22s:152 applied linear regression review terminology...

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22s:152 Applied Linear Regression Chapter 3: Examining Data ———————————————————— Review types of data Quantitative : Continuous weights, heights, i.e. ‘decimal’ values Discrete finite (or infinitely countable) number of values is possible ‘counts’ fall into this category Qualitative : Categorical/Nominal gender: male/female color: green, blue, etc. Ordinal high/median/low 1 Review terminology Variable a particular characteristic that may take on dieret values for dierent subjects or experimental units. e.g. age, weight, chemical make-up Dependent variable the ‘response’ variable the thing we want to ‘predict’ Independent variable an explanatory variable (there may be many) a variable used to predict the response can also be called ‘covariate’ 2 Observation an observation is taken on a single subject, and corresponds to a row in a data matrix or table such as this... country infant.mortality gdp -------- ---------------- --- Afghanistan 154 2848 Albania 32 863 Algeria 44 1531 American.Samoa 11 NA Andorra NA NA Angola 124 355 an observation includes independent and dependent variables 3 Examining Data ———————————————————— Visual investigation of numerical data is important (and very useful) Are there outliers? Are there any ‘suspect’ data points? 40 60 80 100 120 40 60 80 100 120 140 160 reported weight actual weight ●● ●● ●● ●● ●● males females Davis, C. (1990) Body image and weight preoccupation: A com- parison between exercising and non-exercising women. Appetite, 15, 1321. 4

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  • 22s:152 Applied Linear Regression

    Chapter 3: Examining Data————————————————————

    Review types of data

    Quantitative:

    • Continuous weights, heights, i.e. ‘decimal’ values

    • Discrete finite (or infinitely countable)number of values is possible

    ‘counts’ fall into this category

    Qualitative:

    • Categorical/Nominalgender: male/female

    color: green, blue, etc.

    • Ordinal high/median/low1

    Review terminology

    • Variable– a particular characteristic that may take

    on differet values for different subjects orexperimental units.

    e.g. age, weight, chemical make-up

    • Dependent variable– the ‘response’ variable

    – the thing we want to ‘predict’

    • Independent variable– an explanatory variable (there may be many)

    – a variable used to predict the response

    – can also be called ‘covariate’

    2

    • Observation

    – an observation is taken on a single subject,and corresponds to a row in a data matrixor table such as this...

    country infant.mortality gdp

    -------- ---------------- ---

    Afghanistan 154 2848

    Albania 32 863

    Algeria 44 1531

    American.Samoa 11 NA

    Andorra NA NA

    Angola 124 355

    – an observation includes independent anddependent variables

    3

    Examining Data————————————————————Visual investigation of numerical datais important (and very useful)

    • Are there outliers?• Are there any ‘suspect’ data points?

    40 60 80 100 120

    4060

    8010

    012

    014

    016

    0

    reported weight

    actu

    al w

    eigh

    t

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    malesfemales

    Davis, C. (1990) Body image and weight preoccupation: A com-

    parison between exercising and non-exercising women. Appetite,

    15, 1321.

    4

  • • Is there anything unusual in the data?

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