quantitative business tools 1 final

Flashcard maker : Lily Taylor
The science of the collection, organization, and interpretation of numerical data.
•The analysis of population characteristics by inference from sampling.
collections of observations (measurements, survey responses, etc.)
The complete collection of all individuals to be studied.
The collection of data from every member of the population.
A subset of the population
Inferential Statistics
Mathematical methods used to infer the properties of a population from the analysis of the properties of a sample drawn from it.
Parameter !
Numerical measurement describing a characteristic of a population.
Statistic !
Numerical measurement describing a characteristic of a sample.
Quantitative data
numbers representing counts or measurements.
•The number of students who are in-state residents.
The weights of students at UCF
Categorical (qualitative) data
•Names or labels (representing categories). Can be numerical
•Jersey numbers
Discrete data !
The number of possible values is either a finite number, or a “countable” number (0, 1, 2,…)
•The number of students enrolled in ECO 3401 (0 – 350)
•The number of students enrolled at UCF.
Continuous data !
Within an interval there are an infinite number of possible values.
•The distance a student travels between home and school
Nominal !
Data that consist of names, labels, or categories only
•Cannot be arranged in an ordering scheme (such as low to high)
•Ex.: Survey responses (yes, no, undecided)
Data that can be arranged in some order.
•Differences between data values either cannot be determined or are meaningless
•Ex.: BCS college football rankings.
Data that can be arranged in some order.
•Difference between any two values is meaningful.
•No natural zero starting point, where zero means the absence of any quantity.
•Ex.: Temperature.
Data that can be arranged in some order.
•Difference between any two values is meaningful.
•There is a natural zero starting point, where zero means the absence of any quantity.
•Ex.: The number of points earned on a test.
Deceptive Statistics
Evil intent
Unintentional errors
Misuse of graphs
To correctly interpret a graph, you must analyze the numerical information given in the graph, so as not to be misled by the graph’s shape. READ labels and units on the axes!
Exaggerate the difference by increasing each dimension in proportion to the actual amounts
Bad samples
Voluntary response samples
•Internet surveys
•valid conclusions can be made only about the specific group of people who agree to participate and not about the population.
Concluding that one variable causes the other variable when in fact the variables are linked
•Two variables may seemed linked, smoking and pulse rate, this relationship is called correlation. Cannot conclude the one causes the other.
Reported results
When collecting data from people, it’s better to take measurements yourself instead of asking subjects to report results.
Small samples
Conclusions should not be based on samples that are too small.
•Children Out of School in America (Children’s Defense Fund, 1974): Among secondary school students suspended in Los Angeles County, 67% were suspended at least 3 times.
•Sample size: 3
Loaded questions
Survey questions can be “loaded” or intentionally worded to elicit a desired response.
•In a recent study using two different randomly selected groups:
Order of questions
Questions are unintentionally loaded by such factors as the order of the items being considered.
•Does traffic contribute more or less to air pollution than industry? Results: 45% said traffic, 27% said industry
•Does industry contribute more or less to air pollution than traffic ? Results: 57% said industry, 24% said traffic
Observational Study
Observing and measuring specific characteristics without attempting to modify the subjects being studied.
Experiment !
Apply some treatment and then observe its effects on the subjects
Random Sample !
members from the population are selected in such a way that each individual member in the population has an equal chance of being selected
Simple Random Sample
selected in such a way that every possible sample of the same size n has the same chance of being chosen
Systematic Sampling
Select some starting point and then select every ‘K’th element in the population
Convenience Sampling
use results that are easy to get
Stratified Sampling
subdivide the population into at
least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum)
Cluster Sampling
ivide the population area into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters

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