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AP Statistics > AP stats summer > Flashcards

AP stats summer Flashcards

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1
Q

Statistics

A

the study of variability

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2
Q

Variability

A

how things differ

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3
Q

2 branches of ap stats

A

inferential and descriptive

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4
Q

descriptive stats

A

tell the data you collected using mean, median, mode, range

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5
Q

inferential stats

A

look at the data and use it for the big picture

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6
Q

data

A

any collected info

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7
Q

population

A

the group you are interested in

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8
Q

sample

A

A subset of a population, often taken to make inferences

about a population.

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9
Q

Compare population to sample.

A

Populations are generally large and samples are small
subsets of a population. We take samples to make
inferences about populations. We use statistics to estimate
parameters

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10
Q

Compare data to statistics

A

Data is each little bit of information collected from the
subjects…They are the INDIVIDUAL little things we collect…
we summarize them by, for example, finding the mean of a
group of data. If it is a sample, then we call that mean a
statistic. If we have data from every member of a
population, then that mean is called a “parameter”

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11
Q

Compare descriptive to inferential

STATS.

A

Descriptive explains to you about the data that you have,
inference uses the data you have to try to say something
about an entire population.

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12
Q

Compare data to parameters

A

Data is each little bit of information collected from the
subjects…They are the INDIVIDUAL little things we collect…
we summarize them by, for example, finding the mean of a
group of data. If it is a sample, then we call that mean a
statistic. If we have data from every member of a
population, then that mean is called a “parameter”

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13
Q

parameter

A

A numerical summary of a population. Like a mean,

median, range,…of a population

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14
Q

statistic

A

A numerical summary of a sample. Like a mean, median,

range,…of a sample

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15
Q 
We are curious about the average wait
time at a Dunkin Donuts drive through in
your neighborhood. You randomly sample
cars one afternoon and find the average
wait time is 3.2 minutes. What is the
population parameter? What is the statistic?
What is the parameter of interest? What is
the data?
A

The parameter is the true average wait time at that Dunkin
Donuts. This is a number you don’t have and will never
know. The statistic is “3.2 minutes.” It is the average of
the data you collected. The parameter of interest is the
same thing as the population parameter. In this case, it is
the true average wait time of each individual car, so that
would be like “3.8min, 2.2min, 0.8min, 3min”. You take that
data and find the average. That average is called a
“statistic” and you use that to make an inference about the
true parameter.

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16
Q

Compare DATA-STATISTIC-PARAMETER using Categorical Data

A

Data are individual measures…like meal preference “taco,
taco, pasta, burger, burger, taco”…Statistics and parameters
are summaries. A statistic would be “42% of the sample
preferred tacos.” A parameter would be “42% of the
population preferred tacos.”

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17
Q

Compare DATA-STATISTIC-PARAMETER using Quantitative Data

A

Data are individual measures, like how long a person can
hold their breath: “45 sec, 64 sec, 32 sec, 68 sec.” That is
the raw data. Statistics and parameters are summaries like
“the average breath holding time in the sample was 52.4
seconds” and a parameter would be “the average breath
holding time in the population was 52.4 seconds”

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18
Q

census

A

Like a sample of the entire population, you get info from

every member of the population.

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19
Q

does a census make sense

A

A census is ok for small populations (like Mr. Nystrom’s
students) but impossible if you want to survey “all US
teens”

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20
Q

What is the difference between a

parameter and a statistic?

A

BOTH ARE A SINGLE NUMBER SUMMARIZING A
LARGER GROUP OF NUMBERS… But pppp parameters
come from pppp populations… sss statistics come from ssss
samples.

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21
Q 
If I take a random sample of 20
hamburgers from FIVE GUYS and count
the number of pickles on a bunch of them
and one of them had 9 pickles, then the
number 9 from that burger would be called
\_\_\_\_\_\_\_\_\_\_\_\_\_\_
A

a datum, or a data value.

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22
Q 
If I take a random sample 20
hamburgers from FIVE GUYS and count
the number of pickles on a bunch of them
and the average number of pickles was
9.5, then 9.5 is considered a \_\_\_\_\_\_\_\_\_\_
A

statistic. (it is a summary of a sample.)

23
Q 
If I take a random sample of 20
hamburgers from FIVE GUYS and count
the number of pickles on a bunch of them
and I do this because I want to know the
true average number of pickles on a burger
at FIVE GUYS, the true average number of
pickles is considered a \_\_\_\_\_\_\_\_\_\_\_\_
A

parameter, a one number summary of the population. The

truth. AKA the parameter of interest.

24
Q

What is the difference between a

sample and a census?

A

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

25
Q

Use the following words in one
sentence: population, parameter, census,
sample, data, statistics, inference,
population of interest.

A

I was curious about a population parameter, but a census
was too costly so I decided to choose a sample, collect
some data, calculate a statistic and use that statistic to
make an inference about the population parameter (aka the
parameter of interest).

26
Q

If you are tasting soup.. Then the flavor
of each individual thing in the spoon is the
________, the entire spoon is a ______..
The flavor of all of that stuff together is like
the _____ and you use that to __________
about the flavor of the entire pot of soup,
which would be the__________.

A

If you are tasting soup. Then the flavor of each individual
thing in the spoon is DATA, the entire spoon is a
SAMPLE. The flavor of all of that stuff together is like the
STATISTIC, and you use that to MAKE AN INFERENCE
about the flavor of the entire pot of soup, which would be
the PARAMETER. Notice you are interested in the
parameter to begin with… that is why you took a sample.

27
Q

random variables

A

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.

28
Q

What is the difference between

quantitative and categorical variables?

A

Quantitative variables are numerical measures, like height
and IQ. Categorical are categories, like eye color and
music preference

29
Q

What is the difference between

quantitative and categorical data?

A

The data is the actual gathered measurements. So, if it is
eye color, then the data would look like this “blue, brown,
brown, brown, blue, green, blue, brown etc.” The data from
categorical variables are usually words, often it is simply
“YES, YES, YES, NO, YES, NO” If it was weight, then the
data would be quantitative like “125, 155, 223, 178, 222,
etc..” The data from quantitative variables are numbers.

30
Q
  1. What is the difference between discrete

and continuous variables?

A

Discrete can be counted, like “number of cars sold” they
are generally integers (you wouldn’t sell 9.3 cars), while
continuous would be something like weight of a mouse?
4.344 oz

31
Q

quantitative variable

A

Quantitative variables are numeric like: Height, age, number

of cars sold, SAT score

32
Q

categorical variable

A

categorical variables are like categories: Blonde, Listens to Hip Hop, Female, yes, no,… etc.

33
Q

what can a categorical variable be called

A

qualitative

34
Q

quantitative data

A

The actual numbers gathered from each subject. 211

pounds. 67 beats per minute.

35
Q

categorical data

A

The actual individual category from a subject, like “blue” or
“female” or “sophomore”

36
Q

random sample

A

When you choose a sample by rolling dice, choosing
names from a hat, or other REAL RANDOMLY generated
sample. Humans can’t really do this well without the help
of a calculator, cards, dice, or slips of paper.

37
Q

frequency

A

how often something comes up

38
Q

data or datum

A

datum is singular.. Like “hey dude, come see this datum I
got from this rat!” data is the plural.. “hey look at all that
data Edgar got from those chipmunks over there!!”

39
Q

frequency distribution

A

A table or chart that shows how often certain values or

categories occur in a data set.

40
Q

relative frequency

A

The PERCENT of time something comes up

frequency/total

41
Q

How do you find relative frequency?

A

divide the frequency by the total

42
Q

cumulative frequency

A

ADD up the frequencies as you go. Suppose you are
selling 25 pieces of candy. You sell 10 the first hour, 5
the second, 3 the third and 7 in the last hour, the
cumulative frequency would be 10, 15, 18, 25

43
Q

relative cumulative frequency

A

It is the ADDED up PERCENTAGES.. An example is
selling candy, 25 pieces sold overall…, with 10 the first
hour, 5 the second, 3 the third, and 7 the fourth hour,
we’d take the cumulative frequencies, 10, 15, 18 and 25
and divide by the total giving cumulative percentages… .40,
.60, .64, and 1.00. Relative cumulative frequencies always
end at 100 percent.

44
Q

What is the difference between a bar

chart and a histogram

A

bar charts are for categorical data (bars don’t touch) and

histograms are for quantitative data (bars touch)

45
Q

mean

A

average
It is the balancing
point of the histogram

46
Q

What is the difference between a

population mean and a sample mean?

A

population mean is the mean of a population, it is a
parameter, sample mean is a mean of a sample, so it is a
statistic. We use sample statistics to make inferences
about population parameters.

47
Q

What symbols do we use for population

mean and sample mean?

A

Population mean = μ
Sample mean = 𝑥̅
Mu for population mean, xbar for sample mean.

48
Q

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

A

mean is balancing point of histogram, median splits the

area of the histogram in half.

49
Q

median

A

the middlest number, it splits area in half (always in the

POSITION (n+1)/2 )

50
Q

mode

A

the most common, or the peaks of a histogram. We often

use mode with categorical data

51
Q

when do you use mode

A

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…)

52
Q

Why don’t we always use the mean,

we’ve been calculating it all of our life ?

A

It is not RESILIENT, it is impacted by skewness and

outliers

53
Q

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

A

It depends, if we are talking height, it might be the mean,
if we are talking about parental income, we’d probably use
the median, if we were talking about music preference,
we’d probably use the mode to talk about the average
teenager.

AP Statistics (1 decks)

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