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# [BMGT 230] - Final Exam Guide - Ultimate 23 pages long Study Guide!

Department
Course Code
BMGT 230
Professor
Study Guide
Final

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UMD
BMGT 230
FINAL EXAM
STUDY GUIDE

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BMGT230/BMGT230B
Chapter 1: Data
What Are Data?
Data: Systematically recorded information, whether numbers or labels, together with its context.
Data aren’t always in the form of numbers. Data can also be names, labels, etic.
Data values, no matter what kind, are useless without their context.
oContext: the “Five W’s:” Who, What, Where, When, Why, and oftentimes How.
oWho was measured? The cases
oWhat was measured? The variables
oWhere was the data collected?
oWhen and Why was the study performed?
oHow was the data collected?
oThe Who and the What are the two essential questions that need to be answered.
Data table: An arrangement of data in which each row represents a case and each column represents
a variable.
Individuals who answer a survey are referred to as respondents.
People on whom we experiment are subjects or participants.
Animals, plants, websites, and other inanimate subjects are often called experimental units.
The characteristics recorded about each individual or case are called variables.
Variable Types
When a variable names categories and answers questions about how cases fall into those
categories, we call it a categorical variable.
oCategorical variables used only to name categories are sometimes called nominal variables.
oData for which some kind of order is available but for which measured values are not
available are ordinal variables.
When a variable has measures numerical values with units and the variable tells us about the
quantity of what is measured, we call it a quantitative variable.
oThe units tell how each value has been measured.
Sometimes, the same variable may be viewed as categorical or quantitative depending on the
situation.
When there are exactly as many categories as individuals and only one individual in each
category, this is an identifier variable.
oEx. Student ID number
Times Series: Data measured over time. Usually the time intervals are equally spaced (e.g., every
week, every quarter, or every year)
Cross-sectional data: Data taken from situations that vary over time but measured at a single time
instant is said to be a cross-section of the time series.
Data Sources—Where, How, and When
Survey: A study that asks questions of a sample drawn from some population in the hope of learning
Experiment: A study in which the researcher manipulates factor levels to assess the effect of the
factor on the response.
Observational study: A study based on data in which no manipulation of factors has been employed.
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BMGT230/BMGT230B
Chapter 8: Surveys and Sampling
Three Ideas of Sampling
Idea 1: Examine a Part of the Whole
Population: The entire group of individuals or instances about whom we hope to learn.
Sample Survey: A study that asks questions of a sample drawn. Individuals in the population of
interest but who are not in the sampling frame cannot be included in any sample.
Bias: Any systematic failure of a sampling method to represent its population.
Idea 2: Randomize
Randomization: A defense against bias in the sample selection process, in which each individual is
given a fair, random chance of selection.
Nobody can guess the outcome before it happens.
When we want things to be fair, usually some underlying set of outcomes will be equally
likely.
Sampling Error (or Sampling Variability): The natural tendency of randomly drawn samples to
differ, one from another.
Idea 3: The Sample Size Is What Matters
The size of the sample determines what we can conclude from the data regardless of the size of the
population. Aim to get a “representative” sample—depends of the situation.
Representative sample: A sample in which the statistics computed accurately reflect the
corresponding population parameters.
A Census—Does It Make Sense?
Census: An attempt to collect data on the entire population of interest.
While a census may appear to provide the best possible information about the population:
It can be difficult to complete a census.
The population we’re studying may change.
Taking a census can be cumbersome.
Populations and Parameters
Parameter: A numerically valued attribute of a model for a population. We rarely expect to know
the value of a parameter, but we do hope to estimate it from sampled data.
Population parameter: A numerically valued attribute of a model for a population.
Sample: A subset of a population, examined in hope of learning about the population.
Statistic, sample statistic: A value calculated from sampled data, particularly one that corresponds
to, and thus estimates, a population parameter. The term “sample statistic” is sometimes used,
usually to parallel the corresponding term “population parameter.”
Simple Random Sample (SRS)
Simple random sample (SRS): A sample in which each set of n elements in the population has an
equal chance of selection.
The standard against which we measure other sampling methods, and the sampling method
on which the theory of working with sampled data is based.
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