At $1. 8,9. 99, Could This 1. Chevy Monte Carlo SS Show You The Aero Of Your Ways? Today’s Nice Price or Crack Pipe Monte Carlo is for all intents and purposes practically brand new. Let’s still see if the price on this Aero Coupe ensures that it gets old fast. The general consensus around the $2,5. Kawasaki AR8. 0 cappuccino racer was “for that much I could get so much more.” Of course, we’re forgetting the old adage that good things come in small packages and equally that you don’t always have to supersize everything. In the end that seeming inequitable inadequacy drove the vote to a 6.

Crack Pipe loss. You’re just going to let Kawasaki roll those good times for someone else, I guess. As I noted yesterday, the AR8. Honda MB5. 0. Another attempt to fight the good fight was Chevy’s Monte Carlo Aero Coupe, a special edition of the long- running nameplate sold over the 1.

NASCAR circuit with Ford’s then spiffy new aerodynamic Thunderbird. The changes for the Aero Coupe were focused on the notchback rear end. There, a new shorter trunk lid provided room for an elongated rear window, canted at a 2. Minor rant by the way while we’re back here: if we’re calling front trunks “frunks” shouldn’t we call back trunks “bunks?” Ack, never mind. NASCAR rules demanded 3. Aeros Cars and Concepts of Brighton, MI built for Chevy the first model year. In the next, production was brought in- house and a further 6,0.

By then the writing was already on the well for the body- on- frame RWD G- body platform and for ’8. Watch Red Hook Summer Instanmovie on this page. Monte’s last year—no Aero Coupe was offered. Here we have a 1.

Monte Carlo SS Aero Coupe, in Dark Maroon Metallic with a maroon cloth interior. This being an ‘8. SS badging was more for show than for go, but it’s totally working it.

The engine is an H. O. 3. 05 V8, which puts out a factory- claimed 1. Yes, “High Output” and “1. A 2. 00- 4. R four- speed automatic came standard, and here is operated through a console- mounted shifter. These cars were not fast, they did not handle particularly well, nor were their disc/drum brakes really up to modern standards in stopping prowess. Still, the cars are damn pretty and for Saturday night cruising or a mad dash to Vegas, you could do far worse. You might not want to be taking any long trips in this SS however, as one of its big selling points is its extremely low miles.

That’s 9,7. 86 at present and the car shows in the ad as though it hasn’t travelled even that meager distance. The body, interior, and engine bay all present as- new. In fact, the only flaw seemingly evident is a missing cigar lighter next to the HVAC controls. Also, show of hands, who misses glorious red interiors? Yep, that’s what I thought. Me too. Let’s pour out a 4. Ocean Spray in their memory.

What is Monte Carlo Simulation? Monte Carlo simulation lets you see all the possible outcomes of your decisions and assess the impact of risk, allowing for better.

The SS Aero Coupe (which by the way, would be an excellent name for a ship) is perhaps the most badass looking of all Montes, what with its cool folded back glass and Endura front clip. The chunky alloy wheels add to the aggressive style, and these all look to have never met a curb, a good thing. Also, do you want overhang? Because we’ve got overhang! Car’s today generally lack this once common bit of generous proportioning, much to their loss. The ad says that the paint on the car is “almost perfect” and that “[T]his baby starts up everytime and runs like the day it came off the showroom floor.” There’s no word on whether the low miles were accomplished through judicious use or simply years of having been squirreled away, but either way there doesn’t seem to be any major issue here from lack of use. The price is a cool $1.

Monte Carlo Fashions Stock/Share prices, Monte Carlo Fashions Live BSE/NSE, F&O Quote of Monte Carlo Fashions with Historic price charts for NSE / BSE. Experts. More about Tudor 7149/0 Vintage 7149 / 0 "Monte Carlo" Chronograph FULL SET WOW. This is a c. 1976 vintage stainless steel Tudor Monte Carlo Chronograph, reference.

What we want to know is whether, for someone who’s into such things, this Aero Coupe is worth that $1. With that in mind, what do you think: should someone pay that much cash for this SS? You decide! Cars. Kirkland WA, or go here if the ad disappears.

H/T to Matthew Eccles- Ambrose for the hookup! Help me out with NPOCP. Click here to send a me a fixed- price tip, and remember to include your Kinja handle.

Monte Carlo Simulation: What Is It and How Does It Work? Risk analysis is part of every decision we make. We are constantly faced with uncertainty, ambiguity, and variability. And even though we have unprecedented access to information, we can’t accurately predict the future. Monte Carlo simulation (also known as the Monte Carlo Method) lets you see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty.

We will first review the Monte Carlo principle and standard Monte Carlo methods including rejection sampling, importance sampling and standard Markov chain Monte. Buy Bruno Mars tickets at Park Theater at Monte Carlo Resort and Casino from the official Ticketmaster.com site. Buy Park Theater at Monte Carlo Resort and Casino tickets at Ticketmaster.com. Find Park Theater at Monte Carlo Resort and Casino venue concert and event schedules. Today’s Nice Price or Crack Pipe Monte Carlo is for all intents and purposes practically brand new. Let’s still see if the price on this Aero Coupe ensures that. Watch First Knight Online Gorillavid on this page.

Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment.

Monte Carlo simulation furnishes the decision- maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows the extreme possibilities—the outcomes of going for broke and for the most conservative decision—along with all possible consequences for middle- of- the- road decisions. The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems. Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. Watch Planet Of Dinosaurs Online there. It then calculates results over and over, each time using a different set of random values from the probability functions.

Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values. By using probability distributions, variables can have different probabilities of different outcomes occurring.  Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis.  Common probability distributions include: Normal – Or “bell curve.”  The user simply defines the mean or expected value and a standard deviation to describe the variation about the mean.  Values in the middle near the mean are most likely to occur.  It is symmetric and describes many natural phenomena such as people’s heights.  Examples of variables described by normal distributions include inflation rates and energy prices. Lognormal – Values are positively skewed, not symmetric like a normal distribution.  It is used to represent values that don’t go below zero but have unlimited positive potential.  Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.

Uniform – All values have an equal chance of occurring, and the user simply defines the minimum and maximum.  Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product. Triangular – The user defines the minimum, most likely, and maximum values.  Values around the most likely are more likely to occur.  Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels. PERT- The user defines the minimum, most likely, and maximum values, just like the triangular distribution.  Values around the most likely are more likely to occur.  However values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized.  An example of the use of a PERT distribution is to describe the duration of a task in a project management model. Discrete – The user defines specific values that may occur and the likelihood of each.  An example might be the results of a lawsuit: 2. During a Monte Carlo simulation, values are sampled at random from the input probability distributions.  Each set of samples is called an iteration, and the resulting outcome from that sample is recorded.  Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes.  In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen.  It tells you not only what could happen, but how likely it is to happen. Monte Carlo simulation provides a number of advantages over deterministic, or “single- point estimate” analysis: Probabilistic Results.

Results show not only what could happen, but how likely each outcome is. Graphical Results. Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence.  This is important for communicating findings to other stakeholders. Sensitivity Analysis. With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most.  In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom- line results. Scenario Analysis: In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios.  Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred.  This is invaluable for pursuing further analysis.

Correlation of Inputs. In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables.  It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly. An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the entire range of distribution functions. The advent of spreadsheet applications for personal computers provided an opportunity for professionals to use Monte Carlo simulation in everyday analysis work. Microsoft Excel is the dominant spreadsheet analysis tool and Palisade’s @RISK is the leading Monte Carlo simulation add- in for Excel. First introduced for Lotus 1- 2- 3 for DOS in 1.

RISK has a long- established reputation for computational accuracy, modeling flexibility, and ease of use. The introduction of Microsoft Project led to another logical application of Monte Carlo simulation—analyzing the uncertainties and risks inherent to the management of large projects. RISK is also used for project management. Read more about risk analysis.