Summer Vocab

Question 1

Statistics

Answer 1

The study of variability.

Question 2

Variability

Answer 2

Differences. How things differ.

Question 3

Two branches of statistics

Answer 3

Inferential and descriptive

Question 4

Descriptive stats

Answer 4

Description of the data collected.

Question 5

Inferential Stats

Answer 5

Inferences made about the data collected. What conclusions can be made about the entire population based on the data? A small sample can tell a lot about the larger population.

Question 6

Descriptive v. Inferential Stats

Answer 6

Descriptive you are describing the data set, what we know about it. Inferential we are making conclusions about the population based on the data set.

Question 7

Data

Answer 7

Any collected information.

Question 8

Population

Answer 8

The group you’re interested in.

Question 9

Sample

Answer 9

A subset of a population. Taken to make inferences about the population. We take statistics from samples.

Question 10

Data v. Stats

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 each member of population, then that mean is called a “parameter”

Question 11

Parameter

Answer 11

A numerical summary of a population.

Question 12

Statistic

Answer 12

A numerical summary of a sample.

Question 13

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 13

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 all cars. The data is the wait time of each individual car, so that would be like “3.8 min, 2.2 min, .8 min, 3 min”. 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 14

Census

Answer 14

Sample of an entire population, you get information from every member of the population. Makes sense for smaller populations.

Question 15

Random Variables

Answer 15

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 16

Quantitative variables

Answer 16

Numerical measures like IQ and height.

Question 17

Categorical variables

Answer 17

Categories like eye color and music preference.

Question 18

Discrete Variables

Answer 18

Can be counted like “numbers of cars sold” they are generally integers.

Question 19

Continuous Variable

Answer 19

The weight of a mouse (4.344 oz.)

Question 20

Random Sample

Answer 20

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 21

Frequency

Answer 21

How often something comes up.

Question 22

A Frequency Distribution

Answer 22

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

Question 23

Relative Frequency

Answer 23

The PERCENT of time something comes up.

Question 24

How do you find relative frequency?

Answer 24

Divide the frequency by the total

Question 25

Cumulative frequency

Answer 25

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 26

Relative cumulative frequency

Answer 26

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 27

Bar charts v histograms

Answer 27

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

Question 28

Mean

Answer 28

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

Question 29

Population mean v. sample mean

Answer 29

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 30

Symbols for pop and sample mean

Answer 30

Mu for population mean, xbar for sample mean.

Question 31

Histogram: mean and median

Answer 31

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

Question 32

median

Answer 32

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

Question 33

mode

Answer 33

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

Question 34

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

Answer 34

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