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Flashcards in Chapter 1 Deck (52)
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1

Cases

The objects described by a set of data.
Ex. Customers, companies, subjects in a study, stock

2

Label

Is a SPECIAL VARIABLE used in some data sets to distinguish the different cases

3

Variable

Is a characteristic of the case--> different cases can have different values for variables

4

Observation

Describes the data for a particular case

5

Categorical Variable

Places a case into one of several groups or categories
Ex. Bar Graphs, Pie Charts, and Pareto Charts

6

Quantitative Variable

Takes numerical values arithmetic operations, such as adding and averaging, makes sense

7

Statistical Software

In some statistical software spaces are not allowed in variable names--> instead use an underscore

8

Ordered Categorical Variable

Possible values for a grade...A, B, C, D..etc because A is better than B which is better then C and so on

9

Nominal Variable

A categorical variable that is not ordered

10

Instruments

Different areas of application (marketing) can also have their own special variables--> these variable are measured with instruments

11

Rate

Computing a rate is one of several ways of adjusting one variable to create another--> sometime more meaningful than count

12

Distribution

Describes how to values of a variable vary from case to case

13

Pareto Chart

Categories are ordered from MOST frequent-->least frequent-->most important categories for a categorical variable
Ex. frequently used in quality control settings

14

Histogram

The most common graph of the distribution of a quantitative variable wear we group near values into classes--> for small data sets a stemplot can be used

15

How can you describe the overall pattern of a histogram

You can describe the overall pattern of a histogram by its SHAPE, CENTER, and SPREAD

16

Outlier

The most important type of deviation--> an individual value that falls outside the overall pattern

17

When is a distribution symmetric?

If the right and left sides of the histogram are mirror images of each other

18

Skewed to the right

If the right side of the histogram extends much farther out than the left side..and vice versa

19

Positively skewed

Data that skews to the right--> positive skewness is the MOST common type of skewness that we see in real data

20

Time plot

Plots each observation against the time it was measured--> time on a horizontal and the variable you are measuring on a vertical scale

21

Mean

The most common measure of center is the ordinary arithmetic average--> NOT a resistant measure of center as it can be influenced by outliers

22

Median

The median is the midpoint of a distribution, the number such that half the observations are smaller and half are larger

23

Median Odd

(N+1)/2 observations up from the bottom of the list

24

Median Even

It is the mean of the two numbers in the middle

25

Median vs Mean

The median is more resistant than the mean

26

Median and Mean in a Symmetric Distibution

They are close together--> exactly symmetric exactly the same

27

Median and Mean in a skewed distribution

The mean is farther out on the long tail than the median

28

The five number summary

Boxplot-->consits of the smallest observation, the first quartile, the median, the thrid quartile, and the largest observation --> in order form largest to smallest

29

The five number summary vs. distribution

Not the most common numerical description of distribution

30

Most common numerical description of distribution

The mean to measure the center and the standard deviation to measure the spread