Flashcards in Lecture 2 Deck (22)
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1
4 aspects needed to correctly describe a single quantitative variable
Centre, spread (different ways of measuring spread), Skew (+,-,symmetric), weird things (outliers, multiple modes)
2
Centre
Where most data located
3
Spread (2)
Over what range we see most of the data/how much alike/different the observations are
4
Skew
What direction does the spread extend to
5
Weird things
Some points really far away (outliers) or 2 centres
6
Dotplot advantages and disadvantages
Get to see all data points, Easy to interpret but gets messy quickly if there are a lot of data
7
Histogram advantages and disadvantages
Easy to pick up (have an idea of ) on centre, spread, skew, multiple modes and even outliers + Made by most statistical packages (programs) but Different bin widths could give different interpretations of the data
8
Mode
Number that occurs most often
9
We say that the median is _____________ to outliers
robust (an outlier won't change it)
10
Mean advantages and disadvantages
Good for estimating population means and good inferential properties but affected by outliers and skewed data
11
Median advantages and disadvantages
Easy to interpret, Not influenced by outliers but bad inferential properties and longer to calculate
12
Mode advantages and disadvantages
Highest concentration of data and we can see bimodal data but class definition matters (precision des intervalles, peu precis peut avoir plusieurs modes et trop precis fait aucun mode)
13
Population and sample mean notation
Population mean : µ
Sample mean : x barre ou X barre
14
Determine skew w/ median and mean
Mean left to median = left skew, mean right to median = right skew, mean = median : symmetric
15
Range def and pros and cons
Difference between max and min values (it's a measure of spread). Easy to compute but sensitive to outliers
16
Sample variance measurement (S exposant 2)
(formule)
17
Population variance (sigma exposant 2)
(formule)
18
Why squared deviations (2)
The sum of Xi - X barre for all values of Xi is 0. Also, absolute values are not good for inference so we use squared deviations
19
To remember in squared deviations
Variance is measured in squared units
20
Sample standard deviation (S)
Formule . (Racine carrée rajoutée par dessus toute la formule du sample variance)
21
Units of S
Same units as data themselves
22
