Exam 1 Flashcards
1
Q
Data
A
- the facts and figures collected, analyzed, and summarized for presentation and interpretation
2
Q
Observation
A
- the set of measurements obtained for a particular element.
3
Q
Nominal Scale
A
- the data for a variable consists of labels or names (the order of the labels IS NOT meaningful).
4
Q
Ordinal Scale
A
- the data for a variable consists of labels or names (the order of the labels IS meaningful).
5
Q
Interval Scale
A
- numeric data where the interval between values is a fixed unit of measure.
6
Q
Ratio Scale
A
- the data have the properties of the interval scale, and the ratio of 2 values is meaningful.
7
Q
Categorical Data
A
- use labels or names to identify an attribute of an ele-ment.
8
Q
Quantitative Data
A
- Use of numbers
9
Q
Cross-Sectional Data
A
- are data collected at approximately the same point in time.
10
Q
Time Series Date
A
- data collected over several time periods.
11
Q
Statistical Inference
A
- The process of using data collected on a sample to draw conclusions about a population
12
Q
Population
A
- the set of all elements of interest in a particular study.
13
Q
Census
A
- The process of collecting data on the entire population
14
Q
Sample
A
- a subset of the population
15
Q
Sample Survey
A
- The process of collecting data on a sample
16
Q
Frequency Distribution
A
- Tabular summary of the data showing the number of items in each class.
17
Q
Relative Frequency Distribution
A
- Shows the proportion of items belonging to a class.
- Relative Frequency = Frequency/n
18
Q
Bar Graph
A
- Graph showing the frequency, relative frequency or percent frequency distribution.
19
Q
Pie Chart
A
- Presents the relative or percent frequency distribution.
20
Q
Histogram
A
- Graph that shows the frequency, relative frequency or percent frequency distribution
21
Q
Cumulative Frequency Distributions
A
- Shows the number of data items with values less than or equal to the upper class limit
22
Q
Stem and Leaf Display
A
- Shows the rank order of the data.
- Shows the shape of the data set
23
Q
Cross Tabulations
A
- Tabular summary of two variables
24
Q
Scatter Diagram
A
- a graphical representation for two quantitative variables.
25
Q
Sample Statistics
A
- Numbers that describe a sample
26
Q
Population Parameters
A
- Numbers that describe a population
27
Q
Mean
A
- Average Value
28
Q
Median
A
- Middle Value
29
Q
Mode
A
- Value that occurs most often
30
Q
Percentiles
A
- The pth percentile is the value with at least p percent of the observations less than or equal to it and at least (100p) percent of the observations greater than or equal to it.
31
Q
Quartiles
A
- 1st Quartile = 25th percentile
- 2nd Quartile = 50th percentile = median
- 3rd Quartile = 75th percentile
32
Q
Outliers
A
- observations that are much larger or smaller than the rest of the data.
33
Q
Small Outliers
A
- less than Q1 − 1.5(Q3 − Q1).
34
Q
Large Outliers
A
- greater than Q3 + 1.5(Q3 − Q1)
35
Q
Range
A
- The difference between the largest and smallest numbers in the data set.
36
Q
Interquartile Range (IQR)
A
- The difference between the third quartile and the first quartile.
37
Q
Variance
A
- The variance is based on the difference between each observation and the mean.
38
Q
Standard Deviation
A
- The square root of the variance.
39
Q
Z-Scores
A
- z-scores give the relative locations of observations within the data
- z-scores show how far a particular value is from the mean
- Z-Score = (Observation - Mean) / Standard Deviation
- z is the number of standard deviations the observation is from the mean
40
Q
Empirical Rule
A
- For a mound-shaped distribution (uni modal, symmetric, normal distribution) we can get better approximations
– 68.3% of the values of a normal random variable are within plus or
minus one standard deviation of its mean.
– 95.4% of the values of a normal random variable are within plus or minus two standard deviation of its mean.
– 99.7% of the values of a normal random variable are within plus or minus three standard deviation of its mean
41
Q
Probability
A
- a numerical measure of the likelihood that an event will occur
42
Q
Experiment
A
- a process that generates well defined outcomes
43
Q
Sample Space
A
- the set of all outcomes
44
Q
Classical Method
A
- Used when all the experimental outcomes are equally likely.
45
Q
Relative Frequency Method
A
- used when data are available to estimate the proportion of the time each outcome occurs
46
Q
Subjective Method
A
- used when we cannot assume that the outcomes are equally likely, and we have little relevant data available
47
Q
Event
A
- A collection of outcomes (or sample points)
48
Q
Complement of an event
A
- Suppose A is an event. The complement of A, denoted by Ac is another event that consists of all possible outcomes that are not in A.
49
Q
Union of Two Events
A
- If A and B are events, the union of A and B, denoted A ∪ B, is the event containing all outcomes that belong to A, B, or both.
50
Q
Intersection of 2 events
A
- If A and B are events, the intersection of A
and B, denoted A ∩ B, is the event containing all outcomes that belong to both A and B.
51
Q
The Addition Law
A
- P(A ∪ B) = P(A) + P(B) − P(A ∩ B)
52
Q
Mutually Exclusive
A
- If events A and B have no outcomes in common
- P(A ∪ B) = P(A) + P(B)
53
Q
Random Variable
A
- a numerical description of an experiment.
54
Q
Discrete Random Variable
A
- A random variable that assumes either a finite number of values or an infinite sequence of values such as 0, 1, 2
55
Q
Continuous Random Variable
A
- A random variable that may assume any numerical value in an interval or collection of intervals
56
Q
Probability Distributions
A
- describe how probabilities are distributed over the values of the random variable.
57
Q
Expected Value or Mean
A
- compute a measure of central location for a discrete random variable.
58
Q
Variance (Probability)
A
- measure the variability for a discrete random variable.
59
Q
Normal Distribution
A
- the most important probability distribution for continuous random variables.
