chapter 1: picturing distributions with graphs Flashcards

(34 cards)

1
Q

Individuals

A

Particular things

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2
Q

Population

A

Set of individuals

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3
Q

Variable

A

an attribute of an individual.

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4
Q

value (of a variable)

A

any way that that variable could be exhibited by an individual

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5
Q

True or False: every individual must have only a single value for any given variable

A

True

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6
Q

Data

A

numbers with a context, or, values of variables for the individuals in a population

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7
Q

Dataset

A

The particular data that we are presented with

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8
Q

observation

A

a member of our dataset

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9
Q

sample

A

That part of the population from which our observations come

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10
Q

The size of a dataset (or sample)

A

the number of observations in it

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11
Q

in order to clearly define a statistical problem, we must…

A

we must first clearly state the population and the variables that it concerns

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12
Q

exploratory data analysis

A

when you seek simply to describe datasets

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13
Q

distribution

A

a description of the values a variable takes and how

often it takes them

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14
Q

graph

A

a visual representation of a distribution

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15
Q

count

A

a category’s size

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16
Q

If we want to know the proportion of observations in a dataset that are in a given category…

A

we divide the count of that category by the sample size

17
Q

If we want to know the percentage of observations in a dataset that are in a given category…

A

we multiply the proportion of observations in the dataset that are in that category by 100%

18
Q

two ways we can look at the size of a given category

A

we might be interested in how it compares to either the sizes of the other categories, or in how it compares to the size of the population

19
Q

To compare the sizes of the categories with each other

A

we use a bar graph

20
Q

One drawback of bar graphs is

A

it is difficult to look at proportions of observations using them

21
Q

To compare the sizes of the categories with the size of the dataset

A

we calculate the percentage of the dataset that fall into each category

22
Q

to compare the percentage of any given category not only to each other, but… to the dataset as a whole as well

A

we use a pie chart

23
Q

roundoff error

A

when the error in percentage totals reflect the accumulated errors in rounding

24
Q

when dealing with quantitative data, we must have [blank]

A

a unit of measurement

25
If we allow the value of a variable for a single individual to change, we must also allow [blank] for it to do so
time, on a time plot
26
the long-term upward or downward movement | over time on a time plot
trend
27
The amount of change over time in a time plot can be deceiving, so make sure that you [blank]
check the scale of the variable and where its values begin on the 𝑦-axis
28
For small data sets, if we want to compare the values that a quantitative variable takes among different individuals in a population, we can use a [blank]
stem plot (numbers in a column ("stem") at left, other numbers stretching in rows to the right. you lop off the latter number on the stem and the former on the leaf
29
In a stem plot, we reduce the digits in each observation to only the first digit, which we call a...
leaf (get it?). Ex. a score of 107 is represented by the 7 to the right of the 10 in the stem
30
True or false: we NEVER include commas or decimal points in a stem plot
True
31
For larger quantitative datasets, ranges into which the values of the data fall are called [blank]
bins
32
When you make quantitative data categorical in bins, you can then make a bar graph called a [blank]
histogram
33
we define statistics as...
the science of learning from data
34
right-skewed or left-skewed
when data is further away from the center on one side than the other