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TERMS FOR BUSINESS STATISTICSdescriptive statistics: statistical procedures used to describe a population being studied, only can be used to describe the group that is being studied (non-generalized)inferential statistics: making predictions or inferences about a population from observations and analyses of a sample (generalize)data values (observations): information collected regarding some subjectdata: numbers, names, etc. (tells us the who and what)respondents: individuals who answer a surveysubjects/participants: people in an experimentexperimental units: animals, plants, websites (inanimate objects)variable: the aspect/characteristic that differs from subject to subject, individual to individual (what is being measured/“columns”)data: the value of the variablescategorical (qualitative) variable: when a variable names categories and answers questions about how cases fall into these categories-sex, year in school, major-descriptive responses to questions like “what kind of advertising do you use?” with possible answers like “newspapers” “internet” “direct mail”-may only have 2 possible values like “yes” and “no”-ordinal variable: categories that have a natural ordering-numbers could be assigned to categories-EX: Class Rank-1= Freshman-2= Sophomore-3= Junior-4= Senior-EX: Grade A, B, C, D, F (GPA)-EX: Preference: Strongly Agree, Agree, Disagree, Strongly Disagreenominal variables: categories that have no natural ordering-EX: Major: business, mathematics, history-EX: Eye Color: blue, green, brownnumerical (quantitative) variable: when a variable has measured numerical values with units and the variable tells us about the quantity of what is measured-EX: age, height, miles traveleddiscrete variables: there is a natural gap between the values-EX: # of children-EX: # of credit cardscontinuous variables: the values can be arbitrarily close together-EX: Weight-EX: Height-EX: Ageidentifier variable: a unique identifier assigned to each individual or item in a group-EX: social security number, student ID number, tracking number, transaction numberinterval data-no meaningful zero point; cant multiply or divide but the difference between 2 values is meaningful-temperatureratio data-meaningful zero point; can multiply and divide-income, weight, heighttime series data-variables that are measured at regular intervals over time-determining total costs each month of a yearcross-sectional data-several variables are all measured at the same time point-determining sales revenue, number of customers, and expenses for the last month of businessData Sources – Where, How, Whenwhen : data are collected can be important-data that are decades old may mean something different than similar values recorded last yearwhere : data are collected can be important-data collected in Mexico may differ in meaning than data collected in the UShow : data are collected can make the difference betweeninsight and nonsense-data that comes from a voluntary survey on the Internet are almost always worthless-however, data provided by agencies and businesses on websites can be extremely useful1: Examine a Part of the Whole-the first idea is to draw a sample-goal: learn about an entire population of individuals, but examining all of them is not feasible-sample is chosen from the population and examined-samples that over/under emphasize some characteristics of the population are said to be biasedBias: sample doesn’t represent population-generalizations no longer valid-conclusions may no longer be trueSelection Bias-problem in sampling scheme; systematic tendency to exclude one kind of individual from the survey-difference between population of interest and effective population-EX: cell phones, multiple phonesNon-Response Bias-subjects don’t answer-skip questions-EX: answering machinesResponse Bias-subjects lie-interviewer effectself selected sample-more passionate more likely to respond-minority opinion more passion --- opposite of the truth2. Randomize-randomization can protect you against factors that you know are in the data-it can also help protect against factors you are not even aware of-randomization gets rid of biases-randomizing makes sure that on the average the sample looks like the rest of the population-sampling error: what sample-to-sample differences are referred to3. Sample Size is What Matters-it is the size of the sample, not the size of the population, thatmakes the difference in sampling-the fraction of the population that you have sampled doesn’t matter, it is the sample size itself that is importantpopulation: the entire group of individuals in which we are interested but can’t usually assess directly-EX: all voters in US, Visa card holders in DC, all packages at a UPS centerparameter: a number describing a characteristic of the populationsample: the part of the population we actually examine and forwhich we do have datastatistic: a number describing a characteristic of a sampleSampling TechniquesNonstatistical Samplingconvenience: collected in the most convenient manner for the researcher (ask whoever is around)-Bias: opinions are limited to individuals presentvoluntary: individuals choose to be involved, very susceptible to being biased because different people are motivated to respond or not. Often called “public opinion polls”, these are not considered valid or scientific-Bias: sample design systematically favors a particular outcomeStatistical SamplingIndividuals in the sample are chosen based on known or calculable probabilitiesSimple random sampling (SRS): every possible sample of a given size has an equal chance of being selected-draw names out of a hat-use a random number tablesampling frame: list of population-EX: phone book, registered voter list, membership liststratified random sampling: divide population into subgroups (strata) according to some common characteristic-EX: genderselect a SRS from each subgroup, and combine samples from subgroups into onecluster sampling: divide population into several “clusters” each representative of the population-EX: countyselect a SRS of clusters-all items in selected clusters can be used, or items can be chosen from a cluster using another probability sampling techniquesystematic random sampling: decide on sample size (n), divide ordered (EX: alphabetical) frame of (N) individuals into groups of (k) individuals [k=N/n]randomly select one individual from the 1st group, and select every kth

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