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IntroStat - (Chapter 1 7.8 Stat Applications and Types of...

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(Chapter 1, 7.8) Stat Applications and Types of Data Data : facts and figures collected, analyzed and summarized for presentation and interpretation. Data Set: all data collected in a particular study Elements : individual entities of a data set Variables : a characteristic of interest for the elements Observations : The set of measurements collected for a particular element Gender Class Miles from Home Credits Ice Cream Student 1 F Fresh 65 18 Chocolate Student 2 M Junior 1,005 12 Vanilla Student 3 M Senior 32 16 Rocky Road The ____________ is the entire spreadsheet full of data. Each student is an ____________ . For a class with 40 people, there will be 40 elements in the data set. The _______________ are gender, class, miles from home. The ___________ are (F, Fresh, 65, 18, Chocolate) for the first element. For a class of 40 students, there will also be 40 sets of observations. Types of Data Qualitative : labels or names used to identify an attribute of each element Nominal : order does NOT matter Ordinal : order DOES matter Quantitative : require numeric values that indicate how much or how many Interval: ratios of quantities cannot be compared Ratio: ratios of quantities have meaning
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Compare Qualitative and Quantitative: What type of variable is smoking status? What type of variable is SAT score? What type of variable is income? What type of variable is level of satisfaction? What type of variable is GPA? What type of variable is clothing size (s, m, l, xl)? What type of variable is time it takes to run a mile? Cross-Sectional: data collected at the same or approximately the same point in time Time Series Data: data collected over several time periods. Sources of Data Existing Sources : Student Records including: student ID, GPA, number of credits taking, courses Surveys : Experiments : Observational Studies :
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Types of Sampling: Simple Random Sampling : A sample is selected from a population in such a way that each element has the same probability of being selected. Sampling With Replacement : Elements are put back in the population after being selected for the sample allowing for a chance of being selected more than once for a single sample Sampling Without Replacement : Elements are not replaced after being selected and are therefore only chosen once to be in a sample.
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