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Test #1 Flashcards

(60 cards)

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1
Q

Statistics

A
  • the study of how to collect, organize, analyze, and interpret numerical information from data
  • both a science of uncertainty and the technology of extracting information from data
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2
Q

Individuals

A

the people or objects included in the study

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3
Q

Variable

A

a characteristic of the individual to be measured or observed

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4
Q

Quantitative Variable

A

has a value or numerical measurement for which operations such as addition or averaging make sense

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5
Q

Qualitative Variable

A

describes an individual by placing the individual into a category or group, such as male or female

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6
Q

Population data

A

the data from every individual of interest

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7
Q

Sample data

A

the data from some individuals or interest

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8
Q

Population Parameter

A

a numerical measure that describes an aspect of a population

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9
Q

Sample Statistic

A

a numerical measure that describes an aspect of a sample

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10
Q

Nominal level of measurement

A
  • applies to data that consist of names, labels or categories
  • no implied criteria by which the data can be ordered from smallest to largest
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11
Q

Ordinal level of measurement

A
  • applies to data that can be arranged in order

- differences between data values either cannot be determined or are meaningless=

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12
Q

Interval level of measurement

A
  • applies to data that can be arranged in order

- differences between values are meaningful

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13
Q

Ratio level of measurement

A
  • applies to data that can be arranged in order
  • both differences between data values and ratios of data values are meaningful
  • the data has a true zero
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14
Q

Descriptive statistics

A

involves methods of organizing, picturing and summarizing information from samples or populations

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15
Q

Inferential statistics

A

involves methods of using information from a sample to draw conclusions regarding the population

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16
Q

Simple random sample

A

a subset of the population selected in such a manner that every sample size of n from the population has an equal chance of being selected

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17
Q

Four levels of measurement

A

1) Nominal
2) Ordinal
3) Interval
4) Ratio

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18
Q

Steps to draw a random sample

A

1) Number all members of the population sequentially
2) Use a table, calculator, or computer to select random numbers from the numbers assigned to the population members
3) Create the sample by using members with numbers corresponding to those randomly selected

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19
Q

Sampling Techniques

A

1) Random Sampling
2) Stratified Sampling
3) Systematic Sampling
4) Cluster Sampling
5) Multistage Sampling
6) Convenience Sampling

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20
Q

Random Sampling

A

Use a simple random sample from the entire population

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21
Q

Stratified Sampling

A
  • Divide the entire population sequentially
  • Strata are based on a specific characteristic such as age, income, education level, and so on
  • All members of the stratum share the specific characteristic
  • Draw random samples from each stratum
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22
Q

Systematic Sampling

A
  • Number all members of the population sequentially

- from a starting point selected at random, include every kth member of the population sample

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23
Q

Cluster Sampling

A
  • Divide the entire population into pre-exiting segments or clusters
  • Clusters are often geographic
  • Make a random selection of clusters
  • Include every member of each selected cluster in the sample
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24
Q

Multistage Sampling

A
  • Use a variety of sampling methods to crate successively smaller groups at each stage
  • The final sample consists of clusters
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25
Convenience Sampling
Create a sample by using data from population members that are already available
26
Census
measurements or observations from the entire population are used
27
Observational Study
observations and measurements of individuals are conducted in a way that doesn't change the response or the variable being measured
28
Experiment
a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured
29
Placebo Effect
occurs when a subject receives no treatment but (incorrectly) believes he or she is in fact receiving treatment and responds favorably
30
Completely randomized experiment
one in which a random process is used to assign each individual to one of the treatments
31
Control group
receives a dummy treatment, enabling the researchers to control for the placebo effect.
32
Frequency table
partitions data into classes or intervals of equal width and shows how many data values are in each class
33
How to find class width
1) compute: (Largest data value - smallest data value)/ Desired number of classes 2) Increase the computed value to the next highest whole number
34
Lower class limit
the lowest data value that can fit in a class
35
Upper class limit
the highest data value that can fit in a class
36
Class width
the difference between lower class limit of one class and the lower class limits of the next class
37
How to find class boundaries
1) Add 0.5 to the upper class limits | 2) Subtract 0.5 from the lower class limits
38
Find relative frequency
f/n = class frequency/ total of all frequencies
39
How to make a frequency table
1) Determine the number of classes and the corresponding class width 2) Create the distinct classes 3) Fill in upper class limits to create distinct classes that accommodate all possible data values from the data set 4) Tally the data into classes. Total tallies are the class frequency 5) Compute the midpoint for each class 6) Determine class boundaries
40
Distribution Shapes
1) Mound-shaped symmetrical 2) Uniform or rectangular 3) Skewed left or right 4) Bimodal
41
Mound-Shaped Symmetrical
a mound-shaped histogram in which both sides are (more or less) the same when the graph is folded vertically down the middle
42
Uniform or rectangular
a histogram in which every class has equal frequency
43
Skewed left or right
a histogram that has a "tail" stretching out the the left or right
44
Bimodal
a histogram in which the two classes with the largest frequencies are separated by at least one class
45
Features of a bar graph
1) Bars can be vertical or horizontal 2) Bars are uniform width and uniformly spaced 3) The lengths of the bars represent values of the variable being displayed; the same measurement scale is used for the length of each bar 4) the graph is well annotated with title, labels for each bar, and vertical scale or actual value for the length of each bar 5) Useful for quantitative or qualitative data
46
Pareto Chart
- a bar graph in which the bar height represents frequency of an event; the bars are arranged from left to right according to decreasing height - identify the frequency of event s or categories in decreasing order or frequency of occurrence
47
Circle Graph or Pie Chart
- wedges of circle visually display proportional parts of the total population that share a common characteristic - display how a total is dispersed into several categories - Used with 10 or less categories - All data values must add up to 100%
48
Time-series Graph
- data are plotted in order of occurrence eat regular intervals over a period of time - Displays how data changes over a period of time
49
Mode
the value that occurs most frequently in a data set
50
Median
- the central value of an ordered distribution 1) order the dat from smaller to larges 2) for an odd number of data values in the distribution the median is the middle data value 3) For an even number of data values in the distribution, the median is the sum of the two middle values divided by 2
51
Mean
- Sum of all entires/ Number of entries | - Notation= x bar
52
Range
the difference between the largest and smallest values of a data distribution
53
How to compute sample variance and sample standard deviation
1) calculate the mean of the data values 2) find the difference between what happened and what you expected to happen (x - x bar) 3) Find the sum of the squares: - (x - x bar) ^2 - add together for every data point - you can also use sum of x squared - sum of x squared over n 4) divide by n-1 to find sample variance (s^2) 5) take the square root for the sample standard deviation
54
Population Mean
sum of x over N
55
Population Variance
sum of x - mean squared over N
56
Population of standard deviation
square root of sum of x - mean squared over N
57
Scatter Diagram
- a graph in which data pair are plotted at individual points on a grid with horizontal axis x and vertical axis y - x is the explanatory variable - y is the response variable
58
How to calculate r
1) compute the sum of x, sum of y, sum of x squared and sum of y squared, and the sum of xy 2) r= (n times the sum of xy)- (sum of x)(sum of y) / (square root of sum of x^2 - (sum of x) ^2) times (square root of y^2 - (sum of y) ^2)
59
R
sample correlation coefficient computed from a random sample of (x, y) data pairs
60
P
population correlation coefficient computed from all population data pairs (x, y)

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