Chapter 4 Displaying & Summarizing Quantitative Data

Chapter 4 Displaying & Summarizing Quantitative Data Chapter 4 Displaying & Summarizing Quantitative Data - Start

2018-03-09 26K 26 0 0

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Histograms. Similar to bar charts, but with quantitative data.. No gaps between bars.. Summarizes data visually using frequency count.. Data: Amount spent by 50 customers at a grocery store. 2.32 6.61 6.90 8.04 9.45 10.26 11.34 . ID: 644193 Download Presentation

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Chapter 4 Displaying & Summarizing Quantitative Data




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Presentations text content in Chapter 4 Displaying & Summarizing Quantitative Data

Slide1

Chapter 4

Displaying & Summarizing Quantitative Data

Slide2

Histograms

Similar to bar charts, but with quantitative data.

No gaps between bars.

Summarizes data visually using frequency count.

Slide3

Data: Amount spent by 50 customers at a grocery store

2.32 6.61 6.90 8.04 9.45 10.26 11.34

11.63 12.66 12.95 13.67 13.72 14.35 14.52

14.55 15.01 15.33 16.55 17.15 18.22 18.30

18.71 19.54 19.55 20.58 20.89 20.91 21.1323.85 26.04 27.07 28.76 29.15 30.54 31.9932.82 33.26 33.80 34.76 36.22 37.52 39.2840.80 43.97 45.58 52.36 61.57 63.85 64.3069.49

Source:

http://lib.stat.cmu.edu/DASL/Datafiles/Shoppers.html

Slide4

Histogram: Grocery Data

Slide5

Histogram: Heights of Adolescents

Source:

http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights

Slide6

Histogram: Smiling Times of 8-week old baby

Data Source

:

http://cnx.org/content/m16819/latest/

Slide7

Stem-and-Leaf Display

Quick way to summarize a small set of quantitative data.

99, 53, 93 , 82 , 85 , 64 , 75 , 62 , 74 , 81 , 73 , 70 , 81 , 73 , 94, 67 , 93 , 87 , 85 , 36 , 80 , 78

Slide8

Shape of a Distribution

Unimodal

One peak value that occurs more frequently than the rest

Bimodal

Two peak values that occur more frequently than the rest

MultimodalThree or more peak valuesUniformBars in histogram are all about the same height

Slide9

Symmetry

Does the data look symmetric relative to the middle?

Does the distribution of the left half look like the right half?

Is the data skewed?

Are there tails on the data that stretch out away from the center?

Skewed to the Left: tail is on the leftSkewed to the Right: tail is on the right

Slide10

Any unusual features (outliers)?

Sometimes a small number of data values are significantly far away from the rest.

Sometimes they can be a mistake in the data but can also be legitimate values that can be left out with a good explanation.

Slide11

Center of the Distribution: Median

Once we’ve described the basic shape, we want to be able to talk about the center.

Use the horizontal axis to try to identify the center or median.

Half the data above the median, half below

Slide12

Spread of the Data

Range: max – min

Only takes into account the very extremes, doesn’t measure spread in between

Interquartile

Range (IQR)

Quartiles divide data into quartiles (quarters)Lower Quartile: separates bottom 25% from rest of dataUpper Quartile: separates top 25% from rest of dataIQR = upper quartile – lower quartileContains the middle half of the data

Slide13

5 Number Summary

Max

Q3 (upper quartile)

Median

Q1 (lower quartile)

Min

Slide14

Find 5

Number Summary

99, 53, 93 , 82 , 85 , 64 , 75 , 62 , 74 , 81 , 73 , 70 , 81 , 73 , 94, 67 , 93 , 87 , 85 , 36 , 80 , 78

Slide15

Summarizing Symmetric Distributions: The Mean

When data is skewed or contains outliers, the

median

is a useful measure of the center.

For symmetric data distributions, the

mean is another useful calculation for the center.The mean is the arithmetic average Balancing point for the histogram

Slide16

Mean vs. Median

There is a rumor that dean of UNC announced that the average starting salary of graduates majoring in geography in 1984 was $300,000. That seems a bit high, any idea why?

Well, it turns out that Michael Jordan was a geography major and got a $3,000,000 contract in the NBA. While the rest of the geography majors made $25,000 - $45,000, this outlier distorted the mean.

Slide17

Spread: The Standard Deviation

IQR measures spread, but only uses 2 data values.

The

standard deviation

uses

every data value.Only makes sense with symmetric data.Measures how far each data value is from the mean and averages them together.

Slide18

Calculating Std. Dev. by hand

data

deviations

Squared

deviations1010 – 6 = 442= 1644 – 6 = -2(-2)2= 422 – 6 = -4

(-4)

2

= 16

8

8 – 6 = 2

2

2

= 4

6

6 –

6 = 0

0

2

= 0

Data: 10, 4, 2, 8, 6 mean = 6

Sum of squared deviations: 40

Divide by n-1: 10

Square root: ≈ 3.16

Standard Deviation = 3.16

Slide19

Why do we want to use the Standard Deviation?

Look at these 3 data sets:

0, 0, 0, 0, 0, 10, 10, 10, 10, 10

0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

0, 0, 0, 5, 5, 5, 10, 10, 10

Find their mean, median, mode and spread. What do you see?

Slide20

Looking at Histograms again

Source:

Intro Stats,

DeVeaux

For each of the data sets below, create a histogram and use that to decide which set of summary statistics to calculate and then calculate them using Minitab.

Neck SizesStudent EmailGasoline Usage

Slide21


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