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101 Secrets of a Microsoft Excel Addict

This ebook from Francis Hayes gives you professional tools to help you get the most out of your Excel program. Any one of these secrets could be the only one that you ever need to know for Excel, but this ebook includes bunches of those tips and tricks! Just think of all of the useful information you can get from it! If you have ever been frustrated at your lack of progress in Microsoft Excel, this guide will teach you everything that you need to know to harness the powerful functions or time-saving elements of Microsoft Excel. Excel is used by offices all over the world, but so few people take the time to teach you anything important about it. Too much time is usually wasted searching the internet for tips on how to use it more efficiently Learn the best way to master Excel in this ebook!

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Applications to human populations

Recall from Equation 1.9 that if we graph natural log of population growth versus time we can determine the intrinsic rate of increase by finding the slope of the graph. In Fig. 1.6b we have plotted the natural log of human population growth against time. The slope of this line, as determined by the statistical technique of linear regression and computed for us in an Excel spreadsheet, is 0.007. This is the best fit for the intrinsic rate of increase for the human population from 1650 to 2003.

Adding stochasticity to densitydependent models

Just as we did in Chapter 1, we can perform stochastic simulations with density-dependent models. Using the Beverton-Holt model (Eqn. 2.1), Fig. 2.20 shows a deterministic growth curve for an initial population size of 100, a carrying capacity of 1000, and a deterministic value for lambda of 1.1. In order to simulate the effects of demographic stochasticity, we can add a random function in Excel that allows lambda to vary with a mean of 1.1 but with a variance of 0.03. One such result is shown in Fig. 2.20. In this particular case, when the population is small it does not grow very quickly, but it eventually reaches the carrying capacity. Notice that it takes over 100 time units to reach carrying capacity even though the deterministic population reaches K at around 50 time units. Running 25 stochastic simulations in this manner produces a range of population sizes after 100 time units of 128-1000 with a mean of 904 individuals. The lessons are basically the same as in the previous...

Implementing Individual Based Models

Modern personal computers usually have enough memory and power for developing and running IBMs, but vast analyses of parameter space, for example for parametriza-tion, may need the combined power of PC clusters. For analyzing the output of IBMs, many modelers are using other software packages, for example, R, MATLAB, Mathematica, Excel, SPSS, etc.

Urban Transport Systems

To optimize human transport, we must delineate the boundaries between these landscapes and choose transit modes suitable for each. This system does not view an urban journey as a single ride, with cars and public transit in competition the entire way. Rather, we take advantage of the fact that the different modes of transit excel in different parts of the commute cars are unsurpassed as suburban transport, and transit is better suited to higher-density environments. By maximizing the efficiency of each stage of the journey, the system is cumulatively optimized as well.

The Spatial Framework

Or region, large tropical rivers that have an annual flood cycle, flow through low-lying topography and remain largely unregulated, such as the Orinoco and Amazon Rivers of South America, provide excellent case studies. The period of flooding, which includes distinct phases of lateral inundation, throughflow of intermingled river and floodplain water, and drainage, can last several months and be highly predictable in timing and duration (Figure 14.1). In other river systems including those not in the tropics, the timing of flooding may be less predictable and rivers may not overflow their banks, and so the effect is more of a flow pulse'' (Puckridge et al. 1988). Tockner et al. (2000) argue that flood-plains may be more common in the upper and middle reaches of temperate rivers, where floods are shorter and less predictable than in lowland tropical rivers but the expansion and contraction in discharge nonetheless still plays an important ecological role. Species that succeed in a...

Food Web Model Application

Examples of the application of the food-web bioaccumulation for ecotoxicological risk assessment, including the use of forward and backward calculations, include the San Francisco Bay food-web bioaccumulation model. The model is documented in a Gobas and Arnot report listed on the website of the San Francisco Bay Clean Estuary Partnership (CEP), and can be downloaded in the form of a Microsoft EXCEL workbook from http www.rem.sfu.ca. The purpose of this Other applications of the food-web bioaccumulation include the estimation of the BAF and BCF for fish species in lower, middle, and upper trophic levels of aquatic food webs. The model predictions can include the effect of metabolic transformation and trophic dilution on the BAF if a reliable estimate of the chemical's metabolic transformation rate in fish is available. The model is named BAF-QSAR vl.1 and is coded in a Microsoft EXCEL workbook, is freely available for download, and can be run for a large number of chemicals. Food-web...

Survey effort and data collection specific to spatial scale

We recorded field observations with associated geo-referencing data (GPS way-points and tracklogs) on Excel spread sheets for data analyses. We used DISTANCE software (Thomas et al. 2001a, Southwell and Weaver 1993) to determine nest densities based on line transect nest counts. We mapped encounter rates (number of observations per km surveyed) of bonobo and human activity indicators to each survey quadrat using ARC GIS and conducted further spatial analyses via ESRI statistical packages (Mitchell 2005) and other sources. Table 10.1 is a summary of data collection and analytical methods for the three survey phases.

Calculating UEVs

Several methods are used to calculate UEVs they include (1) static calculations, (2) dynamic simulation, and (3) network analysis. Most commonly, static calculations are used for processes where the flows of energy, materials, and services over a particular time period are multiplied by their UEVs, summed, and divided by the available energy, or mass of the product produced during that same time period. Dynamic simulation has been used for some resources that require long periods of time to generate, for instance, forest wood or soils. The dynamic method uses rate of change equations for storages that add emergy as long as the storage of material is accumulating. Evaluating UEVs with a technique of network analysis uses a 'minimum eigenvalue method'. The eigenvalue method can be processed with using linear equations and the EXCEL solver routine. Termed emergy network

Twine in the Baler

Getting serious about sustainability, second, would require a radical reconsideration of the present laissez-faire direction of technology. Many advocates of sustainable development place great faith in the power of technology to improve the efficiency with which energy and resources are used. Better technology may well succeed in doing so, but the same unfettered development of technology has a darker side about which little is said. For example, Marvin Minsky (1994), in a recent issue of Scientific American, asked whether robots will inherit the earth. His answer was an enthusiastic yes. He and others are, accordingly, working hard to deliver us from the limitations of biology, intending to replace human bodies with mechanical surrogates and our brains with devices having the capacity to think a million times faster than we do (Minsky 1994, 112 Moravec 1988). Other knowledgeable observers predict that artificial intelligences will eventually excel us in intelligence and it will be...

A1 Spreadsheets

Almost all small data sets used to illustrate the functioning of methods eventually passed though Excel, for either data input, simple analyses or data presentation. In spreadsheets, data are usually organized as shown in Figure 2.8 that is, in a single data matrix with releves organized in columns and species and environmental variables in rows. For import and export into Plotting scatter diagrams (ordinations). Excel offers sufficient formatting options for graphs and all are set automatically. Subsequent manual formatting is needed as ordination axes x and y must be identically scaled and this is achieved only when manually setting the range of the axes and the width and height of the graphs. Numerically integrating differential equations for dynamic modelling. All models presented in Section 10.1 are implemented in Excel. A column is chosen for each state variable that is, X1, X2, as well as its derivative SX1 St, SX2 St. For time step t 0 the initial values are written in a row...

Spreadsheets

The well-known spreadsheets are probably the most widely known software applications that can help build quite sophisticated models. Microsoft Excel is by far the best-known and widely used spreadsheet. However, there is also Lotus 1-2-3, which actually pioneered the spreadsheet concept and is now owned by IBM, or the open-source OpenOffice suite. The latest addition is Numbers, the Macintosh spreadsheet program. All offer very similar