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an>We assume that when comparing any three distinct bundles, A, B, and C, if A is preferred to B, and simultaneously B is preferred to C, then it must be true that A is preferred to C. This assumption is referred to as the assumption of transitive preferences , and it is assumed to hold for indifference as well as for strict preference.<span><body><html>

that about his two children. In effect, the father neither prefers one to the other nor is, in any meaningful sense, indifferent between the two. The assumption of complete preferences cannot accommodate such a response. Second, <span>we assume that when comparing any three distinct bundles, A, B, and C, if A is preferred to B, and simultaneously B is preferred to C, then it must be true that A is preferred to C. This assumption is referred to as the assumption of transitive preferences , and it is assumed to hold for indifference as well as for strict preference. This is a somewhat stronger assumption because it is essentially an assumption of rationality. We would say that if a consumer prefers a skiing holiday to a diving holiday and a diving

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to the business that supplies the item. When demand increases from Demand 1 to Demand 2 , price rises to P 2 , where at this higher price level economic rent is created. The amount of this economic rent is calculated as <span>(P 2 – P 1 ) × Q 1 . The firm has not done anything internally to merit this special reward: It benefits from an increase in demand in conjunction with a supply curve that does not fully adjust with an in

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The most common example of a regulated monopoly is the local electrical power provider. In most cases, the monopoly power provider is allowed to earn a normal return on its investment and prices are set by the regulatory authority to allow that return.

one of the carriers changes its pricing package, others will likely retaliate. Understanding the market structure of oligopoly markets can help in identifying a logical pattern of strategic price changes for the competing firms. <span>Finally, the least competitive market structure is monopoly . In pure monopoly markets, there are no other good substitutes for the given product or service. There is a single seller, which, if allowed to operate without constraint, exercises considerable power over pricing and output decisions. In most market-based economies around the globe, pure monopolies are regulated by a governmental authority. The most common example of a regulated monopoly is the local electrical power provider. In most cases, the monopoly power provider is allowed to earn a normal return on its investment and prices are set by the regulatory authority to allow that return. 2.2. Factors That Determine Market Structure Five factors determine market structure: The number and relative size of firms

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merger or acquisition candidate. Evaluating a subsidiary or operating division of a parent company. Deciding whether to make a venture capital or other private equity investment. Determining the <span>creditworthiness of a company in order to decide whether to extend a loan to the company and if so, what terms to offer. <span><body><html>

ing, and financing decisions but do not necessarily rely on analysis of related financial statements. They have access to additional financial information that can be reported in whatever format is most useful to their decision.) <span>In evaluating financial reports, analysts typically have a specific economic decision in mind. Examples of these decisions include the following: Evaluating an equity investment for inclusion in a portfolio. Evaluating a merger or acquisition candidate. Evaluating a subsidiary or operating division of a parent company. Deciding whether to make a venture capital or other private equity investment. Determining the creditworthiness of a company in order to decide whether to extend a loan to the company and if so, what terms to offer. Extending credit to a customer. Examining compliance with debt covenants or other contractual arrangements. Assigning a debt rating to a company or bond issue. Valuing a security for making an investment recommendation to others. Forecasting future net income and cash flow. These decisions demonstrate certain themes in financial analysis. In general, analysts seek to examine the past and current performance and financial position of a

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For the most recent financial reporting period, a business domiciled in Ecuador (which recognizes the US dollar as an official currency) has revenue of $2 million and total costs of $2.5 million, which are or can be broken down into total fixed cost of $1 million and total variable cost of $1.5 million. The net loss on the firm’s income statement is reported as $500,000 (ignoring tax implications). In prior periods, the firm had reported profits on its operations. What decision should the firm make regarding operations over the short term? What decision should the firm make regarding operations over the long term? Assume the same business scenario except that revenue is now $1.3 million, wh

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s point of view, cash flows from operation activities have two major drawbacks: CFO does not include charges for the use of long-lived assets. Recall that depreciation is added back to net income in arriving at CFO. <span>CFO does not include cash outlays for replacing old equipment. Free Cash Flow (FCF) is intended to measure the cash available to a company for discretionary uses after making all required cash outlays. It accoun

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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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; It adjusts each item in the income statement to its cash equivalent. It shows operating cash receipts and payments. More cash flow information can be obtained and it is more easily understood by the average reader. <span>The indirect method reconciles net income to net cash flow from operating activities by adjusting net income for all non-cash items and the net changes in the operating working capital accounts. It shows why net income and operating cash flows differ. It is used by most companies. The direct and indirect methods are alternative formats for reporti

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t is the total return, or holding period return (HPR). HPR measures the total return for holding an investment over a certain period of time, and can be calculated using the following formula: <span>P t = price per share at the end of time period t P (t-1) = price per share at the end of time period t-1, the time period immediately preceding time period t P t - P t-1 = price appreciation of the investment D t = cash distributions received during time period t: for common stock, cash distribution is the dividend; for bonds, cash distribution is the coupon payment. It has two important characteristics: It has an element of time attached to it: monthly, quarterly or annual returns. HPR can be computed for any time period. It has n

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3; Nominal Scale Nominal measurement represents the weakest level of measurement. It consists of assigning items to groups or categories. No quantitative information is conveyed and no ordering (ranking) of the items is implied. <span>Nominal scales are qualitative rather than quantitative. Religious preference, race, and sex are all examples of nominal scales. Another example is portfolio managers categorized as value or growth style will have a scale of 1 f

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e assumed to refer to the arithmetic mean. The mean is the sum of all scores divided by the number of scores. It is used to measure the prospective (expected future) performance (return) of an investment over a number of periods. <span>All interval and ratio data sets (e.g., incomes, ages, rates of return) have an arithmetic mean. All data values are considered and included in the arithmetic mean computation. A data set has only one arithmetic mean. This indicates that the mean is unique. The arithmetic mean is t

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etic mean is the only measure of central tendency where the sum of the deviations of each value from the mean is always zero. Deviation from the arithmetic mean is the distance between the mean and an observation in the data set. <span>The arithmetic mean has the following disadvantages: The mean can be affected by extremes, that is, unusually large or small values. The mean cannot be determined for an open-ended data set (i.e., n is unknown). Geometric Mean The geometric mean has three important properties: It exists only if all the observations are greater th

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In a frequency distribution It is important to consider the number of intervals to be used. If too few intervals are used, too much data may be summarized and we may lose important characteristics; if too many intervals are used, we may not summarize enough.

that: Each observation can only lie in one interval. The total number of intervals will incorporate the whole population. The range for an interval is unique. This means a value (observation) can only fall into one interval. <span>It is important to consider the number of intervals to be used. If too few intervals are used, too much data may be summarized and we may lose important characteristics; if too many intervals are used, we may not summarize enough. A frequency distribution is constructed by dividing the scores into intervals and counting the number of scores in each interval. The actual number of scores and the percent

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The following steps are required when organizing data into a frequency distribution together with suggestions on constructing the frequency distribution. Identify the highest and lowest values of the observations. Setup classes (groups into which data is divided). The classes must be mutually exclusive and of equal size. Add up the number of observations and assign

by the total number of observations. Cumulative absolute frequency and cumulative relative frequency are the results from cumulating the absolute and relative frequencies as we move from the first to the last interval. <span>The following steps are required when organizing data into a frequency distribution together with suggestions on constructing the frequency distribution. Identify the highest and lowest values of the observations. Setup classes (groups into which data is divided). The classes must be mutually exclusive and of equal size. Add up the number of observations and assign each observation to its class. Count the number of observations in each class. This is called the class frequency. Data can be divided into two types: discrete and continuous. Discrete: The values in the data set can be counted. There are distinct spaces between the values, such as

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The following steps are required when organizing data into a frequency distribution together with suggestions on constructing the frequency distribution. Identify the highest and lowest values of the observations. Setup classes (groups into which data is divided). The classes must be mutually exclusive and of equal size. Add up the number of observations and assign

by the total number of observations. Cumulative absolute frequency and cumulative relative frequency are the results from cumulating the absolute and relative frequencies as we move from the first to the last interval. <span>The following steps are required when organizing data into a frequency distribution together with suggestions on constructing the frequency distribution. Identify the highest and lowest values of the observations. Setup classes (groups into which data is divided). The classes must be mutually exclusive and of equal size. Add up the number of observations and assign each observation to its class. Count the number of observations in each class. This is called the class frequency. Data can be divided into two types: discrete and continuous. Discrete: The values in the data set can be counted. There are distinct spaces between the values, such as

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ated in terms of odds as follows: Probability Stated as Odds. Given a probability P(E), Odds for E = P(E)/[1 − P(E)]. The odds for E are the probability of E divided by 1 minus the probability of E. <span>Given odds for E of “a to b,” the implied probability of E is a/(a + b). In the example, the statement that the odds for the company’s EPS for FY2014 beating $0.69 are 1 to 7 means that the speaker believes the probability of th

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y’s EPS for FY2014 beating $0.69 are 1 to 7 means that the speaker believes the probability of the event is 1/(1 + 7) = 1/8 = 0.125. Odds against E = [1 − P(E)]/P(E), the reciprocal of odds for E. <span>Given odds against E of “a to b,” the implied probability of E is b/(a + b). The statement that the odds against the company’s EPS for FY2014 beating $0.69 are 15 to 1 is consistent with a belief that the probability of the event is

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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.) Add the interval width to the minimum value (stop after reaching an interval that includes the maximum value) Count (the number of observations in each i

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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.) Add the interval width to the minimum value (stop after reaching an interval that includes the maximum value) Count (the number of observations in each i

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alculate 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.) <span>Add the interval width to the minimum value (stop after reaching an interval that includes the maximum value) Count (the number of observations in each interval) Table (make a table of intervals from small to large that shows the number of observations in each one) <span><body><html>

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ion ( interval width = Range/k.) Add the interval width to the minimum value (stop after reaching an interval that includes the maximum value) Count (the number of observations in each interval) <span>Table (make a table of intervals from small to large that shows the number of observations in each one) <span><body><html>

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Note that as n becomes large, the expression reduces to the mean cubed deviation, SK ≈ (1/n) n∑ i=1 (Xi−X) 3 / s 3 . As a frame of reference, for a sample size of 100 or larger taken from a normal distribution, a skewness coefficient of ±0.5 would be considered unusually large.

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ld: (360 x 6%)/(360 - 60 x 6%) = 6.0606% If we know HPY, then: EAY = (1 + HPY) 365/t - 1 r MM = HPY x 360/t If we know EAY, then: HPY = ( 1 + EAY) t/365 - 1 r MM = [(1 + EAY) t/365 - 1] x (360/t) <span>If we know r MM , then: HPY = r MM x (t/360) EAY = (1 + r MM x t/360) 365/t - 1 <span><body><html>

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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status | not read | reprioritisations | ||
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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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What is 'Antitrust' <span>Antitrust laws are the laws that apply to virtually all industries and to every level of business, including manufacturing, transportation, distribution and marketing. They prohibit a variety of practices that restrain trade. Examples of illegal practices are price-fixing conspiracies, corporate mergers likely to reduce the competitive vigor of particular markets, and predatory acts designed to achieve or maintain monopoly power. BREAKING DOWN 'Antitrust' Antitrust laws are necessary in an open marketplace. Competition among sellers gives consumers lower prices, higher-quali

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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

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

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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

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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 |

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

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

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last reprioritisation on | reading queue position [%] | |||

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

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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 |

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 |

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

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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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 |

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last reprioritisation on | reading queue position [%] | |||

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

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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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 |

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 |

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 |

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 |

status | not read | reprioritisations | ||
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started reading on | finished reading on |

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 |

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 |

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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 |

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 |

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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repetition number in this series | 0 | memorised on | scheduled repetition | ||||

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#math-shit

A stem-and-leaf graph, also called a stemplot, is a way to represent the distribution of numeric data. It was invented by John Tukey, a mathematician, and is a quick way to picture data for numbers that are greater than 0. I'll explain using an example. Suppose you have the following set of numbers (they might represent the number of home runs hit by a major league baseball player during his career). 32, 33, 21, 45, 58, 20, 33, 44, 28, 15, 18, 25 The stem of a stemplot can have as many digits as needed, but the leaves should contain only one digit. To create a stemplot to display the above data, you must first create the stem. Since all of the numbers have just two digits, start by arranging the tens digits from smallest to largest. To create the leaves, draw a vertical bar after each of the tens digits and arrange the ones digits from each number in the data set in order from smallest to largest. If there are duplicate numbers, like 33, list each one. 1|58 2|0158

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A stem-and-leaf graph, also called a stemplot, is a way to represent the distribution of numeric data. It was invented by John Tukey, a mathematician, and is a quick way to picture data for numbers that are greater than 0. I'll explain using an example. Suppose you have the following set of numbers (they might represent the number of home runs hit by a major league baseball player during

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Suppose you have the following set of numbers (they might represent the number of home runs hit by a major league baseball player during his career). 32, 33, 21, 45, 58, 20, 33, 44, 28, 15, 18, 25 <span>The stem of a stemplot can have as many digits as needed, but the leaves should contain only one digit. To create a stemplot to display the above data, you must first create the stem. Since all of the numbers have just two digits, start by arranging the tens digits from

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2|0158 3|233 4|45 5|8 The shape of the resulting display looks something like a bar graph oriented vertically. By examining the stemplot, you can determine certain properties of the data. <span>You can find the median by counting from either end of the stemplot until you find its center. Here, since there are 12 numbers, the center lies between 28 and 32. The median is the average of the two data points: (28+32)/2 = 30.) You can also determine if

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