Intro To Statistics Vocabulary Chapter 4-6

Front 1

Distribution

Back 1

Distribution of a quantitative variable slices up all the possible values of the variable equal- width bins and gives the number of values (or counts) falling into each bin

Front 2

Histogram

Back 2

A histogram uses adjacent bars to show the distribution of a quantitative variable. Each represents the frequency (or relative frequency) of values falling in each bin

Front 3

Gaps

Back 3

a region where there is no value in histogram

Front 4

Relative Frequency Histogram

Back 4

replacing the count on the vertical axis with the percentage of the total number of cases falling in each bin

Front 5

Stem-and-Leaf Display

Back 5

show quantitative data values in a ways that sketches the distribution of the data

Front 6

Dot Plot

Back 6

a dot for each case against a single axis

Front 7

Describe a Distribution

Back 7

1. Shape
2. Center
3. Spread

Front 8

Shape

Back 8

Look for:
1. Single v. Multiple Modes
2.Symmetry vs.Skewness
3. Outliers and gaps

Front 9

Center

Back 9

The place in the distribution of a variable that yo's point to if you wanted to attempt the impossible by summarizing the entire distribution with a single number. Measures of center include the mean and median.

Front 10

Spread

Back 10

A numerical summary of how tightly the values are clustered around the center. Measures of spread include the IQR and standard deviation.

Front 11

Mode

Back 11

A hump or local high point in the shape of the distribution of a variable. The apparent location of modes can change as the scale of a histogram is changed.

Front 12

(Uni, Bi,or Multi)modal

Back 12

Uni- one mode
Bi- two moders
Multi- more that three

Front 13

Uniform

Back 13

a distribution that's roughly flat, no humps

Front 14

Symmetric

Back 14

A distribution is symmetric to the two halves on either sides of the center look approximately like mirror images of each other.

Front 15

Tails

Back 15

parts that typicallly trail off on either side

Front 16

Skewed

Back 16

if not symmetric and one tail stretches out farther than the other

Front 17

Outliers

Back 17

Extreme values that don't appear to belong to rest of the data

Front 18

Midrange

Back 18

Average; not for histogram too sensitive with outliers

Front 19

Median

Back 19

(n+1)/2, if number comes out odd average the two places

Front 20

Range

Back 20

Max.- Min.

Front 21

Quartile

Back 21

The mean and quartiles divide data into four parts with equal number of data value.

Front 22

Interquartile Range

Back 22

upper quartile- lower quartie

Front 23

Percentiles

Back 23

the ith percentile is the number that falls above i% data.

lower quartile- 25%
Upper quartile- 75%

Front 24

5-Number Summary

Back 24

summary of a distribution reports the, Min,Q1, Med, Q3, and Max.

Front 25

Mean

Back 25

found by adding all the data values and dividing by the count
(formula)
usually paired by standard deviation

Front 26

Resistance

Back 26

a calculated summary is said to be resistant if it is affected only by a limited amount of outliers.

Front 27

Variance

Back 27

the sum of squared deviation from the mean, divided by the count minus 1.

Front 28

Standard Deviation

Back 28

square roots of the variance

Front 29

Quartile

Back 29

The mean and quartiles divide data into four parts with equal number of data value.

Front 30

Interquartile Range

Back 30

upper quartile- lower quartie

Front 31

Percentiles

Back 31

the ith percentile is the number that falls above i% data.

lower quartile- 25%
Upper quartile- 75%

Front 32

5-Number Summary

Back 32

summary of a distribution reports the, Min,Q1, Med, Q3, and Max.

Front 33

Mean

Back 33

found by adding all the data values and dividing by the count
(formula)
usually paired by standard deviation

Front 34

Resistance

Back 34

a calculated summary is said to be resistant if it is affected only by a limited amount of outliers.

Front 35

Variance

Back 35

the sum of squared deviation from the mean, divided by the count minus 1.

Front 36

Standard Deviation

Back 36

square roots of the variance

Front 37

Comparing Distribution

Back 37

Consider their:
1. Shaper
2. Center
3. Spread

Front 38

Comparing Box Plots

Back 38

Compare:
1. shapes
2. medians
3. IQRs
4. Outliers

Front 39

Timeplot

Back 39

displays data that change over time

Front 40

Standardizing

Back 40

We standardize to eliminate units. Standardized values can be compared and combined even if the original variables had different unit and magnitude.

Front 41

Standardized Value

Back 41

(n-mean)/sd

Front 42

Z-score

Back 42

tells hoes many standard deviation a value is from the mean; z-scores have a mean of 0 and standard deviation of 1

(look at formulas)

Front 43

Shifts

Back 43

adding or subtracting a constant to each value, all measures of position will increase by the same constant.

Leaves the spread unchanged

Front 44

Rescale

Back 44

multiply or divide all the data values by a constant: the five points ares multiplied or divided by that constants

Front 45

Standard deviation to:
Shapes
Center
Spread

Back 45

Standardizing into z-scores does:

1. not change the distribution of a variable

2. changes the center by making the mean 0

3, changes the spread by making the standard deviation 1

Front 46

Standard Normal Mode

Back 46

Mean=0
Deviation=1
Standard Normal Distribution

Front 47

Normal Models

Back 47

"bell-shaped curves"
appropriate for distributions whose shapes are unimodal and roughly symmetric

Front 48

Parameters

Back 48

not numerical summaries of data
part of the model

Number we use to specify the model

N(mean,sd)

Front 49

Nearly Normal Condition

Back 49

A distribution is nealy Normal id it is unimodal and symmetric. WE can check by looking at a histogram or a normal probability plot.

Front 50

68-95-99.7 Rule

Back 50

In normal models:

68% fall within 1 standard deviation of the mean

95% fall within 2 standard deviation of the mean

99.7% fall within 3 standard deviation of the mean