SCO 2550 Week 1/ Chapter 1 Notes
SCO 2550 Week 1/ Chapter 1 Notes SCO 2550
U of M
Popular in Business Statistics: Data Sources, Presentation, and Analysis
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This 2 page Class Notes was uploaded by Colin Fritz on Sunday September 11, 2016. The Class Notes belongs to SCO 2550 at University of Minnesota taught by Kedong Chen in Fall 2016. Since its upload, it has received 5 views. For similar materials see Business Statistics: Data Sources, Presentation, and Analysis in Business statistics at University of Minnesota.
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Date Created: 09/11/16
SCO 2550 Chapter 1 Notes Colin Fritz The first chapter was fairly basic just covering the most basic of concepts so I will just cover the vocab and some of the concepts covered in the chapter. Content from here on will be much more in depth. Concepts to Know Statistical methods are used for analyzing populations of experimental data Descriptive statistics – it uses numerical and graphical methods to explore, summarize and present data in a concise and clear manor Inferential statistics – uses samples to estimate or predict ideas regarding a large set of data The next terms are all parts of one experiment for collecting data Experimental Unit – a set of units in which data is collected (an example of this is a person) Population – a set of experimental units (an example of this would be people) Variable – a characteristic of an experimental unit (an example of this would be gender) Measurement – giving numbers to variables Census- measuring a variable for every experimental unit of a population Statistical Inference – this is when you estimate or predict an idea of a population based on a sample Quantitative Data – data that is represented numerically Qualitative Data – data that is not represented numerically (an example of this is bad, good, and great for a customer service survey) Process – transforms inputs to outputs (raw materials into goods as an example) Black box – process whose operations are unknown The only other concept to understand is random samples know what they are and when to use each one, there are 4 types: Stratified random sampling – used when the population can be broken into two or more strata which are subsamples that share similar charecteristics – random samples are then obtained from the strata Cluster sampling – simply a more convenient way to group a sample (the example used in the textbook is taking 10 random locations of the 150 of a restaurant and survey the people from each of those locations) Systematic sampling – selecting every kth experimental unit from the entire list of units (surveying every 3 person in a line) Randomized response sampling – used when pollsters are suspected to elicit false answers, one of the questions is random which typically results in a more honest answer
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