Statistics Chapter 1 - Introduction, Data, and Statistics

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  1. What are 3 reasons to use statistics?
    • To make sense of large amounts of data
    • To test hypothesis
    • To make predictions
  2. What is data?
    Measurements, facts, and/or numbers
  3. What is a dataset?
    All the data collected for one study
  4. What are data elements (or units of observation)?
    Entities on which data are collected
  5. What is a variable?
    A characteristic of interest of the elements
  6. What is an observation?
    • Values of all variables for one element
    • Set of measurements for one element
  7. What are the 4 scales of measurement?
    • Nominal 
    • Ordinal
    • Interval
    • Ratio
  8. What are the characteristics of a nominal scale?
    • Label or name is used
    • Data do not suggest any particular order
    • Example: YSU students are classified by college/school/class
  9. What are the characteristics of an Ordinal scale?
    • Non-numeric, but order or rank is meaningful
    • Example: freshman, sophomore, junior, senior
  10. What are the characteristics of an Interval scale?
    • Ordered, numeric
    • Equal and fixed distances between points on the measurement scale.
    • Does not have a true zero
    • Example: temperature scale, SAT scores
  11. What are the characteristics of a Ratio scale?
    • Ordered, numeric, with real zero
    • Ratio of two values is meaningful
    • Example: income, distance, price, quantity
  12. What scales are used for qualitative data?
    • Nominal
    • Ordinal
  13. What scales are used for quantitative variables?
    • Interval
    • Ratio
  14. What is cross-sectional data?
    • Collected at (approximately) same point in time
    • Example: US imports measured in 2005 vs. China
  15. What is time series data?
    • Collected over several time periods
    • Example: Weekly earnings of private nonfarm workers in 1982 dollars (accommodating inflation)
  16. What are descriptive statistics?
    • Summaries of data, which may be tabular, graphical, or numerical.
    • Makes data easier to understand, more meaningful
  17. What are tabular methods to display descriptive statistics?
    • Frequency table (one variable)
    • Crosstabulation (more than one variable)
  18. What are the graphical methods ti dispaly descriptive statistics?
    • Bar graph (qualitative or categorical variables)
    • Histogram (quantitative variables)
    • Scatter plot (two variables)
  19. What is the purpose of Inferential Statistics?
    Draw conclusions, make predictions, or infer something about the population (the whole  from looking at the sample (the part)
  20. What is the population?
    Total category under consideration
  21. What is the sample?
    Subset of population for analysis
  22. What is a parameter?
    • Characteristic of population
    • Example: population mean (greek u)
  23. What is a statistic?
    • Measure derived from sample data
    • Example: sample mean (x bar)
  24. What is the main difference between descriptive and inferential statistics?
    Inferential is the process of using sample statistics to draw conclusions about population parameters. So, it's the interpretation from the descriptive statistics.
  25. When do errors most commonly occur in statistics?
    When collecting the data
  26. What is a census?
    Conducting a survey to collect data for the entire population
  27. What is a sample survey?
    Conducting a survey to collect data for the sample
  28. What is data mining?
    Methods for developing useful decision-making information from large databases

Card Set Information

Statistics Chapter 1 - Introduction, Data, and Statistics
2013-04-11 16:17:25
data dataset observation nominal ordinal population stats

Covers basic terms
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