Lecture 2 Flashcards Preview

Math 203 > Lecture 2 > Flashcards

Flashcards in Lecture 2 Deck (22)
Loading flashcards...
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

T of F : The standard deviation is the average absolute deviation from the mean

False but it doesn’t hurt much to think of it as the average distance of observations from the mean