Exam 1- Ch 1, 2, 3, & 6 Flashcards

0
Q

Statistics that collects, organizes, and presents the data

A

Descriptive Statistics

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1
Q

The methodology of extracting useful information from a data set

A

Statistics

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2
Q

Statistics that draws conclusions about a population based on sample data from that population

A

Inferential statistics

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3
Q

Consists of all items of interest

A

Population

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4
Q

A subset of the population

A

Sample

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5
Q

Data collected by recording a characteristic of many subjects at the same point in time, without regard to differences in time

A

Cross-sectional data

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6
Q

Data collected by recording a characteristic of a subject over several time periods

A

Time series data

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7
Q

Two types of variables

A

Qualitative and quantitative

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8
Q

This quantitative variable assumes a countable number of distinct values

A

Discrete

Ex: number of children, points scored in a game

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9
Q

Quantitative variable that can assume an infinite number of values within some interval

A

Continuous variable

Ex: weight, height, investment return

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10
Q

Scales of measure

A

Nominal
Ordinal
Interval
Ratio

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11
Q

The least sophisticated level of measurement

Data are simply categories for grouping the data

A

Nominal scale

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12
Q

Data may be categorized and ranked with respect to some characteristic or trait

Differences between categories are meaningless because the actual numbers used may be arbitrary

A

Ordinal scale

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13
Q

Data may be categorized and ranked with respect to some characteristic.

No “absolute 0”

A

Interval scale

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14
Q

Strongest level of measurement

There IS an “absolute 0”

A

Ratio scale

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15
Q

The characteristic of an observation or individual

A

Variable

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16
Q

The values associated with a variable

A

Data

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17
Q

By dividing each category’s frequency by the sample size, you get what?

A

Relative frequency

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18
Q

A segmented circle whose segments portray the relative frequencies of the categories of some qualitative variable

A

Pie chart

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19
Q

Depicts the frequency or the relative frequency for each category of the qualitative data as a bar rising vertically from the horizontal axis

A

Bar chart

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20
Q

Identifies the proportion or fraction of values that fall into each class

A

Relative frequency distribution

21
Q

Gives the proportion or fraction of values that fall below the upper limit of each class

A

Cumulative relative frequency distribution

22
Q

A visual representation of a frequency or a relative frequency distribution

23
Q

Shape of distribution

A

Typically symmetric or skewed

24
Shape of distribution that is a mirror image on both sides of its center
Symmetric
25
Shape of distribution where data forms a long, narrow tail to the right
Positively skewed
26
Shape of distribution where data forms a long, narrow tail to the left
Negatively skewed
27
A visual representation of a frequency or a relative frequency distribution
Polygon
28
A visual representation of a cumulative frequency or a cumulative relative frequency distribution
Ogive
29
Provides a visual display of quantitative data, it gives an overall picture of the data's center and variability
Stem and leaf diagram
30
The primary measure of central location
Arithmetic mean
31
Another measure of central location that is not affected by outliers
Median
32
Another measure of central location, by the most frequently occurring value in a data set
Mode
33
Measures of dispersion include:
Range Mean Absolute Deviation Variance and Standard Deviation Coefficient of Variation
34
? = maximum value - minimum value
? = range
35
An average of the absolute difference of each observation from the mean
Mean absolute deviation (MAD)
36
This adjusts for differences in the magnitudes of the means
Coefficient of variation
37
The manner in which data are distributed
Shape
38
A measure of symmetry of the lack of it
Soreness
39
A measure of whether data are peaked of flat relative to a normal distribution
Kurtosis
40
For any data set, the proportion of observations that lie within k standard deviations from the mean is at least 1-1/k^2, where k is any number greater than 1
Chebyshev's Theorem
41
Empirical Rule
Empirical Rule
42
Describes the likelihood that x assumes a value within a given interval
Probability density function
43
For any value x of the random variable X, the cumulative distribution function f(x) is computed as f(x) = P(X _< x)
Cumulative density function
44
Describes a random variable that has an equally likely chance of assuming a value within a specified range
Continuous uniform distribution
45
Closely approximates the probability distribution of a wide range of random variables
Normal distribution
46
Characteristics of normal distribution
Symmetric- about its mean Asymptotic- that is, the tails get closer and closer to the horizontal axis, but never touch it
47
Normal distribution is completely described by two parameters
u is the population mean which describes the central location of the distribution o^2 is the population variance which describes the dispersion of the distribution
48
A special case of the normal distribution
Standard normal (z) distribution
49
Gives the cumulative probabilities P(Z _< z) for positive and negative values of z
Standard normal table (Z table)