VIVID WORKSHOP DATA ATI V.11.2 Q3 13
Latrisha Adan
Description:
Vivid Workshop Data-oriented electrical motor diagnostics mostlyEuropean cars, includes wiring diagrams Engine ECU, appearance, anda brief description of sensors and other electrical components andignition systems, fuel injection, trace on the specified elements,as well as selected information on themechanics
The company Vivid Automotive Data & Media BV Bright - a leadingdeveloper and supplier of software for the automotive aftermarketin the European service centers. The company's goal is to developinnovative and fully integrated solutions that actually reduce thecost of maintenance costs "iron horses."
In December 2006, was officially presented to the flagship productof Vivid WorkshopData ATI, who for five years, updated with updatesand is currently installed and used in more than 25,000 servicesand workshops throughout Europe.
Vivid WorkshopData ATI is based on technical data base, which isEurope's largest repository of these vehicles, and includesimportant information for maintenance and repair, adjustment data,technical drawings and data engine management systems. The softwarepackage is intuitive and user-friendly interface. One license canbe used for stand-alone application or in multiplayer mode with thepossibility of parallel operation of fourusers.
Every six months, Vivid WorkshopData ATI updated with new cars, newfeatures and additional data. Members receive free updates are. Thequality of the data they contain is unparalleled, with standardizeddata for your convenience.
- Data are based solely on the data of the original manufacturers(OEM).
- The database includes all available modern cars presented at theEuropean market.
Vivid WorkshopData ATI - it's more than just a database filled withreliable information and data quality. It is also easy to useapplication that will help you with efficient and economicalmaintenance of vehicles. Search for data maintenance car is quickand easy, which makes it possible to calculate the standard timefor the job.
Key features and benefits Vivid WorkshopDataATI:
- Intuitive: clear structure and simple navigationscheme.
- Compact: all the information about the car is available with 7buttons.
- Fast: all the information necessary for the correct diagnosis canbe obtained with a single button.
- Simple: the search for the required manufacturer, model and typeof vehicle on one single criterion.
- Smart: the program stores in memory information about the last 10cars.
- Convenient: clearly layed overview page to quickly get theinformation you need.
- Simple: Print forms and reports for yourconvenience.
New in Vivid WorkshopData ATI:
Electronics Smart Assistant (Consultant onElectronic)
Automotive electronics is evolving rapidly. To accurately todiagnose faults, auto mechanics now need detailed information aboutthe sophisticated electronic processors (ECM) and relatedcomponents. One scheme, shown on a computer is no longerenough.
Intelligent Module-Assistant Electronics (Electronics SmartAssistant) dramatically improves performance and diagnostics ofelectronic components of cars. Intelligent Assistant Module (SmartAssistant) is easy to use. You can enter codes EOBD, or go directlyto the electronic component. Clear step by step instructions allowyou to accurately diagnose. The database contains thousands ofstandard P-code, error codes, manufacturer and detailed wiringdiagrams, which allows to make a diagnosis quickly andeasily.
Intelligent Module-Assistant Electronics (Electronics SmartAssistant) is integrated into the system or application isavailable as a separate module.
TO Management System: Tools for MOT cars
The new version predlaget operational information for carrying outMOT cars and trucks. Below are the main functions of themodule:
- Browse all jobs THEN the period
- For each system, we offer an overview table showing all possibleassignments TO on the same page with links to the correspondingperiods of OT.
- Intervals of TO with monthly updates.
- Standardized information about the manufacturer's OTperiods.
- Periods of replacing the timing belt.
- Information zeroing indicator OT.
- Specific maintenance schedules covering the use in the summer,winter and on weekends.
- Lists of works with the ability toprint.
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Vivid Workshop Data ATI combines essential maintenance and repair data with . Software Vivid WorkshopData ATI 2015 Multilanguage. . V- and ribbed belt lay .
VIVID WORKSHOP DATA ATI V.11.2 Q3 . Discografia 1981-2012Acura Navigation System 2011 ORANGE DVD Update v3.A0 2011Vivid WorkshopData ATI v.10.2 Multilanguage .
Although every care has been taken to ensure that the HaynesPro WorkshopData Car Edition data is accurate and complete, no liability other than that which may not be excluded by law, can be
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Plots, graphs and other visual representations of data and central to bioinformatics. Such illustrations summarize year/months of data collection and complex statistical analyzes. The main goal of such visual representations is to improve the observer, reader or researcher his/her understanding of the data and his/her abilities to compare with other datasets and reveal trends in the data collected, compiled or analyzed. However, you might have seen good, bad and ugly graphs. Yet, an effective graph is easier to decode or understand than others, by virtue of this, it is likely to be beautiful.
A) No 3D or overdone plots: Our current format to visualize data is paper or screen (a 2D surfaces) by adding another dimension makes information difficult to visualize. Likewise, eliminating clutter and de-emphasizing supporting elements make reduce the attention exhaustion of the reader (i.e., every aspect of a figure should add if needed). Suggestions: Try to keep graphics simple and make most of them as 2D, no shadows or excess ornamentation.
B) No Pie Charts: This type of infographic is probably the least effective when encoding quantitative data (e.g., percentages or fractions) because pie charts use angles and areas, which are very hard for visualize or compare. Suggestions: Use bar charts and dotplot for proportions and percentages. These graphs make comparisons easier by positioning objects along a common scale.Source
C) Careful about scale of things: Some data need to be scaled or transformed (e.g., logarithms) before plotting to allow the reader to visualize the data distribution. For example, some variables show a multiplicative or exponential scales that jumps from tens to hundreds or thousands. Explore you data first and determine if it needs to be transformed.
D) Avoid Microsoft Excel default plots: Most people use Excel at some point to store their data, then comes the temptation to use their reduced offer of plots and charts. In general, these tend to be plain and less customizable than those derived from R graphics. Suggestion: You already are taking this workshop.
E) Careful with the figure legend: Most graphics need a legend when they are in paper, reports or other divulgation source as these images should stand alone from the main text of the article. Then, the accompanying legend should not contradict what the graphic is showing (i.e., self-contradiction). This is a common mistake and can be used against your paper and thesis by reviewers that will consider that your work is careless and sloppy. Suggestions Always proofread your manuscripts.
F) Make simple tables: Tables also should be easy to read with simple formats (use minimal or no grid lines). The information should be visible and no more three decimal places. Remember that p-values cannot be zero (i.e., probability never is equal to 0), instead use p < 0.001 if a value is really small or use scientific notation if such tiny p-value is of relevance.
G) Other Plots to avoid: Radar charts that make comparisons of data points very difficult as they are placed in a circle, the middle portion stacks too many contrasts and there is a disproportionate emphasis of high numbers (i.e., middle or low values are less visible). Use stack bars or bar charts.
The preloaded graphics package of R helps the easy access plots, bar charts, boxplots and other. However, with the development of the ggplot2 package that is part of the tidyverse collection. This package and its growing collection of add-ons, which include at least 133 registered extensions available to explore, has made ggplot2 a dominant, flexible, and beautiful R graphics package. Therefore, we will explore graphics in R using this package. Like most visualizations, it would be ideal to start with installing ggplot2 and datasets that will serve as examples.
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