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Flashcards in Bivariate Data Deck (19)
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1
Q

what is another term for r?

A

correlation coefficient

2
Q

what is another term for r^2?

A

coefficient of determination

3
Q

how would you prove reliable data?

A

interpolated data
r value
r^2 value
amount of data

4
Q

r^2 comment

A

(r^2 percentage) of the variation in y can be associated with the variation in x

5
Q

r comment

A

There is a strong/moderate/weak positive/negative correlation between the variables. There is strong/some/little evidence to show that the greater the level of x the greater the level of y

6
Q

which axis is the dependent variable

A

y

7
Q

which axis is the independent variable

A

x

8
Q

how do you find percentage from a two way table?

A

number/total x 100 = ans

9
Q

what is the residual?

A

distance from the trend (predicted) line

10
Q

how do you find the residual?

A

subtracting predicted y value from actual y value

11
Q

when can’t you eliminate a point? (outlier)

A

if there isn’t enough data
it is checked and is real data
it is the end point of the data

12
Q

how do you find a percentage from a two way table?

A

amount/total x 100 = %

13
Q

comment for a percentage graph from two way table

A

x have a slight/major preference for the blank, whereas y prefer blank (include percentages)

14
Q

what are the types of time series data (explain)

A

seasonal - pattern of data repeats over a regular time period
cyclic - the same pattern repeats but not over a regular time period
random - there is no real pattern

15
Q

how would you deseasonalise data

A

find average for each cycle

divide actual data by cycle averages

16
Q

what is the equation for deseasonalised data

A

actual data/seasonal index = deseasonalised data
or
a/i = d

17
Q

deseasonalised data comment

A

on (day) sales were higher/lower than expected according to deseasonalised data

18
Q

how would you forecast actual data

A

find trend line (of deseasonalised data)
sub number into x
use ans as d in a=di
*this number is never reliable as it is extrapolated data

19
Q

how would you smooth data (3 point average)

A

(data+data+data)/3

a point is lost either end