Lecture 2

Question 1

4 aspects needed to correctly describe a single quantitative variable

Answer 1

Centre, spread (different ways of measuring spread), Skew (+,-,symmetric), weird things (outliers, multiple modes)

Question 2

Centre

Answer 2

Where most data located

Question 3

Spread (2)

Answer 3

Over what range we see most of the data/how much alike/different the observations are

Question 4

Skew

Answer 4

What direction does the spread extend to

Question 5

Weird things

Answer 5

Some points really far away (outliers) or 2 centres

Question 6

Dotplot advantages and disadvantages

Answer 6

Get to see all data points, Easy to interpret but gets messy quickly if there are a lot of data

Question 7

Histogram advantages and disadvantages

Answer 7

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

Question 8

Mode

Answer 8

Number that occurs most often

Question 9

We say that the median is _____________ to outliers

Answer 9

robust (an outlier won’t change it)

Question 10

Mean advantages and disadvantages

Answer 10

Good for estimating population means and good inferential properties but affected by outliers and skewed data

Question 11

Median advantages and disadvantages

Answer 11

Easy to interpret, Not influenced by outliers but bad inferential properties and longer to calculate

Question 12

Mode advantages and disadvantages

Answer 12

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)

Question 13

Population and sample mean notation

Answer 13

Population mean : µ

Sample mean : x barre ou X barre

Question 14

Determine skew w/ median and mean

Answer 14

Mean left to median = left skew, mean right to median = right skew, mean = median : symmetric

Question 15

Range def and pros and cons

Answer 15

Difference between max and min values (it’s a measure of spread). Easy to compute but sensitive to outliers

Question 16

Sample variance measurement (S exposant 2)

Answer 16

(formule)

Question 17

Population variance (sigma exposant 2)

Answer 17

(formule)

Question 18

Why squared deviations (2)

Answer 18

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

Question 19

To remember in squared deviations

Answer 19

Variance is measured in squared units

Question 20

Sample standard deviation (S)

Answer 20

Formule . (Racine carrée rajoutée par dessus toute la formule du sample variance)

Question 21

Units of S

Answer 21

Same units as data themselves

Question 22

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

Answer 22

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