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There are four internal senses: The imagination , The sensuous memory , The common or central or synthesizing sense , and instinct .

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risk for contrast-induced nephropathy (CIN), and administration of intravenous isotonic saline (1-1.5 mL/kg/h) 3 to 12 hours before the procedure and continued for 6 to 24 hours afterward has been shown to decrease the incidence of CIN in high-risk patients.

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risk for contrast-induced nephropathy (CIN), and administration of intravenous isotonic saline (1-1.5 mL/kg/h) 3 to 12 hours before the procedure and continued for 6 to 24 hours afterward has been shown to decrease the incidence of CIN in high-risk patients.

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risk for contrast-induced nephropathy (CIN), and administration of intravenous isotonic saline (1-1.5 mL/kg/h) 3 to 12 hours before the procedure and continued for 6 to 24 hours afterward has been shown to decrease the incidence of CIN in high-risk patients.

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Risk factors include age older than 75 years, diabetes mellitus, chronic kidney disease, conditions of decreased renal perfusion, and concurrent use of nephrotoxic drugs.

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discontinuing omeprazole, repeat kidney function testing in 5 to 7 days is the most appropriate management for this patient with acute interstitial nephritis (AIN).

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Drug-induced AIN is characterized by a slowly increasing serum creatinine 7 to 10 days after exposure; however, it can occur within 1 day of exposure if the patient has been exposed previously. Drug-induced AIN can also occur months after exposure, often with NSAI

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Drug-induced AIN is characterized by a slowly increasing serum creatinine 7 to 10 days after exposure; however, it can occur within 1 day of exposure if the patient has been exposed previously. Drug-induced AIN can also occur months after exposure, often with NSAIDs and proton pump inhibitors (PPIs)

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y>Drug-induced AIN is characterized by a slowly increasing serum creatinine 7 to 10 days after exposure; however, it can occur within 1 day of exposure if the patient has been exposed previously. Drug-induced AIN can also occur months after exposure, often with NSAIDs and proton pump inhibitors (PPIs)<body><html>

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AIN may be asymptomatic or present with mild, nonspecific symptoms; only 10% to 30% have the classic triad of fever, rash, and eosinophilia.

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st 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. Nominal scales are qualitative rather than quantitative. <span>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 for value and 2 for growth. Frequency distributions are usually used to analyze data me

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ency distribution. An interval, also called a class, is a set of values within which an observation falls. Each interval has a lower limit and an upper limit. Intervals must be all-inclusive and non-overlapping. <span>A frequency distribution is a tabular display of data categorized into a small number of non-overlapping intervals. Note 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 mean

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with individual numbers becomes laborious and messy. In such circumstances, it is neater and more convenient to summarize results into what is known as a frequency table. The data in the display is called a frequency distribution. <span>An interval, also called a class, is a set of values within which an observation falls. Each interval has a lower limit and an upper limit. Intervals must be all-inclusive and non-overlapping. A frequency distribution is a tabular display of data categor

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with individual numbers becomes laborious and messy. In such circumstances, it is neater and more convenient to summarize results into what is known as a frequency table. The data in the display is called a frequency distribution. <span>An interval, also called a class, is a set of values within which an observation falls. Each interval has a lower limit and an upper limit. Intervals must be all-inclusive and non-overlapping. A frequency distribution is a tabular display of data categor

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ency distribution. An interval, also called a class, is a set of values within which an observation falls. Each interval has a lower limit and an upper limit. Intervals must be all-inclusive and non-overlapping. <span>A frequency distribution is a tabular display of data categorized into a small number of non-overlapping intervals. Note 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. 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 ma

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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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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. <span>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 percentage of scores in each interval are displayed. This helps in the analysis of large amount of statistical data, and works with all types of measurement scales. Absolute frequency is the actual number of observations in a given interval. Relative frequency is the result of dividing the absolute frequency of e

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of scores in each interval. The actual number of scores and the percentage of scores in each interval are displayed. This helps in the analysis of large amount of statistical data, and works with all types of measurement scales. <span>Absolute frequency is the actual number of observations in a given interval. Relative frequency is the result of dividing the absolute frequency of each return interval by the total number of observations. Cumulative absol

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al are displayed. This helps in the analysis of large amount of statistical data, and works with all types of measurement scales. Absolute frequency is the actual number of observations in a given interval. <span>Relative frequency is the result of dividing the absolute frequency of each return interval 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 l

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olute frequency is the actual number of observations in a given interval. Relative frequency is the result of dividing the absolute frequency of each return interval by the total number of observations. <span>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. The following steps are required when organizing data into a frequency distribution together with suggestions on constructing the frequency distribution. Identify the

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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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t 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. <span>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 the number of children in a family or the number of shares compris

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mber 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. <span>Discrete: The values in the data set can be counted. There are distinct spaces between the values, such as the number of children in a family or the number of shares comprising an index. Continuous: The values in the data set can be measured. There are normally lots of decimal places involved and (theoretically, at least) there are no gaps between permissible values (i.

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ed 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 the number of children in a family or the number of shares comprising an index. <span>Continuous: The values in the data set can be measured. There are normally lots of decimal places involved and (theoretically, at least) there are no gaps between permissible values (i.e., all values can be included in the data set). Examples would include the height of a person and the time to complete an assignment. These values can be measured using sufficiently accurate tools to numerous decimal places. There are two methods that graphically represent continuous data: histograms and frequency polygons. 1. A histogram is a bar chart that displays a frequency distributi

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values (i.e., all values can be included in the data set). Examples would include the height of a person and the time to complete an assignment. These values can be measured using sufficiently accurate tools to numerous decimal places. <span>There are two methods that graphically represent continuous data: histograms and frequency polygons. 1. A histogram is a bar chart that displays a frequency distribution. It is constructed as follows: The class frequencies are shown on the vertical (y) axis (by

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unt basis D = the dollar discount, which is equal to the difference between the face value of the bill, F, and its purchase price, P t = the number of days remaining to maturity 360 = the bank convention of the number of days in a year. <span>Bank discount yield is not a meaningful measure of the return on investment because: It is based on the face value, not on the purchase price. Instead, return on investment should be measured based on cost of investment. It is annualized using a 360-day year, not a 365-day year. It annualizes with simple interest and ignores the effect of interest on interest (compound interest). Holding period yield (HPY) is the return earned by an investor if the money market instrument is held until maturity: P 0 =

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mber 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. <span>Discrete: The values in the data set can be counted. There are distinct spaces between the values, such as the number of children in a family or the number of shares comprising an index. Continuous: The values in the data set can be measu

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ed 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 the number of children in a family or the number of shares comprising an index. <span>Continuous: The values in the data set can be measured. There are normally lots of decimal places involved and (theoretically, at least) there are no gaps between permissible values (i.e., all values can be included in the data set). Example

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me to complete an assignment. These values can be measured using sufficiently accurate tools to numerous decimal places. There are two methods that graphically represent continuous data: histograms and frequency polygons. 1. <span>A histogram is a bar chart that displays 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

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al places. There are two methods that graphically represent continuous data: histograms and frequency polygons. 1. A histogram is a bar chart that displays a frequency distribution. It is constructed as follows: <span>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. There is no space between the bars. From a histogram, we can see quickly where most of the observa

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polygons. 1. A histogram is a bar chart that displays 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). <span>The classes (intervals) are shown on the horizontal (x) axis. 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 th

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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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#13; 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. There is no space between the bars. <span>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 intervals. 2. The frequency polygon is another means of graphically displaying data. It is si

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the heights of bars drawn next to each other). The classes (intervals) are shown on the horizontal (x) axis. There is no space between the bars. From a histogram, we can see quickly where most of the observations lie. <span>The shapes of histograms will vary, depending on the choice of the size of the intervals. 2. The frequency polygon is another means of graphically displaying data. It is similar to a histogram but the bars are replaced by a line joined together. It is constructe

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s. 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 intervals. 2. <span>The frequency polygon is another means of graphically displaying data. It is similar to a histogram but the bars are replaced by a line joined together. It is constructed in the following manner: Absolute frequency for each interval is plotted

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f graphically displaying data. It is similar to a histogram but the bars are replaced by a line joined together. It is constructed in the following manner: Absolute frequency for each interval is plotted on the vertical (y) axis. <span>The midpoint of each class (interval) is shown on the horizontal (x) axis. Neighboring points are connected with a straight line. Unlike a histogram, a frequency polygon adds a degree of continuity to the presentation of the distribution. It

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e following manner: Absolute frequency for each interval is plotted on the vertical (y) axis. The midpoint of each class (interval) is shown on the horizontal (x) axis. Neighboring points are connected with a straight line. <span>Unlike a histogram, a frequency polygon adds a degree of continuity to the presentation of the distribution. It is helpful, when drawing a frequency polygon, first to draw a histogram in pencil, then to plot the points and join the lines, and finally to rub out the histogram. In th

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first to draw a histogram in pencil, then to plot the points and join the lines, and finally to rub out the histogram. In this way, the histogram can be used as an initial guide to drawing the polygon. <span>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). 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 pe

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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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st one is -27%. Let's use 6 non-overlapping intervals, each with a width of 10%. The first interval starts at -27% and the last one ends at 33%. Therefore, the entire range of the HPRs is covered. Hint: <span>If, in an examination, your relative frequency column does not sum to 1 (or 100%), you know that you have made a mistake. <span><body><html>

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Measures of central tendency specify where data are centered. They attempt to use a typical value to represent all the observations in the data set. Population Mean The population mean is the average for a finite populatio

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head><head> Measures of central tendency specify where data are centered. They attempt to use a typical value to represent all the observations in the data set. Population Mean The population mean is the average for a finite population. It is unique; a given population has only one mean. where: N = the number of observations in the entire population X i = the ith observation Î£X i = add up X i , where i is from 0 to

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represent all the observations in the data set. Population Mean The population mean is the average for a finite population. It is unique; a given population has only one mean. <span>where: N = the number of observations in the entire population X i = the ith observation Î£X i = add up X i , where i is from 0 to N Sample Mean The sample mean is the average for a sample. It is a statistic and is used to estimate the population mean.

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que; a given population has only one mean. where: N = the number of observations in the entire population X i = the ith observation Î£X i = add up X i , where i is from 0 to N <span>Sample Mean The sample mean is the average for a sample. It is a statistic and is used to estimate the population mean. where n = the number of observations in the sample Arithmetic Mean The arithmetic mean is what is commonly called the a

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stic and is used to estimate the population mean. where n = the number of observations in the sample Arithmetic Mean The arithmetic mean is what is commonly called the average. <span>The population mean and sample mean are both examples of the arithmetic mean. If the data set encompasses an entire population, the arithmetic mean is called a population mean. If the data set includes a sample of values taken from a population, the

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here n = the number of observations in the sample Arithmetic Mean The arithmetic mean is what is commonly called the average. The population mean and sample mean are both examples of the arithmetic mean. <span>If the data set encompasses an entire population, the arithmetic mean is called a population mean. If the data set includes a sample of values taken from a population, the arithmetic mean is called a sample mean. This is the most widely used measure of central tendency.

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thmetic mean is what is commonly called the average. The population mean and sample mean are both examples of the arithmetic mean. If the data set encompasses an entire population, the arithmetic mean is called a population mean. <span>If the data set includes a sample of values taken from a population, the arithmetic mean is called a sample mean. This is the most widely used measure of central tendency. When the word "mean" is used without a modifier, it can be assumed to refer to the arithmetic mean. The

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an. If the data set encompasses an entire population, the arithmetic mean is called a population mean. If the data set includes a sample of values taken from a population, the arithmetic mean is called a sample mean. <span>This is the most widely used measure of central tendency. When the word "mean" is used without a modifier, it can be assumed to refer to the arithmetic mean. The mean is the sum of all scores divided by the number of scores. It is us

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ulation, the arithmetic mean is called a population mean. If the data set includes a sample of values taken from a population, the arithmetic mean is called a sample mean. This is the most widely used measure of central tendency. <span>When the word "mean" is used without a modifier, it can be 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

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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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r of scores. It is used to measure the prospective (expected future) performance (return) of an investment over a number of periods. All interval and ratio data sets (e.g., incomes, ages, rates of return) have an arithmetic mean. <span>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 the only measure of central tendency where the sum of the deviations of each valu

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(return) of an investment over a number of periods. 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. <span>A data set has only one arithmetic mean. This indicates that the mean is unique. The arithmetic 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 distanc

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d 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. <span>The arithmetic 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. The arithmetic mean has the following disadvantages: Th

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thmetic mean computation. A data set has only one arithmetic mean. This indicates that the mean is unique. The arithmetic mean is the only measure of central tendency where the sum of the deviations of each value from the mean is always zero. <span>Deviation from the arithmetic mean is the distance between the mean and an observation in the data set. 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 determi

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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

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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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13; 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). <span>Geometric Mean The geometric mean has three important properties: It exists only if all the observations are greater than or equal to zero. In othe

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s: 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 <span>The geometric mean has three important properties: It exists only if all the observations are greater than or equal to zero. In other words, it cannot be determined if any value of the data set is zero or negative. If values in the data set are all equal, both the arithmetic and geometric means will be equal to that value. It is always less than the arithmetic mean if values in the data set are not equal. It is typically used when calculating returns over multiple periods. It is a better measure of the compound growth rate of an investment. When returns are variable by perio

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f the data set is zero or negative. If values in the data set are all equal, both the arithmetic and geometric means will be equal to that value. It is always less than the arithmetic mean if values in the data set are not equal. <span>It is typically used when calculating returns over multiple periods. It is a better measure of the compound growth rate of an investment. When returns are variable by period, the geometric mean will always be less than the arithmetic mean. The more dispe

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value. It is always less than the arithmetic mean if values in the data set are not equal. It is typically used when calculating returns over multiple periods. It is a better measure of the compound growth rate of an investment. <span>When returns are variable by period, the geometric mean will always be less than the arithmetic mean. The more dispersed the rates of returns, the greater the difference between the two. This measurement is not as highly influenced by extreme values as the arithmetic mean.

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; It is typically used when calculating returns over multiple periods. It is a better measure of the compound growth rate of an investment. When returns are variable by period, the geometric mean will always be less than the arithmetic mean. <span>The more dispersed the rates of returns, the greater the difference between the two. This measurement is not as highly influenced by extreme values as the arithmetic mean. Weighted Mean The weighted mean is computed by weighting each observed v

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r measure of the compound growth rate of an investment. When returns are variable by period, the geometric mean will always be less than the arithmetic mean. The more dispersed the rates of returns, the greater the difference between the two. <span>This measurement is not as highly influenced by extreme values as the arithmetic mean. Weighted Mean The weighted mean is computed by weighting each observed value according to its importance. In contrast, the arithmetic mean assigns equal weight

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less than the arithmetic mean. The more dispersed the rates of returns, the greater the difference between the two. This measurement is not as highly influenced by extreme values as the arithmetic mean. Weighted Mean <span>The weighted mean is computed by weighting each observed value according to its importance. In contrast, the arithmetic mean assigns equal weight to each value. Notice that the return of a portfolio is the weighted mean of the returns of individual assets in the portfolio. The

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alues as the arithmetic mean. Weighted Mean The weighted mean is computed by weighting each observed value according to its importance. In contrast, the arithmetic mean assigns equal weight to each value. Notice that <span>the return of a portfolio is the weighted mean of the returns of individual assets in the portfolio. The assets are weighted on their market values relative to the market value of the portfolio. When we take a weighted average of forward-looking data, the weighted mean is called expect

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assigns equal weight to each value. Notice that the return of a portfolio is the weighted mean of the returns of individual assets in the portfolio. The assets are weighted on their market values relative to the market value of the portfolio. <span>When we take a weighted average of forward-looking data, the weighted mean is called expected value. Example A year ago, a certain share had a price of $6. Six months ago, the same share had a price of $6.20. The share is now trading at $7.50. Because the most

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ice inflates the weighted mean relative to the un-weighted mean. Median In English, the word "mediate" means to go between or to stand in the middle of two groups, in order to act as a referee, so to speak. <span>The median does the same thing; it is the value that stands in the middle of the data set, and divides it into two equal halves, with an equal number of data values in each half. To determine the median, arrange the data from highest to lowest (or lowest to highest) and find the middle observation.

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groups, in order to act as a referee, so to speak. The median does the same thing; it is the value that stands in the middle of the data set, and divides it into two equal halves, with an equal number of data values in each half. <span>To determine the median, arrange the data from highest to lowest (or lowest to highest) and find the middle observation. If there are an odd number of observations in the data set, the median is the middle observation (n + 1)/2 of the data set. If the number of observation

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value that stands in the middle of the data set, and divides it into two equal halves, with an equal number of data values in each half. To determine the median, arrange the data from highest to lowest (or lowest to highest) and <span>find the middle observation. If there are an odd number of observations in the data set, the median is the middle observation (n + 1)/2 of the data set. If the number of observations is even, there is no single mid