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AP Stats > Summer Vocab > Flashcards

Summer Vocab Flashcards

1
Q

The study of variability

A

Statistics

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2
Q

Differences… how things differ. We all look different, act differently, have different preferences…

A

Variability

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3
Q

2 branches of AP Stats

A

Inferential and Descriptive

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4
Q

Describe data collected, pictures, or summaries like mean, median, and mode.

A

Descriptive Stats

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5
Q

Look at data and use that to say stuff about the big picture (little sample can tell a lot)

A

Inferential Stats

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6
Q

___ explains to you about the data you have while ___ uses that data you have to try to say something about an entire population

A

Descriptive vs. Inferential Stats

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7
Q

Any collected information. Generally each little measurement.
ex. ¨yes, yes, no, yes, yes¨ or ¨3, 1, 2, 1, 4, 3, 3, 4¨

A

Data

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8
Q

Group you’re interested in

ex. “all teenagers in the US” or “all AP students in my school

A

Population

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9
Q

A subset of a population, often to make inferences about the population. Used to calculate stats

A

Sample

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10
Q

___ are generally large

___ are smaller subsets of this ___

A

Population vs. Sample

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11
Q

___ is each little bit of info collected from the subjects, INDIVIDUAL, summarized (ex. finding the mean of group of data aka statistic)
If there’s data from each member of pop. called a ___

A

Data vs. Statistics vs. Parameters

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12
Q

A numerical summary of a population

ex. mean, median, range of a sample

A

Parameter

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13
Q

A numerical summary of a sample

ex. mean, median, range of a sample

A

Statistic

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14
Q

___ are individual measures. ex. meal preferences “taco, taco, pasta, burger, burger, taco”
___ and ___ are summaries. ex. stat “42% of sample preferred tacos” and parameter “42% of population preferred tacos”

A

Data vs. Statistic vs. Parameter (Categorical)

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15
Q

Individual measures, ex. how long a person can hold their breath “45s, 64s, 32s, 68s” = raw data
Summaries, ex. “ the average breath holding time in the sample was 52.4s” and “the average breath holding time in the pop. was 52.4s”

A

Data vs. Stat vs. Parameter (Quantitative)

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16
Q

Like a sample of the entire population, get info from every member of pop.

A

Census

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17
Q

A census is okay for small populations, ex. a classroom

bad for “all US teens”

A

Does a census make sense?

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18
Q

Both are single number summarizing a larger group of numbers.
ex. pppp parameters come from pppp pop. and ssss stats come from ssss stats

A

Parameter vs. Statistic

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19
Q

a value in a data set

ex. # of pickles on a burger

A

Datum (data value)

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20
Q

ex. average # of pickles on a bunch of burgers

A

Statistic (summary of a sample)

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21
Q

ex. true average # of pickles on a burger

A

Parameter (1 # summary of the population)

22
Q

In a ___, you get info from a small part of the population.
In a ___, you get info from the entire population
You can get a parameter from a census, but only a statistic from a sample

A

Sample vs. Census

23
Q

If you randomly choose people from a list, then their hair color, height, weight and any other data collected from them can be considered random variables.

A

What are random variables?

24
Q

___ are numerical measures, ex. height and IQ

___ are categories, ex. eye color and musical preference

A

Quantitative vs. Categorical Variables

25
Q

The data is the actual gathered measurements.

ex. eye color “blue, brown, brown, etc.” or “yes, no”
ex. weight “125, 155, 223, 178, etc.”

A

Quantitative vs. Categorical Data

26
Q

___ is a finite number of values between any two values. It’s always numeric.
ex. # of cars sold (24)

___ is an infinite number of values between any two values. It can be numeric or date/time.
ex. weight of a mouse (4.344 oz)

A

Discrete vs. Continuous Variables

27
Q

Numeric

ex. Height, age, SAT score

A

Quantitative Variable

28
Q

Categories

ex. Blonde, Listens to Hip Hop, Female, yes, no, etc.

A

Categorical Variable

29
Q

What do we sometimes call a categorical variable?

A

Qualitative

30
Q

The actual #’s gathered from each subject.

ex. 211 lbs, 67bpm

A

Quantitative Data

31
Q

The actual individual category from a subject

ex. blue, female, sophomore

A

Categorical Data

32
Q

Real randomly generated sample
Humans can’t really do this well without the help of a calculator, cards, dice, or slips of paper
ex. rolling dice, choosing names from a hat, etc.

A

Random Sample

33
Q

How often something comes up

A

Frequency

34
Q

___ is singular
ex. “hey dude, come see this datum I got from this rat!”

___ is the plural
ex. “hey look at all that data Edgar got from those chipmunks over there!!”

A

Data or Datum?

35
Q

A table, or a chart, that shows how often certain values or categories occur in a data set

A

Frequency Distribution

36
Q

Divide frequency by total

A

Relative Frequency

37
Q

Add up frequencies as you go.
Suppose you’re selling 25 pieces of candy. You sell 10 the first hour, 5 the second, 3 the third, and 7 in the last.
ex. 10, 15 18, 25

A

Cumulative Frequency

38
Q

It is the added up percentages.
Take cumulative frequencies and divide by total.
Always end at 100 percent.
ex. .40, .60, .64, .1.00.

A

Relative Cumulative Frequency

39
Q

___ are for categorical data (bars don’t touch)

___ are for quantitative data (bars touch)

A

Bar Chart vs. Histogram

40
Q

The old average we used to calculate. It is the balancing point of the histogram.

A

Mean

41
Q

___ mean is the mean of a population
ex. parameter

___ mean is the mean of a sample
ex. statistic

We can use sample stats to make inferences about population parameters.

A

Population Mean vs. Sample Mean

42
Q

Symbols used for pop. mean (μ) and sample mean (x̅)

A

Mu and x bar

43
Q

Mean is the balancing point of a histogram, median splits the area of the histogram in half.

A

How can you think about the mean and median to remember the difference when looking at a histogram?

44
Q

The middlest #, it splits the area of a histogram in half

A

Median

45
Q

The most common, or the peak of a histogram. We often mode with categorical data.

A

Mode

46
Q

With categorical variables. For instance, to describe the average teenagers preference, we often speak of what “most” students chose, which is the mode. It is also tells the number of bumps in a histogram for quantitative data (unimodal, bimodal, etc…).

A

When do we often use mode?

47
Q

It is not RESILIENT, it is impacted by skewness and outliers

A

Why don’t we always use the mean?

48
Q

It depends
mean= height
median= parental income,
mode= music preference

A

When we say “the average teenager” are we talking about mean, median or mode?

49
Q

Goes in that order from left to right. Mean-median-mode

A

How are mean, median, and mode positioned in a skewed left histogram?

50
Q

Goes in opposite order from right to left. Mode-median-mean

A

How are mean, median, and mode positioned in a skewed right histogram?

51
Q

The mean chases the tail

A

The mean chases the tail

AP Stats (1 decks)

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