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by: Megan Lester

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MGTOP 215 Chapter 1 MGTOP 215

Megan Lester
WSU
GPA 2.8

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Class notes
COURSE
PROF.
Sung Ahn
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Stats
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This 2 page Class Notes was uploaded by Megan Lester on Thursday September 22, 2016. The Class Notes belongs to MGTOP 215 at Washington State University taught by Sung Ahn in Spring 2015. Since its upload, it has received 5 views. For similar materials see Business Statistics in Business at Washington State University.

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Date Created: 09/22/16
Chapter One 1/13 - 1/16 Friday, January 16, 2015 10:07 AM MgtOp 215 Chapter 1 Dr. Ahn Statistics: 1) commonlymeans a collection of numerical facts or data. 2) a science of collecting, presenting, analyzing, and interpreting numerical data. The word statistics originally meant the collection of facts useful to the person involved with the affairs of state. Why do we learn statistics? 1) to have a better feel for statistics:what it is; how and when to apply statistical techniques to decision making situations; how to interpret the results. 2) to develop analytical skills and critical thinking. The field of statistics 1) descriptivestatistics:organizing and summarizing numerical data in order to describe the various features of the data (Chaps. 2 and 3). 2) inferential statistics:procedures and methodsthat can be used to make inferences (or generalization), predictions, and decisions about the population using the information contained in a sample (Chaps. 8 and on). Probability: 1) commonlymeans a chance. 2) a mathematicalmeans of studying uncertainty, and used as a tool for inferential statistics (Chaps. 4, 5, 6, and 7). Variable: a characteristicof items or individuals. Population: a collectionof all the elements under study (about which we are trying to draw a conclusion) or a collectionof all the measurementsunder study. Sample: a part of the population. Parameter: a numerical descriptive measure that represents a characteristic of the population. Statistic: a numerical descriptive measure from a sample. “Statistics is a science of getting cheaper but good-quality information,although not perfect, about the population, especially about the parameter, instead of getting perfect informationwhich tends to be infeasible and very expensive.” “It is better to be roughly right than to be precisely wrong.” Maynard Keynes Census: evaluation of each and everyunit (element)in the population under study; the census data contain complete(and perfect) information about the population. Difficulties of collecting the census data: 1) expensive (especially when the population is large), 2) time consuming, 3) could be imperfectas they are more prone to human errors and due to lies, 4) may not be feasible (especially when measurementrequires destruction of unit). 4) may not be feasible (especially when measurementrequires destruction of unit). A “representative”sample contains the relevant characteristic of the population, even though the information contained in the sample is imperfect. Simple random sample: a sample drawn such a way that each element of the population has the equal chance of being selected. Three ways of taking a simple random sample: 1) draw tickets out of a hat; 2) use random number tables (an example is on Table E.1 on pp. 796-797and discussion pp.251-252); 3) use MS Excel: Formulas > Insert Function ( x f ) >Math & Trig>RANDBETWEEN. Hard Copy (HC) Homework: Visit the companion Web-site (www.pearsonhighered.com/levine)as described in Appendix C.1 on p. 784 and down load the data file “Bond Funds.” Then randomly select 10 bond funds and list their names (or fund number) along with their types and returns of 2009.(Since the names are between rows 2 and 185, it is convenient to draw numbers between 2 and 185, inclusive.) Data are the different values associated with variables, and can be thought of the information needed to help us make informed decision. Sources of data: • Primary:the data collector using the data for analysis • Secondary: the person analyzing the data is not the data collector Types of data: • qualitative: nonnumeric and categorical • quantitative: numeric ○ discrete: assumes only specific points on a scale ○ continuous: assumes all values within an interval Levels of measurements: • The nominal scale applies to data that are divided into different categories, and these categories are used for identification purposes, e.g., female=0,male=1; ISP: Verizon=1, , AT&T=2, Sprint=3, T-Mobile=4,Other=5. • The ordinal scale applies to data that are divided into different categories that can be ranked, e.g., bad=0, fair=1, good=2; bond ratings: AAA, AA, A, BBB, BB; Student grades: A, B, C, D, F, etc. • The interval scale applies to data that can be ranked and for which the difference between two values can be calculated and interpreted and do not involve a true zero point, e.g., temperature, calendar time, SAT score. • The ratio scale applies to data that can be ranked and for which the ratio between two values can be calculated and interpreted, e.g., income, weight. MyStatLab (MSL) Homework: 1.3, 1.5, 1.7 on pp. 9-10 (These are labeled in MyStatLab as 1.4.3, 1.4.5, 1.4.7) Cross sectional data are collected on different elementsat the same point in time or for the same period of time. Time series data are collected on the same element for the same variable at different points in time or different periods of time. Usually line graphs or bar graphs are used.

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