Business Stat Study Guide Chapter 2 2 1 What are Data o Five W s Who what when where why Provide context for data values o Respondents o Subjects Participants o Relational database Individuals who answer a survey People getting experimented on Two or more separate data tables are linked together so that information can be merged across them 2 2 Variable Types o Categorical variable When a variable names o Quantitative variable When a variable has measured numerical values with units and the variable tells us about the quantity of what is measured o Identifier variable There are exactly as many categories as individuals and only one individual is in each category o Nominal variables Categorical variables used only to name categories o Ordinal values o Time series 2 3 Data Sources o Data mining For example choosing the first last or middle variable Same variable measured at regular intervals over time The analysis of large transactional data sets for purposes other than those for which the data that were collected Chapter 3 3 1 Three ideas of Sampling o Examine a part of the whole Sample survey Biased Designed to ask questions of a small group of people in the hope of learning something about the entire population The summary characteristics of a sample differ from the corresponding characteristics of the population it is trying to represent o Randomize Randomization 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 The variability from sample to sample is often referred to as sampling error even though no error has occurred o The Sample Size it what matters 3 2 A Census o Census Sample of the entire population o Really too much of a hassle 3 3 Population and Parameters o Parameters The key numbers in a model o Population parameter A parameter used in a model for a population Matching statistics with the parameters they estimate o Sample statistic o Representative The sample will estimate the corresponding parameters accurately at 3 4 Simple Random Sample least on average o SRS of being selected as well o Sampling frame o Sampling variability 3 5 Other Sample Designs o Stratified random sampling With this method each combination of individuals has an equal chance A list of individuals from which the sample will be drawn Slice the population into homogeneous groups and then use SRS within each stratum combining the results at the end o Cluster and Multistage Sampling Clusters Splitting the population in this way into parts We select one or a few clusters at random and perform a census Cluster sampling within each of them Multistage samples o Systematic Samples Systematic sample Sampling schemes that provide several methods Sometimes we draw a sample by selecting individuals systematically 3 6 Defining the population o Find out the who o You must specify the sampling frame o Get your target sample Nonresponse is an issue o Constraining the group as you move through the process 3 7 The Valid Survey o What do I want to Know A small sample is drawn from the sampling frame and a draft form of the survey instrument is administered o Who are the right respondents o What are the right questions o What will be done with the results o Measurement errors o Pilot test o Bad Sample Voluntary Response Sample Convenience Sampling Undercoverage Chapter 4 4 1 Three Rules of Data Analysis o Make a picture o Make a picture o Make a picture 4 2 Frequency Tables o Frequency table category names o Relative frequency table category 4 3 Charts o The Area Principle Shows the number of visits for each category and record totals and Displays the percentage rather than the counts of the values in each The area occupied by a part of the graph should correspond to the magnitude of the value it represents Displays the distribution of a categorical variable showing the counts for each category next to each other for easy comparison o Bar Charts o Pie Charts Categorical Data Condition That the data are counts or percentages of individuals in categories 4 4 Contingency Tables o Contingency table o Marginal distribution o Conditional Distributions condition on another o Segmented Bar Charts The table shows how the individuals are distributed along each variable depending on or contingent on the value of the other variable Shows the distribution of one variable for just those cases that satisfy a Treats each bar as the whole and divides it proportionally into segments corresponding to the percentage in each group o Simpson s Paradox Combining percentages and getting absurd numbers o
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