Chapter 3 ( SEMI FINALS) Flashcards

1
Q

is the practice or science of collecting and analyzing numerical data in large quantities,

A

Statistics

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2
Q

is a central or typical value for a probability
distribution.

A

Central tendency

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3
Q

It may also be called a center or location of the distribution.

A

Central tendency

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4
Q

The most common measures of central tendency are the

A

arithmetic mean
the median
the mode

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5
Q

is the sum of all measurements divided by the number of observations.

A

Arithmetic Mean

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6
Q

simply an average of the data.

A

Mean

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7
Q

is the “midpoint” of our data that separates the upper and lower half of the data set.

A

Median

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8
Q

the most frequent value in the data set.

A

Mode

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9
Q

This is the only central tendency measure that
can be used with nominal data, which have purely qualitative category assignments

A

Mode

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10
Q

of a distribution is typically contrasted with its dispersion or variability;

A

Central Tendency

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11
Q

also called
variability, scatter, or spread

A

Dispersion

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12
Q

is simply the difference between the smallest and largest data point in the set.

A

Range

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13
Q

is the average of the absolute deviations from a central point.

A

Mean Absolute Deviation

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14
Q

It is a summary statistic of statistical dispersion or variability.

A

Mean Absolute Deviation

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15
Q

is another way of measuring the spread between numbers in a data set.

A

Variance

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16
Q

it measures how far each number in the set is from the mean.

17
Q

is simply the square root of the Variance.

A

Standard Deviation

18
Q

It is the most commonly used
measure to express dispersion.

A

Standard Deviation

19
Q

are cut points dividing the range of a probability distribution into continuous intervals
with equal probabilities, or dividing the observations in a sample in the same way.

20
Q

Dive the data into 4 parts

21
Q

divide data into 10

22
Q

divide into 1% segments

A

Percentiles

23
Q

Quartiles are
often used as a measure of spread of the data in what is called the

A

interquartile range (IQR

24
Q

simply the difference between the third quartile and first quartile.

A

Interquartile Range

25
is a way of standardizing scores on the same scale by dividing a score's deviation by the standard deviation in a data set.
A z-score, or standard score,
26
it measures the number of standard deviations a given data point is from the mean.
Z-score or Standard Score
27
represents the ratio of the standard deviation to the mean,
Coefficient of Variation
28
allows investors to determine how much volatility, or risk, is assumed in comparison to the amount of return expected from investments.
Coefficient of Variation
29
refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data.
Skewness
30
means the Mean is greater than the Median
Positive Skew
31
means the Mean is less than the Median.
Negative Skew
32
is a statistical measure that expresses the extent to which two variables are linearly related
Correlation
33
meaning they change together at a constant rate
Correlation
34
It’s a common tool for describing simple relationships without making a statement about cause and effect.
Correlation
35
Colloquially, measures of central tendency are often called
Averages
36