH A E(!B@ T L AB‘‘!BD ˆJ#‹Yâ"=pà"#ˆŠBŽŽ!B8àˆI$Mj"‰"$HŠ@I!ÈBT ð'ÅR@H¦HBT $RÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÈdHBˆ $B"ÿÿÿÿÿÿÿÿÿßÿÿÿÿÿÿÿ÷È#¨Aàˆ à#‚"wßÿÿÿÿîÿÿ¾ßÿÿÿÿÿÿÿûÀsßÿÿÿÿîÿÿ¾ÿÿÿÿÿÿÿÿõÀuÜt¦ÝÂÝÜwwÿÿÿÿÿúÿÿÿÿÿÿÿÿÿÿÿÿþvÛ²•íÝîvïÝÛ·wÿÿÿÿÿõÿÿÿÿÿÿÿ¿ÿÿÿþvÞ7½î«îöëØ:¯ÿÿÿÿÿúü?ÿÿÿÿ}¿ÿÿÿþwY·½î«îöÿëÛú¯ÿÿÿÿÿõûßÿÿÿÿ}ÿÿÿÿþw›·½ïwîöï÷Û½ßÿÿÿÿÿû÷úcÆþ?»¸îïÿþwÜW¾wò÷÷Ü}ßÿÿÿÿÿ÷÷ù]»w}ß»·nïÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ÷ûÁãp×°u_ÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ÷ûß›wüß×·õ_ÿþ@{ÛÝ»w}ßï·{¿ÿþ@ |;ãÅ˜þ/ï¸û¿ÿþH A ÿÿÿÿÿÿÿÿÿÿÿþL A! ÿÿÿÿÿÿÿÿÿÿÿþJ#‡<áÐ"#ˆˆ@—,ç£ÅÃ…ÿÿÿÿÿÿÿÿÿÿÿþI$H‘0"$Hˆ@˜‘5$&$J I'Çò'ÅP@Ÿ r$$#… H¤‘$P@!’$$ J HdH‘0$B !‘!$$$D H#‡áÐ#‚ é£Ä#ˆ @ @ @ @ H H! H q@—,ç£ÅÃ€ H ‰ @˜‘5$&$@ H @@Ÿ r$$#€ H @!’$$ @ H !‘!$$$@ OÉ é£Ä#€ @ @ @ @ OÀ H H#‡bâœ H$H™“" OÇÈ‘> H$‘ H$H‘2" H#‡áÑœ @ @ @ @ GÀ € H € HˆŽ$#€ G„H‘$$@ @AÅ $'À @&E $$ H$B$d@ GÃ¢££€ @ @ ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿà This done, the analyst is prompted to identify those nodes which are to appear in the new analysis map. From the example, suppose that the analyst selects the two nodes representing the initial false start of the analysis, namely the first BivariateRegression and Scatterplot objects produced. The effect on the analysis can be seen below. LÌ‡mÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðçÿÿçÿÿŸÿçÿññÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðçÿÿçÿÿŸÿçÿññÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÃÿÿÃÿÿŸÿÿÿðáÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÃÿÃÃ“˜fðá‡ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÛœÿÛœ™““$ÏòI39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð™œÿ™œù™‘äòIó9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðœÿœÁ™8fòIƒ9ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð™œÿ™œ™>'ó39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð<œÿ<œ™œ3$Ïó39ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð<œÿ<œÄžxfó¹‰ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÿÿÿÿÿÿþÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÿÿÿÿÿÿòÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÿÿÿÿÿÿøÿÿÿÿÿÿ?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð00à0F~À†cÃÆ0|€°a€ðñ€0F``À1†€Æfc0f˜0a€`1™˜À7Î7Æìùà`Ï|ÌxóçŸÆc0fxû»>x3Ç3<ùçÀ1€ÀæÃ6fìc |Ù†0ÌÍ™ŒÛ‡ÀÁ€0|Í›»È6aŒ33fc6Àað0`66f¬c€`Ùž0HýŒØ†a€0fÍ™±«à6gŒ?`c6a˜``66c¸`à`Ù¶0xÁŒØ†c€0fÍ˜ î86mŒ0`c6a˜À`66c¸b``Ù¶00Í™ŒØ†f€0fÍ˜àî˜6mŒ3fc6a™€`æ¶a;À`Ï0xðç˜ƒÇá€0|xø@Dð3ÇÇ<9æ`ñø`À0À0ÀÀ0€0À`0€000ð0øü0 ÿÿ€ÿ0 ÿüÿ€0 ÿÿàà0 ÿþø0 ÿÿðü0 ÿÿÿ0 ÿÿøÿ€0 ÿÿ€à0 ÿüø0 ÿÿàü0 ÿþÿ0 ÿþðÿÀ0 ÿÿ?à0 ÿÿøø0 ÿÿÀþ0 ?ÿüÿ0 ÿÿàÿÀ0 ÿþ?à0 ÿÿðø0 ÿÿþ0 ÿÿøÿ0 ÿÿÀÿÀ0 ?ÿÃð0 ð0 0 0 ?ÿÿÿÿÿÿÿÿþ0 ?ÿÿÿÿÿÿÿÿþ0 ?ÿþ?ÿ¡ÿÿþ0 ?ÿþßÿ¯ÿÿþ0?ÿþÙÎ/ÿþ0?ÿþÖõ£kÿþ0?ÿþÑÅ¯kÿþ0?ÿþ×µ¯kÿþ0?ÿþ9Ê!lÿþ0`?ÿÿÿÿÿÿÿÿþ00ø0#?`Ã1áã€À`Œý€Ç‡Œ?ÿÿÿÿÿÿÿÿþ00Ì0#00`ÃÀc31€€`ŒÀÁ€cŒÌÆ<00|Ìñö3v|ð0gŽ>fŒÆ800fù›63v1>lÃffÌÆm€Ãà`Àƒ7Í†lÍØÆ@ù³a™›3¶ƒ000fÍ›3cV1À0lÏ$~ÀÆlÃ0ÀÀƒ6llÍXÇÁ³<`‘û°Ã0ÿàðÿàÿ†00fÍ›1AÜ0p0lÛ<`ÀÆlÃ1€Àƒ6l6lÇpÁÀÁ³l`ñƒ°Æ0ÿàðÿàÿ†00fÍ›1ÁÜ100lÛfÌÆlÃ3Àƒ6l6lÇpÄÀÁ³l`a›3°Ì0ÿàðÿàÿ†0?føñð€ˆà0gŽ8`ñáÏ0Ã0ÿàðÿàÿ†0€`€€00`€€000À000€00ÿø00ÿøÿà080ÿà0àð0m(ÿð0ø0ÿè0øþ0h0à00€0ÿø0øà0 ‡ÿæ0àð0@Gÿæ0ø0þ$&0üþ0þ´¦0à0ÿàþD&0€0ÿàþI¦0üà0ÿà&0àð0ÿàÿæ0€ø80|þ<0à?ÿÿÿÿÿÿÿÿþ0€€?ÿÿÿÿÿÿÿÿþ0|à0àð0ø0n000ÿÿÿÿÿÿÿÿÿÿÿÀ0@0@0@0@0@0ÿÿÿÿÿÿÿÿÿ@0@0@0@0xpƒ@0DD@0DãÄO4q@0DD"L‰@0xD"Da@0@D"D@0@$D"L‰@0@àÃ„4q@0@08@0@0"@0=€@0à@0@0>#@0x@0@ œ@0@"c€@0ðB@0@Hž@0@@@0@@0@0ÀáÀ@0 @@0#‡<8±C@0„@ˆDÉDD@0Dˆ|àDD@0$ˆ@DD@0$H‰DDD€@0Ã‡†8Cƒ@0@0@0@0‘@0ì@0€@0 @0O@0IÀ@0y@0I@0ÿÏÿÿÿÿÿÿÿ@0@0@0@0@0@0ÿÿÿÿÿÿÿÿÿÿÿÀ0000000000000000000000000000000000000001€æ€001ŒÀ0ÀÀ0³ìÏ6ÆqöqóÌÏ|ñç³Ï€0›±ŒÙ³æÆ¶ÆlÙ³m›,¶lÀ0š±Ÿ³6ly†xÇïŸ³aûŽ6lÀ0ŽáŒØ36lÙ†ØÆØ3a€ã¶lÀ0ŽáŒÙ³68Ù†ØÆlÙ³aši¶lÀ0Ù„@ìÏæ8}†|sÌÏ`óÏ3ÌÀ00330000000000000000000000000000000000æÀ00ÀÆ`06ÆqöqóÌ=÷Ùœ|ñà0æÆ¶Æl†dÃÌÙ†m› 06ly†xÇìžpÁìÙžaû€06lÙ†ØÆ6ÀlÙ¶a€à068Ù†ØÆl¶LÆlÙ¶aš`0æ8}†|sÏÏxwÇÏŸ`óÀ0À0À0À0000Üp0þÃü0þüÿÿ0þøÿàÿÿÀ0þ`Àü?ÿð0þ€>ÿÀÿü0üð?øÿþ0ü0€ÿÿÿ€0üÀ|ÿàÿà0üðþÿø0ø€ÿÀÿþ0ø`|?øÿÿ0ø€àÿÿÿÀ0øÿð?ÿð0ø0øþÿü0ðÀÀÿÀÿÿ0ð>?üÿÿÀ0ððÿ€?à0ð`~0ð€0ÿÿÿÿÿÿÿÿÿÿÿÿÿ€0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿü€0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ€0€0€0€0ÿÿÿÿÿÿÿÿÿÿü€0?ÿÿÿÿÿÿÿÿÿÿÿÿÀÿÿÿÿÿÿÿü€0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿà @€€0 @€€0 @à€0 ø@|€€0ø @€€0 @€0 @€0 @ €0€ @ €0b€ @ø€0D€ @„ €0H€À @ €0Q€ @À€0b€ |@|€0D€ø @@€0H€$ €@@ð€0Q€ $ à€@ðð@ €0b€ @€à @@€@@ €0D€@ø @ €€ @@à@@ø€0H€@ ð €ø@ˆð €0Q€ð € @óá @bˆ|@ €0â€D~ @,‡À @‚Ž@€0D€Dˆ @@@˜~€0È€GB @@@ è¢|€€0Q€LB@ >ß!ðð@8€€ >€0b€ôôGÀ@ | @@@€€0D€@D!ð àqÁ€08áÃ€€0D‡$@Ã‡ "8Ä€0D€ˆ€ @8pÒ#°ÑÄ@$G8â$A"¢'€0DpãÃ†–mŽ8iÀˆŽxðâÀ"D2 A "É2"€$‘"DCã"<¡„€0|I™U"™ † A >0#Á ‰á%Ç‘0""D D€0DÁHU ‰à Aò $A ‰$H‘$A&DB$ €0D!H"U ‰ ˆ" A"D2dA"‰2"€$H“"DÃ <AÄ€0D$I"U‘"™2 ˆ‘"$I8Ñ£Á8ˆÑÄ@áÃ‡8€0DàÃ†UŽhÑà‡0â@€€0€0`€0H €€0 !@>€0ÈŠŒ ø@9€0Q“€ @€0b¢$ @€0DÆD @€0ÈŠŒ @|€0ñQ“ÿà €@À€0 2b¢&2 € @ÿÿÿÿ“ÿÿÿÿÿü€0üTDÆDT`ÿ ?ÿÿÿÿòÿÿÿÿÿÿÀ?ÿÿÿÿÿÿü€0ÿÿÿÿÿÿÿÿÿÿÿÿÿòÿÿÿÿÿà€0€0€0€0ÿÿÿÿÿÿÿÿÿÿÿÿÿ€0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿü0ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ0000000000000ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿðÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿð A small icon representing a different analysis map has appeared in the network at the location previously occupied by the BivariateRegression and Scatterplot objects. This new node can be interacted with in the same fashion as any other node. Indeed, the displayed node has already been named DeadEnd by the analyst. Zooming in on this icon produces the following analysis map. LÌ ªÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿþ@þÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþœÿÿþOÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþžÿþOÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþž`áàOÀàÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþžNLÎ@ÎNÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþžN|ÎOÎNÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþž@`ÎOÎNÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþžOÌÎOÎNÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþœÎLÎOÎNÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþàâ`@N`ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþóÀ˜``›c8û8ùæg¾xóÙçÀócÛc6lÙ¶Í–[6`›6<ÃÃ>9æg°yç™æ`€€ @@@€€ @@@€€ ÿÿÿÿÿÿÿÿÿÿÿ€€€€€€ÿÿÿÿÿÿÿÿþ€"€"€ú€"€"€€€€€€€€€€€€€€€€€€€€€€€€€Ã€€@ €€G