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statistics > summer vocab > Flashcards

summer vocab Flashcards

1
Q

what is statistics

A

the study of variability

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2
Q

what is variability

A

differences, how things differ. there is variability everywhere. we all look different, act different, have different preferences. statistics looks at these differences.

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3
Q

what are 2 branches of AP stats

A

inferential and descriptive

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4
Q

what are descriptive stats

A

tel me what you got, describe to me the data you collected, use pictures or summaries like mean, median, range ect.

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5
Q

what are inferential stats

A

look at the data and use that to say stuff about the big picture

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6
Q

compare descriptive and inferential stats

A

descriptive explains about the data that you have, inferential uses data you have to try to say something about an entire population

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7
Q

what is data

A

any collected information. generally each little measurement

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8
Q

what is a population

A

the group your interested in. sometimes its big like “all the teenagers in the us” other times its small like “all AP students in my school”.

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9
Q

what is a sample

A

a subset of a population, often taken to make inferences about a population. e calculate statistics from samples.

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10
Q

compare population to sample

A

populations a generally larger and samples are small subsets of these populations. we take samples to make inferences about populations. we use statistics to estimate parameters.

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11
Q

compare data to statistics

A

data is each little bit of information collected from subjects. they are individual little things we collect, we summarize by, for example, finding the mean of the group of data. if it is a sample then we call that mean a “statistic” if we have data from each member of a population then that mean is called a parameter.

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12
Q

compare data to parameter

A

data is each little bit of information collected from the subjects. they are individual 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 each member of a population then that mean is called a parameter.

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13
Q

what is a parameter

A

a numerical summary of a population. like a mean, median, or range of a population

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14
Q

what is a statistic

A

a numerical summary of a sample. like mean, median or 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 you 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
  1. the parameter is the true average wait time at the dunkin donuts. this is a number you do not have and never will know.
  2. the statistic is “3.2 minutes”. it is the average of the data you collected.
  3. the parameter of interest is the same thing as the population parameter, so in this case it would be the true average wait time.
  4. the data is the wait time of each individual car
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16
Q

compare data-statistic-parameter using a categorical example

A

data are individual measures like meal preference “taco, taco, pasta, taco, 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 a quantitative example

A

data are individual measures like how long someone can hold their breath “45 sec, 64 sec, 32 sec, 68 sec”. statistics and parameters are summaries like “the average breath holding time in the sample was 52.4” and a parameter would be “the average breath holding time for the population was 52,4”

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18
Q

what is a census

A

like a sample of the entire population you get information from every member of the population

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19
Q

does a census make sense

A

a census is okay for a small population like “statistic students” but impossible if you want to survey “all US teens”

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20
Q

what is the difference between a statistic and a parameter

A

both are a single number summarizing a larger group of numbers but Parameters come from Populations and Statistics come from Samples

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21
Q

if i take a random sample of 20 hamburgers from 5 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 of 20 hamburgers from 5 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

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23
Q

if i take a random sample of 20 hamburgers from 5 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 or parameter of interest

24
Q

what is the difference of 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 the census, but only a statistic from a sample.

25
Q

use the following words n one sentence: population, parameter, census, samples, data, statistics, inference, population of interest

A

i was curious about a population parameter, but a census was to 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

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
  1. data
  2. sample
  3. statistic
  4. make an inference
  5. parameter
27
Q

what are random varibles

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 varibles

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 be “blue, brown, brown, brown, blue, brown, ect.” the data from categorical variables are often words like “yes, no, yes, yes, ect.”. for data such as weight the data would be quantitative like “125, 155, 223, ect.”. quantitative data are numbers.

30
Q

what is the difference between discrete and continuous variables

A

discrete can be counted like “number of cars sold”, they are generally intervals (you cant sell 9.3 cars) , while continuous would be something like “the weight of a mouse” (4.344 oz.)

31
Q

what is a quantitative variable

A

quantitative variables are numerical: height, age, number of cars sold, SAT scores

32
Q

what is a categorical variable

A

categorical variables are like categories: blonde, listens to hip hop, female, yes

33
Q

what do we sometimes call a categorical variable

A

qualitative

34
Q

what is quantitative data

A

the actual numbers gathered from each subject, 221 pounds, 67 beats per minute, ect.

35
Q

what is categorical data

A

the actual individual category from a subject like blue, female, ect.

36
Q

what is a random sample

A

when you use a sample by rolling a dice, choosing a name from a hat, or other real randomly generated sample.

37
Q

what is a frequency distribution

A

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

38
Q

data vs datum

A

datum is singular “look at this datum i got from this rat” while data is plural “look at this data we collected from these rats”

39
Q

what is frequency

A

how often something comes up

40
Q

what is meant by relative frequency

A

the percent of time that something comes up (frequency/total)

41
Q

how do you find relative frequency

A

frequency divided by the total

42
Q

what is meant by relative cumulative frequency

A

add up the frequencies as you go. suppose you are selling candy, 25 pieces sold overall, with 10 sold in the first hour, 5 the second, 3 the third, and 7 the fourth hour. we would take the frequencies, 10, 15, 18, 25 and divide them by the total (25) giving cumulative percentages, .40, .60, .64, 1

43
Q

what is cumulative frequency

A

add up the frequencies as you go. suppose you are selling candy, 25 pieces sold overall, with 10 sold in the first hour, 5 the second, 3 the third, and 7 the fourth hour. the cumulative frequencies would be: 10, 15, 18, 25

44
Q

what is the difference between a bar chart and a histogram

A

bar charts are for categorical data (bars do not touch) and histograms are for quantitative data (bars touch)

45
Q

what is the mean

A

the old average we used to calculate. the balancing point of a histogram

46
Q

what is the difference between a population mean and a sample mean

A

population mean is the mean of a population, its a parameter, sample mean is a mean of a sample, so its a statistic.

47
Q

what symbols do we use for population mean and sample mean

A

mu ( μ ) is used for population (parameter), x-bar ( x̄ ) is used for sample (statistic)

48
Q

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

A

mean is the balancing point of the histogram while median is the area of the histogram in half

49
Q

what is the median

A

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

50
Q

what is the mode

A

the most common occurring piece of data or the peaks of a histogram. we often use the mode with categorical data

51
Q

when do we often use the mode

A

with categorical variables. for instance to describe the average teenagers preference, we often speak of what “most” students choose, which is the mode. it also tells the number of bumps in a histogram for quantitative data (uni-modal, bi-modal, ect.)

52
Q

why do not we always use the mean

A

it is no resilient, it gets impacted by skewness and outliers

53
Q

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

A

it depends, it it is about height it might be mean, if it is based on parental income it could be median and if it is based on musical preference you would use the mode

54
Q

what is a clear example of where the mean would change but median would not

A

if you were to ask eight people how much money they had in their wallet and you found they had {1, 2, 2, 5, 5, 8, 8, 9}. the mean of the set of data is 5 and the median is also 5. you might say that “the average person in this group had 5 dollars”. if the numbers were a little different and instead it was {1, 2, 2, 5, 5, 8, 8, 9000} in this case the median is still 5 but the mean goes to over 1000. 5 dollars is a better description of the average person in the group and 9000 is simply an outlier. this would show resilience

55
Q

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

A

goes in the order from left to right mean-median-mode

56
Q

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

A

goes in the opposite order mode-median-mean

57
Q

who chases the tail

A

the mean and outliers chase the tail

statistics (1 decks)

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