MODULE 4 - VISUALIZING DATA Flashcards

1
Q

what is a contingency table?

A

tables of data frequencies or proportions within different levels of categorical variabl

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2
Q

what is frequency?

A

the number of sampling units that falls in each level

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3
Q

what are one-way contingency tables?

A

or data with a single categorical variable and are shown as a one-dimensional table of columns

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4
Q

what are two-way contingency tables?

A

for data with two categorical variables and are shown as a two-dimensional table of rows and columns

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5
Q

what are marginal distributions?

A

the row and column sums of a two-way contingency table

they can be shown as frequencies or proportions

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6
Q

how to calculate marginal distributions as frequencies

A

row: sum frequencies across all columns for each row

column: sum frequencies across all rows for each column

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7
Q

how to calculate marginal distributions as proportions

A

table total: sum all frequencies in the table

row: sum frequencies across all columns for each row and divide by table total

column: sum frequencies across all rows for each column and divide by table total

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8
Q

what are conditional distributions?

A

are two-way tables that show the proportion of sampling units for one variable within each level of the second variable

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9
Q

how to calculate conditional distributions

A
  1. Identify the primary versus secondary variable. This determines whether you use the row or column marginal distribution
  2. For each cell in the new table, divide the value from the contingency table by the marginal distribution of the primary variable.
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10
Q

advantages/disadvantages to a histogram

A

pros: provide a great way to visualize the pattern of relative abundance in your sampling units along the numerical variable

cons: that it is cumbersome to display histograms when your dataset also has multiple levels of a categorical variable

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11
Q

what is a bin?

A

a small range of the numerical variable

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12
Q

4 parts to a box plot

A
  1. box
  2. solid line
  3. whiskers
  4. extreme values
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