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UA / Statistics / EPID 276 / What are variables that can only assume whole numbers?

What are variables that can only assume whole numbers?

What are variables that can only assume whole numbers?


School: University of Arizona
Department: Statistics
Course: Statistic Inference in Management
Professor: Suzanne delaney
Term: Fall 2016
Tags: Statistics
Cost: 25
Name: Week 2 BNAD 276 Notes (Statistics)
Description: These are the week two lecture and online notes for BNAD 276. Material includes levels of measurement, types of sampling, correlation, and different data.
Uploaded: 01/13/2016
5 Pages 182 Views 0 Unlocks

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  • Dependent Variable: what is being measured (y-axis)
  • Independent Variable: how groups differ (x-axis)
  • Single blind: participant doesn't know what group you're in
  • Double blind: experimenter nor participant doesn't know which group their in (placebo or not) helps eliminate error
  • Continuous variable: variables that can assume any value. There are (in principle) an infinite # of values between two numbers
  • Ex. distance time
  • Discrete variables: variables that can only assume whole numbers.
  • amount of sand = continuous
  • grains of sand in hond = discrete
  • # of kids in class = discrete
  • # of people in a population = continuous
  • Measurement: Observable actions
  • Theoretical constructs: concepts like humor or Satisfaction
  • Categorical data: also called qualitative data. a set of observations where any single observation is a word or number that represents a class or category.
  • Numerical data: quantitative. A set of observations were any single conservation is a number that represents an amount or count.

We also discuss several other topics like Do we still use horsepower today?

The Four levels of Measurement If you want to learn more check out What is the meaning of attack angle?

Categorical data

  • Nominal data - classification, differences in kind, names of categories
  • Ordinal data - order, rankings, difference in degree

If you want to learn more check out When is a time series considered a white noise?

Numeric data

  • Interval data - measurable differences in amount equal intervals.
  • Ratio data - measurable differences in amount with a "true zero"
  • distance, time height weight ← true zero

We also discuss several other topics like What sport is commonly known as stick ball?


temperature C/F: interval                                 type of dog: nominal

temperature Kelvin: Ratio                                 Grade (A, B, C): ordinalWe also discuss several other topics like lals ucsc

gender: nominal                                        test score: ratio

family size: ratio                                         GPA: interval

Jersey number: nominal                                IQ: intervalWe also discuss several other topics like What are the different ways that a limit can fail to exist?

place in race: ordinal                                         Age: Ratio

Age : ratio                                                me: Ratio

Yearly salary: ratio

Phone #: nominal

  • Time series design: each observation represents a measurement at some point in time. Repeated measurements allow us to see trends.
  • Cross-sectional design: each observation represents a measurement of some point in time comparing across groups allows us to see differences.
  • Do more young or old people call in sick? - CS
  • In the last 6 months, did more young or old call in sick? - CS

  • Census: measures each person in the specific population
  • Parameter: measurement of characteristic of the population
  • usually unknown
  • usually represented by greek letters

  • Statistic: numerical value calculated from a sample
  • usually represented by roman letters ()
  • Descriptive statistics: organizing and we summarizing data
  • Inferential stats: generalized beyond actual observations making "inferences" based on data collected
  • Simple Random Sampling: each person from the population has an equal probability of a being included
  • very difficult to do
  • must have a list of everyone in a population
  •  sample frame: how you define a population
  • ex: avg. weight of UA Football team
  • Systematic Random Sampling: A probability sampling technique that involves selecting every K+n (you pick the #) person from a sampling frame
  • Ex.
  • check every 200th light bulb
  • survey every 10th voter
  • Stratified Sampling: sampling technique that involves dividing sample into subgroups or strata, and then selecting samples from each of these groups
  • sampling technique can maintain ratios for the different groups
  • Cluster Sampling: sampling technique divides a population into subgroups (clusters) by region or physical space, soup
  • can measure everyone or select samples for each cluster

Non-random sampling

  • Convenience Sampling: Involves sampling people nearby
  • Snowball Sampling: non-random where one or more members of a population are located and used to lead the researcher to other members of the population
  • find someone and ask them to introduce them to more like them
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