Graphics, Tables and Basic Statistics (Chapter 3)

• Review approaches to visually displaying Data.
• Graphics that display key statistical features of measurements from a sample.
• Define the distribution of a set of data.
• Review common basic statistics.
• Extremes (Minimum and Maximum)
• Central Tendency ( Mean, Median)
• Spread (Range, Variance, Standard Deviation)
• Review not so common basic statistics.
• Extremes (upper and lower quartiles)
• Central Tendency (Mode, Winsorized Mean)
• Spread (Interquartile Range)

Lecture Objectives :

Graphics

The visual portrayal of quantitative information

Are used to:

• Display the actual data table
• Display quantities derived from the data
• Show what has been learned about the data from other analyses
• Allow one to see what may be occurring in the data over and above what has already been described

Graphical Display

Objectives

• Tabulation
• Description
• Illustration
• Exploration

“A picture is worth a thousand words…”

Objectives

• Avoid distortion of the true story.
• Induce the viewer to think about the substance, not the graph.
• Reveal the data at several layers of detail.
• Encourage the eye to compare different pieces.
• Support the statistical and verbal descriptions of the data.

As you create graphics keep the following in mind.

Nutrient Profiles for Selected Candy

Chocolate Manufacturers Association

National Confectioners Association

7900 Westpark Blvd. Suite A 320, McLean, Virginia 22102

URL: http://www.candyusa.org/nutfact.html

Standard data format

Qualitative characteristic

Quantitative characteristics

Example Data

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Candy data as Excel spreadsheet

Column chart

Display the data table

What are the problems with this graph?

Alternate Display

Sorting and expanding the scale of the graph allows all labels to be seen as well as displaying a characteristic of the data.

Vertical Display of Data

In this case, a vertical display allows better comparison of calorie amounts.

Pie Charts

A pie chart is good for making relative comparisons among pieces of a whole.

Statistical Uses of Graphics

Describe Distributions of Measurements

• Box & Whisker plot (Boxplot)
• Histogram

Compare Distributions

• Multiple Box & Whisker plots

Associations and Bivariate Distributions

• Scatter plot
• Symbolic scatter plot

Multidimensional Data Displays

• All pairwise scatter plot
• Rotating scatter plot

Graphical Methods in Support of Statistical Inference

• Regression lines
• Residual plots
• Quantile-quantile plots
• Cumulative distribution function plots
• Confidence and prediction interval plots
• Partial leverage plots
• Smoothed curves

Most of these will be demonstrated at some point in the course.

Basic Statistics

Before we get more into statistical uses of graphics, we need to define some basic statistics. These statistics are typically referred to as “descriptive statistics”, although as we will see, they are much more than that. These basic statistics address specific aspects of the distribution of the data.

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• What is the range of the data?
• When we sort the data, what number might we see in the “middle” of the range of values?
• What number tells us over what sub range do we find the bulk of the data ?

We will use the calorie data to illustrate.

Extremes

Extremes

• Minimum(calories) = 10
• Maximum(calories) = 210

First, if we sort the data we can immediately identify the extremes.

The minimum and maximum are “statistics”.

Reminder: A statistic is a function of the data. In this case, the function is very simple.

Range

Range: the difference between the largest and smallest measurements of a variable.

Extremes

• Minimum(calories) = 10
• Maximum(calories) = 210

Range = 210-10 = 200

Tells us something about the spread of the data.

The middle of the range is a measure of the “center” of the data.

Midrange = minimum + (Range/2)

=10 + 200/2

=110

Is it a “good” measure of the center of the data?

Measures of Central Tendency

Median = middle value in the sorted list of n numbers: at position (n+1)/2

= unique value at (n+1)/2 if n is an odd number or

= average of the values at n/2 and n/2+1 if n is even

= (160 + 160)/2 = 160

Mean = sum of all values divided by number of values (average)

= (10 + 60 + 60 + 60 + … + 210 + 210)/22

= 133.6

Trimmed mean = mean of data where some fraction of the smallest and largest data values are not considered. Usually the smallest 5% and largest 5% values (rounded to nearest integer) of data are removed for this computation.

= 136.0 (with 10% trimmed, 5% each tail).

Estimate the value that is in the center of the “distribution” of the data .

Again – these are statistics (functions of the data)

Mathematical Notation

We will need some mathematical notation if we are to make any progress in understanding statistics. In particular, since all statistics are functions of the data, we should be able to represent these statistics symbolically as equations using mathematical notation.

Quartiles

Suppose we divide the sorted data into four equal parts. The values which

separate the four parts are known as the quartiles. The first or lower quartile

Q1, is the 25th percentile of the sorted data, the second quartile, Q2, is the median and the third or upper quartile, Q3, is the 75th percentile of the data. Because the sample size integer, n+1, does not always divide easily by 4, we do some estimating of these quartiles by linear interpolation between values.

Here n=22, (n+1)/4=23/4=5.75, hence Q1 is three quarters between the 5th and 6th observations in the sorted list. The 5th value is 60 and the 6th value is 60, thus

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60 + .75(60-60)=60.

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For Q2, (n+1)/2 = 23/2 = 11.5, e.g. half way between the 11^th and 12^th obs.

Q2 = 160 + .5(160-160) = 160.

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For Q3, 3(n+1)/4 = 3(23)/4 = 69/4 = 17.25, e.g a quarter of the way between the 17^th and 18^th observations.

Q3 = 180 + .25(180-180) = 180

Percentiles

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100p^th Percentile: that value in a sorted list of the data that has approx p100% of the measurements below it and approx (1-p)100% above it. (The p quantile.)

Examples:

Q1 = 25^th percentile

Q2 = 50^th percentile

Q3 = 75^th percentile

• Ott & Longnecker suggest finding a general 100p^th percentile via a complicated graphical method (pp. 87-90).
• We will relegate these elaborate calculations to software packages…
• We will however return to this later when we discuss QQ-Plots.

Distribution function

0 < p < 1

Simplified Quartiles

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A simpler way to find Q1 & Q3 is as follows:

1. Order the data from the lowest to the highest value, and find the median.
2. Divide the ordered data into the lower half and the upper half, using the median as the dividing value. (Always exclude the median itself from each half.)
3. Q1 is just the median of the lower half.
4. Q3 is just the median of the upper half.

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Ex: For the candy data we still get Q1=60 and Q3=180.

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Ex: {3, 4, 7, 8, 9, 11, 12, 15, 18}.

We get Q1=(4+7)/2=5.5 and Q3=(12+15)/2=13.5.

Measures of Variability

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• Range
• Interquartile Range
• Variance
• Standard Deviation

Variance and Standard Deviation

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Variance: The sum of squared deviations of measurements from their mean divided by n-1.

These measure the spread of the data.

Rough approximation for large n: s≈range/4.

Using Excel Data Analysis Tool

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Under the “Tools” menu in Excel there is a tool called “Data Analysis”. This tool is not normally loaded when the Excel default installation is used so you may have to load it yourself. This will require the Excel CD. Use the Tools > Add Ins option, select the Data Analysis tool and add it to your menu.

Excel Data Analysis Tool

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Select the Data Analysis Tool

Select Descriptive Statistics

The menu below appears.

Enter the Input Range and check the output options desired.

Excel Descriptive Statistics Output

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You should be able to easily identify the basic statistics we have described so far.

Note: the variance is not in this list. This is typical of statistics packages. Since the variance is simply the square of the Standard Deviation, it is often considered redundant.

Learn to use the Excel Help files. Type “Statistic” in the Excel Help Keyword dialog for a list of helps available.

Importing a text data file in standard format into Minitab

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Pull down menus

Session worksheet with script commands

Spreadsheet like data area

Computing Descriptive Stats

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Frequency Table

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Mode = most abundant

A tabular representation of a set of data.

A frequency table also describes the distribution of the data and facilitates the estimation of probabilities.

The “Histogram” dialog in the Excel Data Analysis Tool can be used to create this table. But it is not straightforward.

Stem and Leaf Plot

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Rough grouping or “binning” of the data.

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Histogram of calories N = 22

Midpoint Count

20 1 *

40 0

60 5 *****

80 1 *

100 0

120 0

140 3 ***

160 6 ******

180 2 **

200 1 *

220 3 ***

• A printer graph of the frequency table.
• Easy to do by hand.
• Quick visualization of the data.

Box Plot for Calories

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Median (Q2)

75th percentile (Q3)

25th percentile (Q1)

Maximum

Minimum

Interquartile

range

Box Plot

(SAS Proc Insight)

A visualization of most of the basic statistics.

Is there an Excel Tool? No.

Percentiles

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100p^th Percentile: that value in a sorted list of the data that has approx p100% of the measurements below it and approx (1-p)100% above it. (The p quantile.)

Examples:

Q1 = 25^th percentile

Q2 = 50^th percentile

Q3 = 75^th percentile

A distribution is said to be symmetric if the distance from the median to the 100p^th percentile is the same as the distance from the median to the 100(1-p)^th percentile. Otherwise the distribution is said to be skewed.

In the case above, the distribution is skewed to the right since the right tail is longer than the left tail.

Smoothed histogram

0 < p < 1

Frequency Histogram

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Frequency

Bin width

A graphical presentation of the frequency table where the relative areas of the bars are in proportion to the frequencies.

This is a frequency histogram

Density Histogram

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A density histogram (or simply a histogram) is constructed just like a frequency histogram, but now the total area of the bars sums to one. This is accomplished by rescaling the vertical axis. Instead of frequencies, the vertical axis records the rescaled value of the density.

Sum of shaded area is equal to one.

Histograms have important ties to probability.

Number of Bins for Histograms

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How we view the “distribution” of a dataset can depend on how much data we have and how it is binned.

Scatterplot

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Graphics to examine relationships

Is the relationship linear or non-linear?

Matrix Plot

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View multiple variables at one time.

Three-D� Views

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Chernoff Faces

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Displaying

multiple variables

symbolically.