AP stats summer

Question 1

Statistics

Answer 1

the study of variability

Question 2

Variability

Answer 2

how things differ

Question 3

2 branches of ap stats

Answer 3

inferential and descriptive

Question 4

descriptive stats

Answer 4

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

Question 5

inferential stats

Answer 5

look at the data and use it for the big picture

Question 6

data

Answer 6

any collected info

Question 7

population

Answer 7

the group you are interested in

Question 8

sample

Answer 8

A subset of a population, often taken to make inferences

about a population.

Question 9

Compare population to sample.

Answer 9

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

Question 10

Compare data to statistics

Answer 10

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”

Question 11

Compare descriptive to inferential

STATS.

Answer 11

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.

Question 12

Compare data to parameters

Answer 12

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”

Question 13

parameter

Answer 13

A numerical summary of a population. Like a mean,

median, range,…of a population

Question 14

statistic

Answer 14

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

range,…of a sample

Question 15

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?

Answer 15

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.

Question 16

Compare DATA-STATISTIC-PARAMETER using Categorical Data

Answer 16

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.”

Question 17

Compare DATA-STATISTIC-PARAMETER using Quantitative Data

Answer 17

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”

Question 18

census

Answer 18

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

every member of the population.

Question 19

does a census make sense

Answer 19

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

Question 20

What is the difference between a

parameter and a statistic?

Answer 20

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

Question 21

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
\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Answer 21

a datum, or a data value.

Question 22

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 \_\_\_\_\_\_\_\_\_\_

Answer 22

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

Question 23

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 \_\_\_\_\_\_\_\_\_\_\_\_

Answer 23

parameter, a one number summary of the population. The

truth. AKA the parameter of interest.

Question 24

What is the difference between a

sample and a census?

Answer 24

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.

Question 25

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

Answer 25

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).

Question 26

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__________.

Answer 26

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.

Question 27

random variables

Answer 27

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.

Question 28

What is the difference between | quantitative and categorical variables?

Answer 28

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

Question 29

What is the difference between | quantitative and categorical data?

Answer 29

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.

Question 30

0. What is the difference between discrete | and continuous variables?

Answer 30

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

Question 31

quantitative variable

Answer 31

Quantitative variables are numeric like: Height, age, number | of cars sold, SAT score

Question 32

categorical variable

Answer 32

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

Question 33

what can a categorical variable be called

Answer 33

qualitative

Question 34

quantitative data

Answer 34

The actual numbers gathered from each subject. 211 | pounds. 67 beats per minute.

Question 35

categorical data

Answer 35

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

Question 36

random sample

Answer 36

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.

Question 37

frequency

Answer 37

how often something comes up

Question 38

data or datum

Answer 38

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!!"

Question 39

frequency distribution

Answer 39

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

Question 40

relative frequency

Answer 40

The PERCENT of time something comes up | frequency/total

Question 41

How do you find relative frequency?

Answer 41

divide the frequency by the total

Question 42

cumulative frequency

Answer 42

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

Question 43

relative cumulative frequency

Answer 43

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.

Question 44

What is the difference between a bar | chart and a histogram

Answer 44

bar charts are for categorical data (bars don't touch) and | histograms are for quantitative data (bars touch)

Question 45

mean

Answer 45

average It is the balancing point of the histogram

Question 46

What is the difference between a | population mean and a sample mean?

Answer 46

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.

Question 47

What symbols do we use for population | mean and sample mean?

Answer 47

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

Question 48

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

Answer 48

mean is balancing point of histogram, median splits the | area of the histogram in half.

Question 49

median

Answer 49

the middlest number, it splits area in half (always in the | POSITION (n+1)/2 )

Question 50

mode

Answer 50

the most common, or the peaks of a histogram. We often | use mode with categorical data

Question 51

when do you use mode

Answer 51

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…)

Question 52

Why don't we always use the mean, | we've been calculating it all of our life ?

Answer 52

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

Question 53

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

Answer 53

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.