# on 19-Jul-2017 (Wed)

#### Flashcard 1429113539852

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#sister-miriam-joseph #trivium
Question
[...] is the norm of rhetoric.
Effectiveness

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Effectiveness is the norm of rhetoric.

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#### Flashcard 1432865082636

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#i-q #types-of-inteligence
Question
[...], e.g. music performance, singing, musical composition, rhythmic patterns.
Musical=rhythmic

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Musical=rhythmic, e.g. music performance, singing, musical composition, rhythmic patterns.

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#### Flashcard 1433034689804

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#italian #italian-grammar
Question
Verbs normally have two voices: [...] and [...]
active

passive.

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Verbs normally have two voices: active and passive.

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#### Flashcard 1610314288396

Question
Explain the disadvantages of native speakers as language teachers.
Many school systems prefer to hire native speakers (NSs) as language teachers because of their authentic relationship to the target language and culture, but native speakers don’t necessarily know the home culture of their students nor the intellectual tradition of their school system. NSs represent an attractive exotic other but, as research has shown, they cannot act as models for learners who by definition will not become native speakers. Non-native language teachers have the advantage of having learned the language the way their students do

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#### Flashcard 1621283179788

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#cashflow-statement
Question
Investment in working capital equals [...] operating assets net of [...]
the increase in short-term

operating liabilities.

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Subject 3. Cash Flow Statement Analysis
; 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

#### Flashcard 1635085585676

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#statistical-concepts-and-market-returns
Question
skewness analyzes whether the distribution of returns is [...]
symmetrically shaped or lopsided

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where the returns are centered (central tendency); how far returns are dispersed from their center (dispersion); whether the distribution of returns is symmetrically shaped or lopsided (skewness); and whether extreme outcomes are likely (kurtosis).

#### Flashcard 1636237184268

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Question
A [...] is a tabular display of data categorized into a small number of non-overlapping intervals.
frequency distribution

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Subject 3. Frequency Distributions
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

#### Annotation 1636245572876

 #reading-8-statistical-concepts-and-market-returns 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.

Subject 3. Frequency Distributions
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

#### Flashcard 1636479143180

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Question
The shapes of histograms will vary, depending on [...]
the choice of the size of the intervals.

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Subject 3. Frequency Distributions
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

#### Flashcard 1636489366796

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Question
The [...] 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.
relative frequency

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Subject 3. Frequency Distributions
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

#### Flashcard 1636579020044

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Question
Second step in finding the median
find the middle observation.

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Subject 4. Measures of Center Tendency
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

#### Flashcard 1636598680844

Question
preferably
[default - edit me]

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referably outside. Automatisch von Google übersetzt Ideally you would practice it in the sun, preferably outside or in a sunny room. Automatisch von Google übersetzt 13 weitere Beispiele Siehe auch lieber mögen, lieber haben Übersetzungen für <span>preferably Adverb vorzugsweise preferably, mainly, chiefly möglichst preferably besser better, preferably lieber rather, better, preferably, sooner Google Übersetzer für Unternehmen:Translator Too

#### Flashcard 1636600777996

Question
[default - edit me]
 Adverb vorzugsweise preferably , mainly , chiefly möglichst preferably besser better , preferably lieber rather , better , preferably , sooner

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omatisch von Google übersetzt The session should be as short and as intensive as possible, preferably two or three days. Automatisch von Google übersetzt 13 weitere Beispiele Siehe auch lieber mögen, lieber haben Übersetzungen für preferably <span>Adverb vorzugsweise preferably, mainly, chiefly möglichst preferably besser better, preferably lieber rather, better, preferably, sooner Google Übersetzer für Unternehmen:Translator ToolkitWebsite-Übersetzer Ziehe die Datei oder den Link auf diesen Bereich, um das Dokument oder die Webseite zu übersetzen. Ziehe den Lin

#### Annotation 1636606807308

 I explore the notion of Abbasid metapoetry, sug- gesting that the term, as defined by twentieth-century critics, appropriately describes the relationship of Abbasid poets to their predecessors

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#### Annotation 1636608380172

 Poetry and the debates around poetry became a common feature of every court. These debates had their impact on the attitudes of poets towards their craft, which necessarily led to more conscious and deliberate poetry that con- stantly addresses its references and calls attention to its processes. Moreover, the poet became an authority on poetry, often presiding over discussions of his work and that of others. Knowledge of poetry became a specialized field. Poets and poems were compared, examined, and weighed against each other. The reception of poetry became a specialized skill not everyone could lay claim to. This is what al-Amidi calls al- 'ilm bi al-shi'r 11 (knowledge of poetry, criticism), a science whose experts often included poets.

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#### Annotation 1636609953036

 In Abbasid metapoesis, one must avoid forcing the term on earlier poetry. In the Arabic tradition, whose common poetic themes is the boast, most boast poems include long descriptions of what good poetry is and who a great poet is. Thus, one must distinguish between thematic metapoetry, meaning poems whose subject or theme is poetry, and a metapoetry that is deeper and much more critical in nature, although it is not always signaled or marked as clearly as the former. I call this second type "referential or contextual metapoesis," to reflect a consciousness in the manner a poet engages his poetic references.

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#### Annotation 1636611525900

 Abbasid poetry not a continuation of what came before: the Abbasid experience is a step away from the archetypal qasidah.

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#### Annotation 1636613885196

 The keyword mnemonic demands considerable creative effort if the learner is to go beyond the words for which suggested conversions of words to images are available. Retrieval practice could offer an alternative technique for learning vocabulary, one that is less demanding and might be applied by a wider range of learners to a wider range of vocabulary.

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#### Flashcard 1636617817356

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Question
If there are an odd number of observations in the data set, the median formula is [...]
(n + 1)/2

The middle observation

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Subject 4. Measures of Center Tendency
dle 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. <span>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 middle observation (there are two, actually). To find the median, take the arithmetic mean of the two middle o

#### Flashcard 1636620963084

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Question
If the number of observations is even, how do you find the median?
Take the arithmetic mean of the two middle observations.

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Subject 4. Measures of Center Tendency
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. <span>If the number of observations is even, there is no single middle observation (there are two, actually). To find the median, take the arithmetic mean of the two middle observations. The median is less sensitive to extreme scores than the mean. This makes it a better measure than the mean for highly skewed distributions. Looking at median income is usua

#### Flashcard 1636623322380

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Question
For highly skewed distributions what is a better measure of central tendency, the mean or the median?
the median because it is less sensitive to extreme scores than the mean.

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Subject 4. Measures of Center Tendency
he middle observation (n + 1)/2 of the data set. If the number of observations is even, there is no single middle observation (there are two, actually). To find the median, take the arithmetic mean of the two middle observations. <span>The median is less sensitive to extreme scores than the mean. This makes it a better measure than the mean for highly skewed distributions. Looking at median income is usually more informative than looking at mean income, for example. The sum of the absolute deviations of each number from the median is lower than the sum of

#### Flashcard 1636625681676

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Question
Looking at [...] income is usually more informative than looking at [...] income
median

mean

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Subject 4. Measures of Center Tendency
, actually). To find the median, take the arithmetic mean of the two middle observations. The median is less sensitive to extreme scores than the mean. This makes it a better measure than the mean for highly skewed distributions. <span>Looking at median income is usually more informative than looking at mean income, for example. The sum of the absolute deviations of each number from the median is lower than the sum of absolute deviations from any other number. Note that whenever you c

#### Flashcard 1636628040972

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Question
The sum of the absolute deviations of each number from the median is [...] than the sum of absolute deviations from any other number.
lower

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Subject 4. Measures of Center Tendency
The median is less sensitive to extreme scores than the mean. This makes it a better measure than the mean for highly skewed distributions. Looking at median income is usually more informative than looking at mean income, for example. <span>The sum of the absolute deviations of each number from the median is lower than the sum of absolute deviations from any other number. Note that whenever you calculate a median, it is imperative that you place the data in order first. It does not matter whether you order the data from smallest to largest o

#### Flashcard 1636630400268

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Question
whenever you calculate a median, it is imperative that you [...]
place the data in order first.

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Subject 4. Measures of Center Tendency
t median income is usually more informative than looking at mean income, for example. The sum of the absolute deviations of each number from the median is lower than the sum of absolute deviations from any other number. Note that <span>whenever you calculate a median, it is imperative that you place the data in order first. It does not matter whether you order the data from smallest to largest or from largest to smallest, but it does matter that you order the data. Mode Mode means

#### Flashcard 1636632759564

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Question
Mode is the [...] score in a distribution.
most frequently occurring

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Subject 4. Measures of Center Tendency
a median, it is imperative that you place the data in order first. It does not matter whether you order the data from smallest to largest or from largest to smallest, but it does matter that you order the data. Mode <span>Mode means fashion. The mode is the "most fashionable" number in a data set; it is the most frequently occurring score in a distribution and is used as a measure of central tendency. A set of data can have more than one mode, or even no mode. When all values are different, the data set has no mode. When a distribution has one value that appears most frequently, it i

#### Flashcard 1636635118860

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Question
A set of data can have [...] mode, or even [...].
more than one

no mode

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Subject 4. Measures of Center Tendency
ou order the data. Mode Mode means fashion. The mode is the "most fashionable" number in a data set; it is the most frequently occurring score in a distribution and is used as a measure of central tendency. <span>A set of data can have more than one mode, or even no mode. When all values are different, the data set has no mode. When a distribution has one value that appears most frequently, it is said to be unimodal. A data set that has two modes is said

#### Flashcard 1636637478156

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Question
When all values are different, the data doen's have a [...]
mode

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Subject 4. Measures of Center Tendency
fashion. The mode is the "most fashionable" number in a data set; it is the most frequently occurring score in a distribution and is used as a measure of central tendency. A set of data can have more than one mode, or even no mode. <span>When all values are different, the data set has no mode. When a distribution has one value that appears most frequently, it is said to be unimodal. A data set that has two modes is said to be bimodal. The advantage of the mode a

#### Annotation 1636639837452

 #reading-8-statistical-concepts-and-market-returns When a distribution has one value that appears most frequently, it is said to be unimodal. A data set that has two modes is said to be bimodal.

Subject 4. Measures of Center Tendency
mber in a data set; it is the most frequently occurring score in a distribution and is used as a measure of central tendency. A set of data can have more than one mode, or even no mode. When all values are different, the data set has no mode. <span>When a distribution has one value that appears most frequently, it is said to be unimodal. A data set that has two modes is said to be bimodal. The advantage of the mode as a measure of central tendency is that its meaning is obvious. Like the median, the mode is not affected by extreme values. Further, it is the o

#### Flashcard 1636641410316

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Question
Which is the only measure of central tendency that can be used with nominal data.
Mode

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Subject 4. Measures of Center Tendency
to be unimodal. A data set that has two modes is said to be bimodal. The advantage of the mode as a measure of central tendency is that its meaning is obvious. Like the median, the mode is not affected by extreme values. Further, <span>it is the only measure of central tendency that can be used with nominal data. The mode is greatly subject to sample fluctuations and, therefore, is not recommended for use as the only measure of central tendency. A further disadvantage of the mode is that many di

#### Annotation 1636643769612

 #has-images #reading-8-statistical-concepts-and-market-returns Harmonic Mean The harmonic mean of n numbers xi (where i = 1, 2, ..., n) is: The special cases of n = 2 and n = 3 are given by: and so on. For n = 2, the harmonic mean is related to arithmetic mean A and geometric mean G by:

Subject 4. Measures of Center Tendency
ons and, therefore, is not recommended for use as the only measure of central tendency. A further disadvantage of the mode is that many distributions have more than one mode. These distributions are called "multimodal." <span>Harmonic Mean The harmonic mean of n numbers x i (where i = 1, 2, ..., n) is: The special cases of n = 2 and n = 3 are given by: and so on. For n = 2, the harmonic mean is related to arithmetic mean A and geometric mean G by: The mean, median, and mode are equal in symmetric distributions. The mean is higher than the median in positively skewed distributions and lower than th

#### Flashcard 1636645342476

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Question
The mean is higher than the median in [...] distributions.
positively skewed

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Subject 4. Measures of Center Tendency
and so on. For n = 2, the harmonic mean is related to arithmetic mean A and geometric mean G by: The mean, median, and mode are equal in symmetric distributions. <span>The mean is higher than the median in positively skewed distributions and lower than the median in negatively skewed distributions. Extreme values affect the value of the mean, while the median is less affected by outliers. Mode helps to identify shape and skewness of distribution.<span><body><html>

#### Flashcard 1636647701772

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Question
The mean is lower than the median in [...] distributions
negatively skewed

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Subject 4. Measures of Center Tendency
and so on. For n = 2, the harmonic mean is related to arithmetic mean A and geometric mean G by: The mean, median, and mode are equal in symmetric distributions. <span>The mean is higher than the median in positively skewed distributions and lower than the median in negatively skewed distributions. Extreme values affect the value of the mean, while the median is less affected by outliers. Mode helps to identify shape and skewness of distribution.<span><body><html>

#### Flashcard 1636650061068

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Question
The mean, median, and mode are equal in [...].
symmetric distributions

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Subject 4. Measures of Center Tendency
of n = 2 and n = 3 are given by: and so on. For n = 2, the harmonic mean is related to arithmetic mean A and geometric mean G by: <span>The mean, median, and mode are equal in symmetric distributions. The mean is higher than the median in positively skewed distributions and lower than the median in negatively skewed distributions. Extreme values affect the value of the mean, while th

#### Flashcard 1636652420364

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Question
Mode helps to identify [...] and [...] of distribution.
shape and skewness

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Subject 4. Measures of Center Tendency
ric distributions. The mean is higher than the median in positively skewed distributions and lower than the median in negatively skewed distributions. Extreme values affect the value of the mean, while the median is less affected by outliers. <span>Mode helps to identify shape and skewness of distribution.<span><body><html>

#### Annotation 1636662906124

 Another benefit derives from the comprehensibility of the images: Without meaning, memory is poor (Bransford & Johnson, 1973; Bartlett, 1932), and novel foreign words are, by definition, not understood by the learner. The keyword ideas and sounds, though, are understandable and therefore, more memorable than the foreign words.

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#### Annotation 1636664478988

 The effectiveness of the keyword method also appears to depend upon the quality of the keyword image

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#### Annotation 1636666313996

 When compared with items that had also been presented just once but had not received retrieval practice, Landauer and Bjork found that all retrieval schedules enhanced recall at a later test, and that performance was best with an expanding schedule.

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#### Flashcard 1636667886860

Question
Define: Retrieval practice
It involves retrieving target information once, or preferably several times, prior to some criterion test.

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#### Annotation 1636670246156

 Another potential benefit of retrieval practice is a motivational one. If practice is scheduled to enable high levels of success, learners have a more positive learning experience and may approach the task more confidently and enthusiastically.

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#### Flashcard 1636817308940

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Question
In addition to permitting ranking and addition or subtraction, ratio scales allow [...].
computation of meaningful ratios

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In addition to permitting ranking and addition or subtraction, ratio scales allow computation of meaningful ratios.

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Subject 2. Measurement Scales
s in temperature, but a temperature of 30°F is not twice as warm as one of 15°F. Ratio Scale Ratio scales are like interval scales except that they have true zero points. This is the strongest measurement scale. <span>In addition to permitting ranking and addition or subtraction, ratio scales allow computation of meaningful ratios. A good example is the Kelvin scale of temperature. This scale has an absolute zero. Thus, a temperature of 300°K is twice as high as a temperature of 150°K. Two financial examples of ra

#### Flashcard 1636819668236

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Question
In a frequency distribution It is important to consider [...] to be used.
the number of intervals

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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.

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Flashcard 1636822027532

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Question
In a frequency distribution If too few intervals are used, [...].
too much data may be summarized and we may lose important characteristics

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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.

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Flashcard 1636824386828

Tags
Question
In a frequency distribution if too many intervals are used, [...]
we may not summarize enough.

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#### Parent (intermediate) annotation

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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.

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Flashcard 1636826221836

Tags
Question
The following steps are required when organizing data into a frequency distribution.
• Identify [...].

• Setup classes (groups into which data is divided).

• Add up the number of observations and assign each observation to its class.

• Count the number of observations in each class.
the highest and lowest values of the observations

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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

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Flashcard 1636828581132

Tags
Question
The following steps are required when organizing data into a frequency distribution.
• Identify the highest and lowest values of the observations

• Setup classes (groups into which data is divided).

• [...]

• Count the number of observations in each class.
Add up the number of observations and assign each observation to its class.

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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

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Flashcard 1636830416140

Tags
Question
The following steps are required when organizing data into a frequency distribution.
• Identify the highest and lowest values of the observations

• [...]

• Add up the number of observations and assign each observation to its class.

• Count the number of observations in each class.
Setup classes (groups into which data is divided).

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#### Parent (intermediate) annotation

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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

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Flashcard 1636832251148

Tags
Question
The following steps are required when organizing data into a frequency distribution.
• Identify the highest and lowest values of the observations

• Setup classes (groups into which data is divided).

• Add up the number of observations and assign each observation to its class.

• [...]
Count the number of observations in each class.

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#### Parent (intermediate) annotation

Open it
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

#### Original toplevel document

Subject 3. Frequency Distributions
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 classes in a frequency distribution must be [...] and [...]
mutually exclusive

of equal size.

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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). <span>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 freque

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Subject 3. Frequency Distributions
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 number of observations in each class is called [...]
the class frequency.

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tions. 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 <span>the number of observations in each class. This is called the class frequency.<span><body><html>

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Subject 3. Frequency Distributions
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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[...] is a procedure for updating beliefs based on new information.
Bayes’ formula

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In probability, the return on a risky asset is an example of a [...]

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[...] is a quantity whose future outcomes are uncertain.

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A possible value of a random variable is called an [...]
outcome

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An [...] is a specified set of outcomes.

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To save words, it is common to use [...] to represent a defined event.
a capital letter in italics

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A [...] is a number between 0 and 1 describing the chance that a stated event will occur.
Probability

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The two defining properties of a probability are as follows:

1. The probability of any event E is [...]

2. The sum of the probabilities of any set of mutually exclusive and exhaustive events equals 1.

a number between 0 and 1: 0 ≤ P(E) ≤ 1.

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The two defining properties of a probability are as follows:

1. The probability of any event E is a number between 0 and 1: 0 ≤ P(E) ≤ 1.

2. The [...]

sum of the probabilities of any set of mutually exclusive and exhaustive events equals 1.

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Covering or containing all possible outcomes.

Exhaustive

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P followed by parentheses stands for [...]

the probability of (the event in parentheses)

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If A, B, and C are mutually exclusive and exhaustive events then P(A) + P(B) + P(C) = [...]

1.

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The probability of any event is the [...] included in the definition of the event.

sum of the probabilities of the distinct outcomes

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In investments, we often estimate the probability of an event as a [...]

relative frequency of occurrence based on historical data.

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The probability of an event estimated as a relative frequency of occurrence is called an [...]

Empirical probability

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Relationships must be stable through time for [...] to be accurate.

Empirical probabilities

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Can we ecalculate an empirical probability of an event not in the historical record?

No

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Can we calculate a reliable empirical probability for a very rare event?

No

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A probability drawing on personal judgment is called a [...]

Subjective probability

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There are cases in which we may adjust an empirical probability to account for perceptions of changing relationships in this cased we are talking about a [...]

Subjective probability

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There are cases in which we have no empirical probability to use at all, then we would be making a [...]

Subjective probability

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#### Annotation 1637156261132

 Within Nelson and Narens’s (1994) influential framework, students are characterized as confronting three types of decisions—selection of kind of processing, allocation of study time, and termination of study. We have expanded that framework to include deci- sions about study strategies and scheduling

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Subjective probabilities appear in various places in this reading, notably in our discussion of [...]

Bayes’ formula.

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Because a priori and empirical probabilities generally do not vary from person to person, they are often grouped as [...]

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#### Annotation 1637163601164

Odds

In business and elsewhere, we often encounter probabilities stated in terms of odds—for instance, “the odds for E” or the “odds against E.” For example, as of November 2013, analysts’ fiscal year 2014 EPS forecasts for JetBlue Airways (NASDAQ: JBLU) ranged from $0.55 to$0.69. Suppose one analyst asserts that the odds for the company beating the highest estimate, $0.69, are 1 to 7. Suppose a second analyst argues that the odds against that happening are 15 to 1. What do those statements imply about the probability of the company’s EPS beating the highest estimate? We interpret probabilities stated in terms of odds as follows: • Probability Stated as Odds. Given a probability P(E), 1. 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. 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 the event is 1/(1 + 7) = 1/8 = 0.125.

1. Odds against E = [1 − P(E)]/P(E), the reciprocal of odds for E. 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 1/(1 + 15) = 1/16 = 0.0625. To further explain odds for an event, if P(E) = 1/8, the odds for E are (1/8)/(7/8) = (1/8)(8/7) = 1/7, or “1 to 7.” For each occurrence of E, we expect seven cases of non-occurrence; out of eight cases in total, therefore, we expect E to happen once, and the probability of E is 1/8. In wagering, it is common to speak in terms of the odds against something, as in Statement 2. For odds of “15 to 1” against E (an implied probability of E of 1/16), a$1 wager on E, if successful, returns $15 in profits plus the$1 staked in the wager. We can calculate the bet’s anticipated profit as follows:

 Win: Probability = 1/16; Profit =$15 Loss: Probability = 15/16; Profit =–$1 Anticipated profit = (1/16)($15) + (15/16)(–$1) = $0 Weighting each of the wager’s two outcomes by the respective probability of the outcome, if the odds (probabilities) are accurate, the anticipated profit of the bet is$0.

#### Annotation 1637165436172

 What to study ? which items to study is one of the two most frequently investigated components of self-regulated study (the other is deciding how long to persevere once a choice has been made)

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These values can be measured using sufficiently accurate tools to numerous decimal places.
Continuous data

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ts 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. <span>These values can be measured using sufficiently accurate tools to numerous decimal places.<span><body><html>

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Subject 3. Frequency Distributions
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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Examples [...] data would include the height of a person and the time to complete an assignment.
Continuous

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an>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.<span><body><html>

#### Original toplevel document

Subject 3. Frequency Distributions
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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[...] : The values in the data set can be measured.
Continuous

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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

#### Original toplevel document

Subject 3. Frequency Distributions
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

#### Annotation 1637174873356

 How Long to Study Once an item has been selected for study, two more decisions must be made: (1) how long to persist before moving on to another item and (2) when to stop studying the item altogether.

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#### Annotation 1637177232652

 Deciding when to stop studying . It seems simple: Stop studying when you know the information. Often, however, people stop studying when they do not know the information—when, for example, they are under time pressure or they feel that the information is either not learnable or not worth learning

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#### Annotation 1637178805516

 Perhaps most surprising is that the participants dropped items that they seemed to realize they did not really know.

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#### Annotation 1637180378380

 our participants seemed to be of two minds: They dropped items they knew at the time, but they also seemed aware that they might forget those items by the time of the test. If they knew that they might forget those items, why not leave them in the deck for further study? Our hypothesis is that the participants believed, wrongly, that there was little to gain from restudying an answer once they could recall it, even if the answer might other- wise be forgotten

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#### Annotation 1637181951244

 A subsequent experiment supported this hypothesis. When participants were asked to type in answers as they studied, 58% of dropped items were dropped after a sin- gle correct recall (and 20% after not having been recalled at all!). Such a strategy is far from optimal: Items that are close to being recallable (i.e., in the region of prox- imal learning) on a later test should be prioritized, not dropped.

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#### Annotation 1637183786252

 spacing practice and self-testing, both of which are de- sirable difficulties—that is, manipulations that introduce difficulties during study, but enhance long-term learning (Bjork, 1994)

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#### Annotation 1637186931980

 separating successive study sessions rather than massing such sessions—has large positive ef- fects on long-term memory (see, e.g., Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006; Dempster, 1988). But what do students choose to do? Choosing to space study activities requires a belief that most students may not have—that spacing is advanta- geous

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#### Annotation 1637188504844

 students would choose massed practice if given a choice, however counterproductive that choice might be.

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#### Annotation 1637190077708

 Choosing to self-test . Self-testing can be a very effec- tive study strategy, especially in the interest of long-term

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[...] means selling borrowed shares in the hope of repurchasing them later at a lower price.
Selling short or shorting stock

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#### Annotation 1637193485580

 retention (see, e.g., Bjork, 1975; Roediger & Karpicke, 2006). The act of retrieving information from memory increases, sometimes dramatically, the likelihood that information can be retrieved again in the future. When adopting self-testing as a study strategy, it is important to choose the right time to test oneself, because the more difficult the retrieval (provided it succeeds), the larger the benefits;

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[...] a trade in two closely related stocks involving the short sale of one and the purchase of the other.

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A result in probability theory stating that inconsistent probabilities create profit opportunities.
Dutch book theorem

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To understand the meaning of a probability in investment contexts, we need to distinguish between two types of probability: [...]
unconditional and conditional.

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#### Annotation 1637200563468

 #reading-9-probability-concepts To understand the meaning of a probability in investment contexts, we need to distinguish between two types of probability: unconditional and conditional. Both unconditional and conditional probabilities satisfy the definition of probability stated earlier, but they are calculated or estimated differently and have different interpretations. They provide answers to different questions.

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The probability of an event not conditioned on another event is called an [...]
unconditional probability

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#### Annotation 1637204757772

 #reading-9-probability-concepts In analyses of probabilities presented in tables, unconditional probabilities usually appear at the ends or margins of the table, hence the term marginal probability. Because of possible confusion with the way marginal is used in economics (roughly meaning incremental), we use the term unconditional probability throughout this discussion.

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An unconditional probability is also called [...]
a Marginal Probability

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The probability in answer to the straightforward question “What is the probability of this event A?” is an [...]

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A fraction is made up of two integers—one on the top, and one on the bottom. The top one is called the [...] , the bottom one is called the [...]
numerator

denominator

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#### Annotation 1637212622092

 #reading-9-probability-concepts Suppose the question is “What is the probability that the stock earns a return above the risk-free rate (event A)?” The answer is an unconditional probability that can be viewed as the ratio of two quantities. The numerator is the sum of the probabilities of stock returns above the risk-free rate. Suppose that sum is 0.70. The denominator is 1, the sum of the probabilities of all possible returns. The answer to the question is P(A) = 0.70. Now suppose we want to know the probability that the stock earns a return above the risk-free rate (event A), given that the stock earns a positive return (event B). With the words “given that,” we are restricting returns to those larger than 0 percent—a new element in contrast to the question that brought forth an unconditional probability. The conditional probability is calculated as the ratio of two quantities. The numerator is the sum of the probabilities of stock returns above the risk-free rate; in this particular case, the numerator is the same as it was in the unconditional case, which we gave as 0.70. The denominator, however, changes from 1 to the sum of the probabilities for all outcomes (returns) above 0 percent. Suppose that number is 0.80, a larger number than 0.70 because returns between 0 and the risk-free rate have some positive probability of occurring. Then P(A | B) = 0.70/0.80 = 0.875. If we observe that the stock earns a positive return, the probability of a return above the risk-free rate is greater than the unconditional probability, which is the probability of the event given no other information.

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In the question “What is the probability of A, given that B has occurred?” The probability in answer to this last question is a [...]

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A conditional probability is denoted [...]
P(A | B)

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If A = 0.7 and B 0.8, then P(A | B) = [...] = 0.875.
0.70/0.80

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#### Annotation 1637218651404

 When students were asked how they decided what to study next, 59% chose “Whatever’s due soonest/overdue,” and only 11% chose “I plan my study schedule ahead of time, and I study whatever I’ve scheduled.” Similarly, 86% of students re- sponded “no” when they were asked whether they usually returned to course material to review it after a course had ended—although the ideal answer to this question is yes. The responses of the meager 14% of students who said “yes” may be partly attributable to courses that spanned multiple academic terms.

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#### Annotation 1637221534988

 Self-testing . Overall, the results of our survey sug- gest that students either do not appreciate the benefits of spacing, or simply do not make choices to mass or space at all but rather attend to whatever is most urgent.

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#### Annotation 1637223107852

 A large percentage of participants (68%) seemed to realize the metacognitive benefits of testing, because their response to this question was “to figure out how well I have learned the information I’m studying.” Interestingly, however, only 18% thought of testing as a learning event

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#### Annotation 1637225467148

 Dropping material from further study . Our work on flashcards suggests that people often stop studying something once they feel they know it at the present time, meaning that they often stop too soon