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sts also need to compare revenue/cost/depreciation before and after the replacement to identify changes in these elements. Expansion projects. Projects concerning expansion into new products, services, or markets involve <span>strategic decisions and explicit forecasts of future demand, and thus require detailed analysis. These projects are more complex than replacement projects. Regulatory, safety and environmen

ood capital budgeting decisions can be made). Otherwise, you will have the GIGO (garbage in, garbage out) problem. Improve operations, thus making capital decisions well-implemented. <span>Project classifications: Replacement projects. There are two types of replacement decisions: Replacement decisions to maintain a business. The issue is twofold: should the existing operations be continued? If yes, should the same processes continue to be used? Maintenance decisions are usually made without detailed analysis. Replacement decisions to reduce costs. Cost reduction projects determine whether to replace serviceable but obsolete equipment. These decisions are discretionary and a detailed analysis is usually required. The cash flows from the old asset must be considered in replacement decisions. Specifically, in a replacement project, the cash flows from selling old assets should be used to offset the initial investment outlay. Analysts also need to compare revenue/cost/depreciation before and after the replacement to identify changes in these elements. Expansion projects. Projects concerning expansion into new products, services, or markets involve strategic decisions and explicit forecasts of future demand, and thus require detailed analysis. These projects are more complex than replacement projects. Regulatory, safety and environmental projects. These projects are mandatory investments, and are often non-revenue-producing. Others. Some projects need special considerations beyond traditional capital budgeting analysis (for example, a very risky research project in which cash flows cannot be reliably forecast). LOS a. describe the capital budgeting process and distinguish among the various categories of capital projects; <span><body><html>

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A company grows by making investments that are expected to increase revenues and profits

A company grows by making investments that are expected to increase revenues and profits. The company acquires the capital or funds necessary to make such investments by borrowing or using funds from owners. By applying this capital to investments with long-term benefits, t

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An unlimited funds environment assumes that the company can raise the funds it wants for all profitable projects simply by paying the required rate of return.

the first project or new economic conditions are favorable. If the results of the first project or new economic conditions are not favorable, you do not invest in the second project. Unlimited funds versus capital rationing —<span>An unlimited funds environment assumes that the company can raise the funds it wants for all profitable projects simply by paying the required rate of return. Capital rationing exists when the company has a fixed amount of funds to invest. If the company has more profitable projects than it has funds for, it must allocate the funds to achieve

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Overall, the functions of profit are as follows: Rewards entrepreneurs for risk taking when pursuing business ventures to satisfy consumer demand. Allocates resources to their most-efficient use; input factors flow from sectors with economic losses to sectors with economic profit, where profit reflects goods most desired by society. Spu

Total variable cost divided by quantity; (TVC ÷ Q) Average total cost (ATC) Total cost divided by quantity; (TC ÷ Q) or (AFC + AVC) Marginal cost (MC) Change in total cost divided by change in quantity; (∆TC ÷ ∆Q) <span>3.1. Profit Maximization In free markets—and even in regulated market economies—profit maximization tends to promote economic welfare and a higher standard of living, and creates wealth for investors. Profit motivates businesses to use resources efficiently and to concentrate on activities in which they have a competitive advantage. Most economists believe that profit maximization promotes allocational efficiency—that resources flow into their highest valued uses. Overall, the functions of profit are as follows: Rewards entrepreneurs for risk taking when pursuing business ventures to satisfy consumer demand. Allocates resources to their most-efficient use; input factors flow from sectors with economic losses to sectors with economic profit, where profit reflects goods most desired by society. Spurs innovation and the development of new technology. Stimulates business investment and economic growth. There are three approaches to calculate the point of profit maximization. First, given that profit is the difference between total revenue and total costs, maximum profit occurs at the output level where this difference is the greatest. Second, maximum profit can also be calculated by comparing revenue and cost for each individual unit of output that is produced and sold. A business increases profit through greater sales as long as per-unit revenue exceeds per-unit cost on the next unit of output sold. Profit maximization takes place at the point where the last individual output unit breaks even. Beyond this point, total profit decreases because the per-unit cost is higher than the per-unit revenue from successive output units. A third approach compares the revenue generated by each resource unit with the cost of that unit. Profit contribution occurs when the revenue from an input unit exceeds its cost. The point of profit maximization is reached when resource units no longer contribute to profit. All three approaches yield the same profit-maximizing quantity of output. (These approaches will be explained in greater detail later.) Because profit is the difference between revenue and cost, an understanding of profit maximization requires that we examine both of those components. Revenue comes from the demand for the firm’s products, and cost comes from the acquisition and utilization of the firm’s inputs in the production of those products. 3.1.1. Total, Average, and Marginal Revenue This section briefly examines demand and revenue in preparation for addressing cost. Unless the firm is a pu

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The singular key to a successful conversation is comfort.

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Chapter 8. Rehearse only your conversational bookends.

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iplicas por 100. Ese porcentaje lo multiplicas por el costo estimado Step 2: Current Revenue: divides el costo acumulado en ese año, lo divides entre el costo estimado total y lo multiplicas por el precio del contrato. <span>Step 3:Profit=current revenue-Current cost<span><body><html>

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. The tax treatment of all non-cash items is the same as that of other items in the books. There are no differed taxes incurred. Calculate the FCFF for Proust for the year. Solution <span>NCC = Depreciation + non-cash restructuring charges - Cash expenses during the year in which they are capitalized = 130 + 30 - 200 = -$40 million FCFF = NI + NCC + Int (1 - Tax rate) - FCInv - WCInv = 250 + (-40) + 50 (1 - 0.3) - 20 - 100 = $125 million FCFF

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; Free cash flow = CFO - capital expenditure Free Cash Flow to the Firm (FCFF): Cash available to shareholders and bondholders after taxes, capital investment, and WC investment. <span>FCFF = NI + NCC + Int (1 - Tax rate) - FCInv - WCInv NI: Net income available to common shareholders. It is the company's earnings after interest, taxes and preferred dividends. NCC: Net non-cash

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; Free cash flow = CFO - capital expenditure Free Cash Flow to the Firm (FCFF): Cash available to shareholders and bondholders after taxes, capital investment, and WC investment. <span>FCFF = NI + NCC + Int (1 - Tax rate) - FCInv - WCInv NI: Net income available to common shareholders. It is the company's earnings after interest, taxes and preferred dividends. NCC: Net non-cash

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The nominal risk-free rate of return includes both the real risk-free rate of return and the expected rate of inflation. A decrease in expected inflation rate would decrease the nominal risk-free rate of return, but would have no effect on the real risk-free rate of return.

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ays a frequency distribution. It is constructed as follows: The class frequencies are shown on the vertical (y) axis (by the heights of bars drawn next to each other). The classes (intervals) are shown on the horizontal (x) axis. <span>There is no space between the bars. From a histogram, we can see quickly where most of the observations lie. The shapes of histograms will vary, depending on the choice of the size of the interva

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; The relative frequency for a class is calculated by dividing the number of observations in a class by the total number of observations and converting this figure to a percentage (multiplying the fraction by 100). <span>Simply, relative frequency is the percentage of total observations falling within each interval. It is another way of analyzing data; it tells us, for each class, what proportion (or percentage) of data falls in that class. Let's look at an example. The fo

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Construction of a Frequency Distribution. Sort (in ascending order) Calculate the range (Range = Maximum value − Minimum value) Intervals creation (decide the number you will put in the frequency distribution, k.)

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Construction of a Frequency Distribution. Sort (in ascending order) Calculate the range (Range = Maximum value − Minimum value) Intervals creation (decide the number you will put in the frequency distribution, k.) Width determination ( interval width = Range/k.) Ad

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Another distance measure of dispersion that we may encounter, the interquartile range, focuses on the middle rather than the extremes. The interquartile range (IQR) is the difference between the third and first quartiles of a data set: IQR = Q 3 − Q 1 . The IQR represents the length of the interval containing the mi

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t;:"","st":"Math Goodies","th":98,"tu":"https://encrypted-tbn0.gstatic.com/images?q\u003dtbn:ANd9GcQcW8yGVex34TZK0u33zmGPrJUYi4Sw9vz1DI4e1YYbbQn3bEVPNVAGcXN2","tw":216} <span>In probability theory, the sample space of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes are listed as elements in the set. Sample space - Wikipedia https://en.wikipedia.org/wiki/Sample_space Feedback About this result People also ask What is the sample space in statistics? A sample space

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t":"Math Goodies","th":98,"tu":"https://encrypted-tbn0.gstatic.com/images?q\u003dtbn:ANd9GcQcW8yGVex34TZK0u33zmGPrJUYi4Sw9vz1DI4e1YYbbQn3bEVPNVAGcXN2","tw":216} In probability theory, <span>the sample space of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes are listed as elements in the set. Sample space - Wikipedia https://en.wikipedia.org/wiki/Sam

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#edx-probability

Putting together a probabilistic Model that is, a model of a random phenomenon or a random experiment involves two steps. First step, we describe the possible outcomes of the phenomenon or experiment of interest. Second step, we describe our beliefs about the likelihood of the different possible outcomes by specifying a probability law. Here, we start by just talking about the first step, namely, the description of the possible outcomes of the experiment. So we carry out an experiment. For example, we flip a coin. Or maybe we flip five coins simultaneously. Or maybe we roll a die. Whatever that experiment is, it has a number of possible outcomes, and we start by making a list of the possible outcomes-- or, a better word, instead of the word "list", is to use the word "set", which has a more formal mathematical meaning. So we create a set that we usually denote by capital omega. That set is called the sample space and is the set of all possible outcomes of our

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Putting together a probabilistic Model that is, a model of a random phenomenon or a random experiment involves two steps. First step, we describe the possible outcomes of the phenomenon or experiment of interest. Second step, we describe our beliefs about the

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https://en.wikipedia.org/wiki/Mathematical_model","th":120,"tu":"https://encrypted-tbn0.gstatic.com/images?q\u003dtbn:ANd9GcSuTdVRcwZOxMfups7ajRrrK7-0wG9HlGQMOjH29i6tYgAjzXEg6rJKMUgH","tw":200} <span>A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. ... A model may help to explain a system and to study the effects of different components, and to make pr

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Putting together a probabilistic Model that is, a model of a random phenomenon or a random experiment involves two steps. First step, we describe the possible outcomes of the phenomenon or experiment of interest. Second step, we describe our beliefs about the likelihood of the different possible outcomes by specifying a probability law. Here, we start by just talking about the first step, namely, the description of the possible outcomes of the experiment. So we carry out an experiment. For example

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e first step, namely, the description of the possible outcomes of the experiment. So we carry out an experiment. For example, we flip a coin. Or maybe we flip five coins simultaneously. Or maybe we roll a die. <span>Whatever that experiment is, it has a number of possible outcomes, and we start by making a list of the possible outcomes-- or, a better word, instead of the word "list", is to use the word "set", which has a more formal mathematical meaning. So we create a set that we usually denote by capital omega. That set is called the sample

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ng a list of the possible outcomes-- or, a better word, instead of the word "list", is to use the word "set", which has a more formal mathematical meaning. So we create <span>a set that we usually denote by capital omega. That set is called the sample space and is the set of all possible outcomes of our experiment. The elements of that set should have certa

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rd, instead of the word "list", is to use the word "set", which has a more formal mathematical meaning. So we create a set that we usually denote by capital omega. <span>That set is called the sample space and is the set of all possible outcomes of our experiment. The elements of that set should have certain properties. Namely, the elements should be mutually exclusive and collectively exhaust

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ematical meaning. So we create a set that we usually denote by capital omega. That set is called the sample space and is the set of all possible outcomes of our experiment. <span>The elements of that set should have certain properties. Namely, the elements should be mutually exclusive and collectively exhaustive. What does that mean? Mutually exclusive means that, if at the end of the experiment, I tell you that this outcome happened, then it shoul

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rder to play with that theory, or maybe check it out, and so on, then, in such a case, you might want to work with the second sample space. This is a common feature in all of science. <span>Whenever you put together a model, you need to decide how detailed you want your model to be. And the right level of detail is the one that captures those aspects that are relevant and of interest to you.<span><body><html>

#edx-probability

MITx Video | 6.041x | Spring 2014 | Video transcript | L01-2: Sample space examples Let us now look at some examples of sample spaces. Sample spaces are sets. And a set can be discrete, finite, infinite, continuous, and so on. Let us start with a simpler case in which we have a sample space that is discrete and finite. The particular experiment we will be looking at is the following. We take a very special die, a tetrahedral die. So it's a die that has four faces numbered from 1 up 4. We roll it once. And then we roll it twice [again]. Were not dealing here with two probabilistic experiments. We're dealing with a single probabilistic experiment that involves two rolls of the die within that experiment. What is the sample space of that experiment? Well, one possible representation is the following. We take note of the result of the first roll. And then we take note of the result of the second roll. And this gives us a pair of numbers. Each one of the possible pairs of numbers corresponds to one of th

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MITx Video | 6.041x | Spring 2014 | Video transcript | L01-2: Sample space examples Let us now look at some examples of sample spaces. Sample spaces are sets. And a set can be discrete, finite, infinite, continuous, and so on. Let us start with a simpler case in which we have a sample space that is discrete and finite. The particul

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MITx Video | 6.041x | Spring 2014 | Video transcript | L01-2: Sample space examples Let us now look at some examples of sample spaces. Sample spaces are sets. And a set can be discrete, finite, infinite, continuous, and so on. Let us start with a simpler case in which we have a sample space that is discrete and finite. The particular experiment we will be looking at is the following. We take a very

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comes. Now this is a case of a model in which the probabilistic experiment can be described in phases or stages. We could think about rolling the die once and then going ahead with the second roll. So we have two stages. <span>A very useful way of describing the sample space of experiments-- whenever we have an experiment with several stages, either real stages or imagined stages. So a very useful way of describing it is by providing a sequential description in terms of a tree. So a diagram of this kind, we call it a tree. You can think of this as the root of the tree from which you start. And the endpoints of the tree, we usually call 1 them the leaves. So the experiment starts. We carry out the first phase, which in this case is the first roll. And we see what happens. So maybe we get a 2 in the first roll. And then we take

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#reading-9-probability-concepts

The complement of event A is the event that A does not occur. It is expressed as A c . The probabilities of an event and its complement must have a sum of 1: P(A) + P(A c ) = 1. Note that event A and its complement, A c , are mutually exclusive (there is no overlapping of their possible outcomes) and exhaustive (these two events include all possible outcomes). For example, event A refers to an increase in interest rates. The probability of event A is 0.7. A c is the event that interest rates will not increase. P(A c ) = 1 - 0.7 = 0.3. Probabilities are either unconditional or conditional. Unconditional probability, also called marginal probability, is simply the probability of an event occurring. It refers to the probability of an event that is not conditioned on the occurrence of another event. For example, what is the probability that a stock earns a return above the risk-free rate? An unconditional probability can be considered as a stand-alone probability. It is expressed as P(A).

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The complement of event A is the event that A does not occur. It is expressed as A c . The probabilities of an event and its complement must have a sum of 1: P(A) + P(A c ) = 1. Note that event A and its complement, A c , are mutually exclusive (t

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The complement of event A is the event that A does not occur. It is expressed as A c . The probabilities of an event and its complement must have a sum of 1: P(A) + P(A c ) = 1. Note that event A and its complement, A c , are mutually exclusive (there is no overlapping

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

total more than 8 (6,3; 6,4; 6,5; 6,6). The probability of a total greater than 8, given that the first dice is 6, is therefore 4/6 = 2/3. More formally, this probability can be written as: P(total>8 | Dice 1 = 6) = 2/3. In this equation, <span>the expression to the left of the vertical bar represents the event and the expression to the right of the vertical bar represents the condition. Thus, it would be read as "The probability that the total is greater than 8, given that Dice 1 is 6, is 2/3." In more abstract form, P(A|B) is the probability of event A given

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

nction of two random variables, X and Y, gives the probability of joint occurrences of the values of X and Y. If we know the conditional probability P(A|B) and we want to know the joint probability P(AB), we can use the following <span>multiplication rule for probabilities: P(AB) = P(A|B) x P(B) Example 1 If someone draws a card at random from a deck and then, without replacing the first card, draws a second card, what is the probability that both card

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started reading on | finished reading on |

oil price, P(B), is 0.4. The probability of an increase in airfare given an increase in oil price, P(A|B), is 0.3. The joint probability of an increase in both oil price and airfare, P(AB), is 0.3 x 0.4 = 0.12. Hint: <span>Look out for the words "given that" or "you are told that," which will help you know that the probability is conditional. In the absence of such information, the probability will be unconditional. The letter after the | is the event that we know has definitely occurred, whereas the letter before the | is the event whose probability we are trying to calculate. <span>

status | not read | reprioritisations | ||
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last reprioritisation on | reading queue position [%] | |||

started reading on | finished reading on |

Hint: Look out for the words "given that" or "you are told that," which will help you know that the probability is conditional. In the absence of such information, the probability will be unconditional. <span>The letter after the | is the event that we know has definitely occurred, whereas the letter before the | is the event whose probability we are trying to calculate. <span><body><html>