6+7: Data analysis I & II: descriptive & inferential statistics Flashcards

1
Q

6(7) basic statistics applied to levels of measurement to measure 2 properties

A
  • nominal scales
    • central tendency –> mode
  • ordinal scales
    • central tendency –> median
    • dispersion –> range (but quartile distance in presence of extreme value!)
  • interval & ratio scales
    • central tendency –> mean
    • dispersion –> variance & standard deviation
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2
Q

statistical difference

  • def=
  • it is like…
  • a diff could…
A
  • A statistical difference is a function of the difference between means relative to the variability.
  • Like a signal-to-noise ratio.
  • a diff could be due to chance, esp. if variability is high
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3
Q

t-test def=

+ 3 associated values

A

The t-test assesses whether the means of two groups are statistically different from each other

+ 3 associated values

  • t-value = ( avg_T - avg_C ) / SE( avg_T - avg_C )
  • p-value = probability of t-value due to randomness only
  • alpha level = significance level, often 0.05, used as threshold
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4
Q

null VS alternative hypothesis

A

null H is conservative, alternative H represents a change as compared to the current state of knowledge

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5
Q

Type I error

& alpha level

in hypothesis testing

A

= falsely rejecting a null H

w probability p = alpha level (commonly set at 5%)

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6
Q
  • test statistic
  • used for
  • properties
  • sample types
A
  • a statistic that reflects the ratio of systematic over unsystematic variation
  • used for statistical tests
  • it has a known distribution, so that you can calculate the p-value
  • t, F statistics
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7
Q

3 important test types and when to use them

A

numerical dep. variable VS numerical indep. variable => regression analysis

numerical dep. variable VS categorical indep. variable

=> ANOVA or t-test

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8
Q

ANOVA meaning

A

ANalysis Of VAriance

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9
Q

regression analysis w a linear model:

  • model
  • method
A
  • = linear equation + probabilistic error term
  • => minimize distance from empirical measurements & predictions; typically, using least square distance(least squares method)
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10
Q

attention point w large datasets

A

sample size tends to make every parameter significant; to check for that, it is necessary to consider the R squared value.

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11
Q

standardizing variables =

what for?

A

subtract mean, then divide by stdev

to make them comparable!

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12
Q

ANOVA compares 2 elements:

+ name of independent variables in ANOVA

A
  • variation between the groups
  • variation within the groups

+ independent variables in ANOVA are called factors

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