...All-In-One / CompTIA Network+ All-in-One Exam Guide / Meyers & Jernigan / 170133-8 / Appendix A A PPENDIX Mapping to the CompTIA A+ Objectives A CompTIA A+ Essentials Objectives Map Topic Chapter(s) Domain 1.0 Hardware 1.1 Categorize storage devices and backup media FDD 3 HDD 3, 11 Solid state vs. magnetic Optical drives CD / DVD / RW / Blu-Ray Removable storage 11 3, 13 3, 13 11, 13, 17 Tape drive 17 Solid state (e.g. thumb drive, flash, SD cards, USB) 13 External CD-RW and hard drive 13, 11 Hot swappable devices and non-hot swappable devices 13 1.2 Explain motherboard components, types and features Form Factor 9 ATX / BTX, 9 micro ATX 9 NLX 9 I/O interfaces 3, 18, 20, 22, 23, 25 Sound 3, 20 Video 3 1219 AppA.indd 1219 12/9/09 5:58:26 PM All-In-One / CompTIA Network+ All-in-One Exam Guide / Meyers & Jernigan / 170133-8 / Appendix A CompTIA A+Certification All-in-One Exam Guide 1220 Topic Chapter(s) USB 1.1 and 2.0 3, 18 Serial 3, 18 IEEE 1394 / Firewire 3, 18 Parallel 3, 22 NIC 3, 23 Modem 3, 25 PS/2 18 Memory slots 3, 6 RIMM 6 DIMM 3, 6 SODIMM 6 SIMM 6 Processor sockets 3, 5, 9 Bus architecture 5, 8 Bus slots 8, 9, 21 PCI 8, 9 AGP 8, 9 PCIe 8, 9 AMR 9 CNR 9 PCMCIA 21 PATA 11 IDE 11 EIDE 11 SATA, eSATA ...
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...Saying please and showing gratitude is an action children preform in order to receive what they desire. At this point in life, no child understands why they say please or show gratitude, but they learned to say please obtain what they desire. These minuscule colloquies display an unequal distribution of power. Furthermore, a person being asked “please” has the power in the interaction, because they possess something desired by others. In addition, showing gratitude is recognition of power and showing thankfulness for their kindness. Saying please and showing gratitude serve a function in society. These actions are societal norms for asking for and receiving something and children that do not do these things are deviant to societal norms and are ill mannered....
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..."Distribution and Pricing" Please respond to the following: • Discuss the various ways that distribution adds value (or utility) to a product or service, the impact that wholesalers and retailers have on that value, and how it can be used as a competitive advantage. Strictly speaking, although a wholesaler may own or control retail operations, wholesalers do not sell to end customers. Indeed, many wholesale operations are themselves owned by retailers or manufacturers. Wholesalers are extremely important in a variety of industries, including automobiles, grocery products, plumbing supplies, electrical supplies, and raw farm produce. Wholesaling involves that part of the marketing process in which intermediaries, i.e., those between the producer and end consumer, buy and resell goods, making them available to an expanded buyer's market over an expanded geographical market area. As middle agents, wholesalers are only effective when the price they charge for goods and services is less than the value placed by customers. By facilitating the transfer of title of goods, they are involved in the bulking and distributing of goods. (www.referenceforbusiness.com/encyclopedia/Val-Z/Wholesaling.html#ixzz1nOmy6Yk2 ) • As you market yourself to employers, the salary you demand is essentially your personal price. Keeping that in mind, determine the pricing strategy should you follow (penetration, skimming, or competitive). Explain your rationale. In most cases competitive pricing strategy...
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...SCENARIO ANALYSIS, DECISION TREES AND SIMULATIONS In the last chapter, we examined ways in which we can adjust the value of a risky asset for its risk. Notwithstanding their popularity, all of the approaches share a common theme. The riskiness of an asset is encapsulated in one number – a higher discount rate, lower cash flows or a discount to the value – and the computation almost always requires us to make assumptions (often unrealistic) about the nature of risk. In this chapter, we consider a different and potentially more informative way of assessing and presenting the risk in an investment. Rather than compute an expected value for an asset that that tries to reflect the different possible outcomes, we could provide information on what the value of the asset will be under each outcome or at least a subset of outcomes. We will begin this section by looking at the simplest version which is an analysis of an asset’s value under three scenarios – a best case, most likely case and worse case – and then extend the discussion to look at scenario analysis more generally. We will move on to examine the use of decision trees, a more complete approach to dealing with discrete risk. We will close the chapter by evaluating Monte Carlo simulations, the most complete approach of assessing risk across the spectrum. Scenario Analysis The expected cash flows that we use to value risky assets can be estimated in one or two ways. They can represent a probability-weighted average of cash flows...
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...Hypothesis Testing and EViews p-values: Suppose that we want to test a null hypothesis about a single parameter using its estimated value (for example a mean or a regression coefficient). We can do so using a t-test. To begin, suppose that the parameter to be estimated is β. We must first specify a null hypothesis and an alternative hypothesis. 2 tail test: For a two tailed test, we want to test whether β is a particular value or not. We first set the value of β that we want to test. We’ll call this β0 to indicate that this will be the value of β under the null hypothesis. In a two tail test, the null and alternative hypotheses are: H0 : β = β 0 HA : β = β0 ˆ We proceed by estimating β. We denote the estimated value as β. This could for example be a sample mean estimate of the population mean, a least squared estimate of a regression coefficient, or a maximum likelihood estimate of a model coefficient, ˆ depending on the context. The estimate β is usually accompanied by a standard error ˆ to indicate how precisely it is estimated. We denote this standard error as se(β). This ˆ is a random variable with a sampling distribution. It will have reflects the fact the β different values in different samples. We can then form the following test statistic by computing the standardised statistic ˆ whereby we subtract the hypothesisized value β0 from the estimate β and divide by its standard error: t-stat = ˆ β − β0 ˆ se(β) ˆ Again, this test statistic is a random variable since it depends...
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...1.10 Sampling Distributions The main objective of most statistical inference is to draw conclusion about the population parameters based on samples studies that is quite small in comparison to the size of the population. In order that conclusion of sampling theory and statistical inference valid, samples must be chosen so as to the representation of a population. For example, Television executives want to know the proportion of television viewers who watch that network’s program. Particularly determining the proportions that are watching certain programs is impractical and prohibitively expensive. One possible alternative method can be providing approximation by observing what a sample of 1,000 television viewer’s watch. Thus they estimate the population proportion by calculating the sample proportion. Similarly, the field of quality control illustrates yet another reason for sampling. In order to ensure that a production process is operating properly, the operations manager needs to know the proportion of defective units that are being produced. If the quality-control technician must destroy the unit in order to determine whether or not it is defective, there is no alternative to sampling: a complete inspection of the population would destroy the entire output of the production process. We know that the sample proportion of television viewers or of defective items is probably not exactly equal to the population proportion we want it to estimate. Nonetheless, the sample statistic...
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...PROBABILITY 1. ACCORDING TO STATISTICAL DEFINITION OF PROBABILITY P(A) = lim FA/n WHERE FA IS THE NUMBER OF TIMES EVENT A OCCUR AND n IS THE NUMBER OF TIMES THE EXPERIMANT IS REPEATED. 2. IF P(A) = 0, A IS KNOWN TO BE AN IMPOSSIBLE EVENT AND IS P(A) = 1, A IS KNOWN TO BE A SURE EVENT. 3. BINOMIAL DISTRIBUTIONS IS BIPARAMETRIC DISTRIBUTION, WHERE AS POISSION DISTRIBUTION IS UNIPARAMETRIC ONE. 4. THE CONDITIONS FOR THE POISSION MODEL ARE : • THE PROBABILIY OF SUCCESS IN A VERY SMALL INTERAVAL IS CONSTANT. • THE PROBABILITY OF HAVING MORE THAN ONE SUCCESS IN THE ABOVE REFERRED SMALL TIME INTERVAL IS VERY LOW. • THE PROBABILITY OF SUCCESS IS INDEPENDENT OF t FOR THE TIME INTERVAL(t ,t+dt) . 5. Expected Value or Mathematical Expectation of a random variable may be defined as the sum of the products of the different values taken by the random variable and the corresponding probabilities. Hence if a random variable X takes n values X1, X2,………… Xn with corresponding probabilities p1, p2, p3, ………. pn, then expected value of X is given by µ = E (x) = Σ pi xi . Expected value of X2 is given by E ( X2 ) = Σ pi xi2 Variance of x, is given by σ2 = E(x- µ)2 = E(x2)- µ2 Expectation of a constant k is k i.e. E(k) = k fo any constant k. Expectation of sum of two random variables is the sum of their expectations i.e. E(x +y) = E(x) + E(y) for any two...
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...CHAPTER 6: THE NORMAL DISTRIBUTION AND OTHER CONTINUOUS DISTRIBUTIONS 1. In its standardized form, the normal distribution a) has a mean of 0 and a standard deviation of 1. b) has a mean of 1 and a variance of 0. c) has an area equal to 0.5. d) cannot be used to approximate discrete probability distributions. ANSWER: a TYPE: MC DIFFICULTY: Easy KEYWORDS: standardized normal distribution, properties 2. Which of the following about the normal distribution is NOT true? a) Theoretically, the mean, median, and mode are the same. b) About 2/3 of the observations fall within 1 standard deviation from the mean. c) It is a discrete probability distribution. d) Its parameters are the mean, , and standard deviation, . ANSWER: c TYPE: MC DIFFICULTY: Easy KEYWORDS: normal distribution, properties 3. If a particular batch of data is approximately normally distributed, we would find that approximately a) 2 of every 3 observations would fall between 1 standard deviation around the mean. b) 4 of every 5 observations would fall between 1.28 standard deviations around the mean. c) 19 of every 20 observations would fall between 2 standard deviations around the mean. d) all of the above ANSWER: d TYPE: MC DIFFICULTY: Easy KEYWORDS: normal distribution, properties 4. For some positive value of Z, the probability that a standardized normal variable is between 0 and Z is 0.3770. The value of Z is a) 0.18. b) 0.81. c) 1.16. d) 1...
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...kemsley@yale.edu, (203) 432-3780, fax: (212) 432-6974. ** University of California at Los Angeles, Anderson Graduate School of Management, Box 9511481, Los Angeles, CA 90095-1481, (310) 825-4720, michael.williams@anderson.ucla.edu. Debt, Equity, and Taxes Abstract In this study, we extend Miller’s (1977) capital structure analysis by adding potentially high personal taxes on dividends and share repurchases, and by focusing on mature firms with at least some pre-existing equity. We demonstrate that personal taxes on equity distributions push new equity financing to an inferior corner, but they do not push internal equity (i.e., reinvested free cash flows) to a corner. Therefore an interior capital structure solution remains, in which firms are indifferent between using debt and internal equity financing, while preferring to avoid issuing additional external equity. Interestingly, many attributes of Miller’s model survive high personal taxes on equity distributions, including the aggregate debt-equity ratio, the identity of the marginal investment clientele, and investors’ portfolio allocations between debt and equity securities....
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...Discussion of Skewness | A symmetric distribution is one in which the 2 "halves" of the histogram appear as mirror-images of one another. A skewed (non-symmetric) distribution is a distribution in which there is no such mirror-imaging.For skewed distributions, it is quite common to have one tail of the distribution considerably longer or drawn out relative to the other tail. A "skewed right" distribution is one in which the tail is on the right side. A "skewed left" distribution is one in which the tail is on the left side. The above histogram is for a distribution that is skewed right.Skewed distributions bring a certain philosophical complexity to the very process of estimating a "typical value" for the distribution. To be specific, suppose that the analyst has a collection of 100 values randomly drawn from a distribution, and wishes to summarize these 100 observations by a "typical value". What does typical value mean? If the distribution is symmetric, the typical value is unambiguous-- it is a well-defined center of the distribution. For example, for a bell-shaped symmetric distribution, a center point is identical to that value at the peak of the distribution.For a skewed distribution, however, there is no "center" in the usual sense of the word. Be that as it may, several "typical value" metrics are often used for skewed distributions. The first metric is the mode of the distribution. Unfortunately, for severely-skewed distributions, the mode may be at or near the left or...
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...QUESTION 1 1. For the interval estimation of μ when σ is known and the sample is large, the proper distribution to use is | | the normal distribution | | | the t distribution with n degrees of freedom | | | the t distribution with n + 1 degrees of freedom | | | the t distribution with n + 2 degrees of freedom | 5 points QUESTION 2 1. An estimate of a population parameter that provides an interval of values believed to contain the value of the parameter is known as the | | confidence level | | | interval estimate | | | parameter value | | | population estimate | 5 points QUESTION 3 1. The value added and subtracted from a point estimate in order to develop an interval estimate of the population parameter is known as the | | confidence level | | | margin of error | | | parameter estimate | | | interval estimate | 5 points QUESTION 4 1. Whenever the population standard deviation is unknown and the population has a normal or near-normal distribution, which distribution is used in developing an interval estimation? | | standard distribution | | | z distribution | | | alpha distribution | | | t distribution | 5 points QUESTION 5 1. The z value for a 97.8% confidence interval estimation is | | 2.02 | | | 1.96 | | | 2.00 | | | 2.29 | 5 points QUESTION 6 1. The t value for a 95% confidence interval estimation with 24 degrees of freedom is | | 1.711 | | | 2...
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...| .361 | Range | 42.20 | 77.10 | 88.70 | 76808 | Minimum | 40.20 | 22.90 | 25.50 | 281 | Maximum | 82.40 | 100.00 | 114.20 | 77089 | Percentiles | 10 | 50.1000 | 54.3300 | 45.1000 | 888.00 | | 20 | 57.8000 | 69.6200 | 57.3000 | 1592.00 | | 25 | 62.0000 | 73.7500 | 60.8000 | 1965.00 | | 30 | 64.5000 | 80.0500 | 63.2000 | 2489.00 | | 40 | 68.8000 | 87.5200 | 70.6000 | 4311.00 | | 50 | 71.3000 | 91.2000 | 73.5000 | 6679.00 | | 60 | 72.6000 | 94.6000 | 77.6000 | 9737.00 | | 70 | 74.1000 | 97.8000 | 80.2000 | 13362.00 | | 75 | 75.3000 | 99.1500 | 82.7000 | 17642.00 | | 80 | 76.9000 | 99.6000 | 88.1000 | 22004.00 | | 90 | 79.2000 | 99.9000 | 94.3000 | 34056.00 | The mean is the sum of all observations (values)...
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...C H A P T E R 6 The Normal Distribution Objectives After completing this chapter, you should be able to Outline Introduction 6–1 Normal Distributions 1 2 3 4 5 6 7 Identify distributions as symmetric or skewed. Identify the properties of a normal distribution. Find the area under the standard normal distribution, given various z values. Find probabilities for a normally distributed variable by transforming it into a standard normal variable. Find specific data values for given percentages, using the standard normal distribution. Use the central limit theorem to solve problems involving sample means for large samples. Use the normal approximation to compute probabilities for a binomial variable. 6–2 Applications of the Normal Distribution 6–3 The Central Limit Theorem 6–4 The Normal Approximation to the Binomial Distribution Summary 6–1 300 Chapter 6 The Normal Distribution Statistics Today What Is Normal? Medical researchers have determined so-called normal intervals for a person’s blood pressure, cholesterol, triglycerides, and the like. For example, the normal range of systolic blood pressure is 110 to 140. The normal interval for a person’s triglycerides is from 30 to 200 milligrams per deciliter (mg/dl). By measuring these variables, a physician can determine if a patient’s vital statistics are within the normal interval or if some type of treatment is needed to correct a condition and avoid future illnesses. The question then is,...
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...analysis using appropriate statistical methods allows the researchers to establish correlations between independent and dependent variables, define possible outcomes, and identify areas of potential study in the future accurately. Statistics is important for researchers because it allows them to investigate and interpret the data more accurately, and researchers will notice patterns in the data that would be overlooked otherwise and result in inaccurate and possibly subjective conclusions (Portney &ump; Watkins, 2009). Frequency distribution is a method used in descriptive statistics to arrange the values of one or multiple variables in a sample, so it will summarize the distribution of values in a sample. Frequency distribution is the most basic and frequently used method in statistics because it creates organized tables of data which can be used later to calculate averages or measure variability. The organized data frequency distribution provides continuous data that is easier to...
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...MAT 540 Quiz Answers 1) Deterministic techniques assume that no uncertainty exists in model parameters. Answer: TRUE Diff: 1 Page Ref: 489 Main Heading: Types of Probability Key words: deterministic techniques 2) Probabilistic techniques assume that no uncertainty exists in model parameters. Answer: FALSE Diff: 1 Page Ref: 489 Main Heading: Types of Probability Key words: probabilistic techniques 3) Objective probabilities that can be stated prior to the occurrence of an event are classical or a priori. Answer: TRUE Diff: 2 Page Ref: 489 Main Heading: Types of Probability Key words: objective probabilities, classical probabilities 4) Objective probabilities that are stated after the outcomes of an event have been observed are relative frequencies. Answer: TRUE Diff: 2 Page Ref: 489 Main Heading: Types of Probability Key words: relative frequencies 5) Relative frequency is the more widely used definition of objective probability. Answer: TRUE Diff: 1 Page Ref: 490 Main Heading: Types of Probability Key words: relative frequencies 6) Subjective probability is an estimate based on personal belief, experience, or knowledge of a situation. Answer: TRUE Diff: 2 Page Ref: 490 Main Heading: Types of Probability Key words: subjective probability 7) An experiment is an activity that results in one of several possible outcomes. Answer: TRUE Diff: 1 Page Ref: 491 Main Heading: Fundamentals of Probability Key words: experiment ...
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