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# BNAD 276 Week 2 Notes BNAD 276 001

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Notes for week 2 of BNAD 276
COURSE
Statistic Inference in Management
PROF.
Dr. Suzanne Delaney
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Stats
KARMA
25 ?

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This 2 page Class Notes was uploaded by madelinef on Thursday September 1, 2016. The Class Notes belongs to BNAD 276 001 at University of Arizona taught by Dr. Suzanne Delaney in Fall 2016. Since its upload, it has received 45 views.

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Date Created: 09/01/16
BNAD 276 Notes Week 2  Measurement=observable actions  Placebo o Single blind- the subject does not know what group they are in o Double blind- the subject and experimenter do not know what group the subject is in  Continuous variable- variables that can assume any value between any two numbers. Ex: height, distance  Discrete variable- variables that can only assume whole numbers. NO intermediate values between the whole number. Ex: eggs, number of kids in class  Categorical data- also called qualitative date. A set of observable observations where any single observation is a word or number representing a class or category. Ex: gender, type of dog  Numeric Data- Quantitate data. A set of observations where any single observation is a number represented a count or amount. Ex: Temperature  4 Levels of measurement o nominal- classification, differs in kind, names of categories. Not numeric at all. Ex: categories, weakest, names, gender, jersey number, ethnic group o ordinal- order, rankings, differs in degree. More like a number but not a number still. Ex: place in a race, grade in school o interval- measureable, differs in amount, equal intervals. Ex: SAT score, IQ score, GPA, year, time o ratio- measureable differs in amount with a true zero. Ex: time to do something, distance, height, number of kids, bank account.  Farenheight temperature- interval  Kelvin- ratio because kelvin scale has absolute 0  Time series design- each observation represents a measurement at some point in time. Repeated measurement allows us to see trends  Cross sectional design- each observation represents a measurement at some point in time. Comparing lets us see differences  Census- measures each person in the specific population  Sample- measures a subset of population  Parameter- measurement or characteristic of the population o Usually unknown (estimated) o Represented by Greek letters  Statistic- numerical value calculated form a sample usually represented by roman letters o Descriptive statistic- organizing and summarizing data o Inferential statistic- generalizing beyond actual observations. Making inferences based on data collected  Simple random sampling- population has equal probability of being included  Sample frame - how you define population  Systematic random sampling- a probability sampling technique that involves selecting every person you are sampling from  Stratified sampling- sampling technique that involved dividing a sample into subgroups  Cluster sampling- divide a population subgroup into subgroups. Can either measure everyone or select subjects.  Non random sampling o Convenience sampling – sampling technique that involves people. A non-random sample and vulnerable to bias.  Snowball sample- non-random technique in which 1 or more members of a population are located and used to lead research to another member of the population. Used when we have no way to find them  Judgement sample- technique that involves sampling people who an expert said would be useful to sample  Scatterplot- displays relationship between two continuous variables  Correlation- measure of how two variable co-occur and also can be used for prediction o Range between -1 and +1. Closer to 0 the weaker the relationship and worse the prediction. o -1 and +1 are the same! The negative refers to if the values of 1 variable goes up the value for the other goes down. And positive values are if the one goes up, the three goes up o 0.9-0.7= strong predictor o 0.4-0.6= medium predictor o 0.1-0.3= weak predictor o 0= no relationship

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