School

University of MarylandDepartment

Business and ManagementCourse Code

BMGT 230Professor

Radu LazarStudy Guide

FinalThis

**preview**shows pages 1-3. to view the full**23 pages of the document.**UMD

BMGT 230

FINAL EXAM

STUDY GUIDE

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

BMGT230/BMGT230B

Business Statistics: A First Course

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

something about the entire population.

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.

find more resources at oneclass.com

find more resources at oneclass.com

###### You're Reading a Preview

Unlock to view full version

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

BMGT230/BMGT230B

Business Statistics: A First Course

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.

find more resources at oneclass.com

find more resources at oneclass.com

###### You're Reading a Preview

Unlock to view full version