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ISDS 2000 > Exam 1- Ch 1, 2, 3, & 6 > Flashcards

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

A

Histogram

23
Q

Shape of distribution

A

Typically symmetric or skewed

24
Q

Shape of distribution that is a mirror image on both sides of its center

A

Symmetric

25
Q

Shape of distribution where data forms a long, narrow tail to the right

A

Positively skewed

26
Q

Shape of distribution where data forms a long, narrow tail to the left

A

Negatively skewed

27
Q

A visual representation of a frequency or a relative frequency distribution

A

Polygon

28
Q

A visual representation of a cumulative frequency or a cumulative relative frequency distribution

A

Ogive

29
Q

Provides a visual display of quantitative data, it gives an overall picture of the data’s center and variability

A

Stem and leaf diagram

30
Q

The primary measure of central location

A

Arithmetic mean

31
Q

Another measure of central location that is not affected by outliers

A

Median

32
Q

Another measure of central location, by the most frequently occurring value in a data set

A

Mode

33
Q

Measures of dispersion include:

A

Range
Mean Absolute Deviation
Variance and Standard Deviation
Coefficient of Variation

34
Q

? = maximum value - minimum value

A

? = range

35
Q

An average of the absolute difference of each observation from the mean

A

Mean absolute deviation (MAD)

36
Q

This adjusts for differences in the magnitudes of the means

A

Coefficient of variation

37
Q

The manner in which data are distributed

A

Shape

38
Q

A measure of symmetry of the lack of it

A

Soreness

39
Q

A measure of whether data are peaked of flat relative to a normal distribution

A

Kurtosis

40
Q

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

A

Chebyshev’s Theorem

41
Q

Empirical Rule

A

Empirical Rule

42
Q

Describes the likelihood that x assumes a value within a given interval

A

Probability density function

43
Q

For any value x of the random variable X, the cumulative distribution function f(x) is computed as f(x) = P(X _< x)

A

Cumulative density function

44
Q

Describes a random variable that has an equally likely chance of assuming a value within a specified range

A

Continuous uniform distribution

45
Q

Closely approximates the probability distribution of a wide range of random variables

A

Normal distribution

46
Q

Characteristics of normal distribution

A

Symmetric- about its mean

Asymptotic- that is, the tails get closer and closer to the horizontal axis, but never touch it

47
Q

Normal distribution is completely described by two parameters

A

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
Q

A special case of the normal distribution

A

Standard normal (z) distribution

49
Q

Gives the cumulative probabilities P(Z _< z) for positive and negative values of z

A

Standard normal table (Z table)

ISDS 2000 (1 decks)

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