1. Statistics is...
a collection of techniques for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data... statistics uses data to gain understanding, to answer questions…or as the textbook says:
“the science of learning from data”
A dataset is… a collection of information about individuals (or observations) "measured" on variables

2. A variable is…
any characteristic of an individual or observation.
a variable takes on different values for different
observations… the distribution of a variable
tells us what values it takes on and with what frequency or relative frequency. We are interested in analyzing the distributions of variables both graphically and numerically …
Variables come in two basic varieties…
categorical - puts an individual into categories or
into its levels
quantitative - takes on numerical values, like
measurements or counts or rates

3. The first 10 observations of Dr. Padgett's dataset are given above.
plantno marsh form type flower plantht lleaves totmass bllenavg
si un unk no
si un unk no
si un unk no
si tn tall no
si tn tall no
si tn tall no
si un unk no
si tn tall no
ph tn tall no
ph tn tall no
The first 10 observations of Dr. Padgett's dataset
are given above.
The categorical variables are marsh, form, type
and flower. Their values are categories.
The quantitative variables are plantht, lleaves,
totmass, and bllenavg. Notice that their values
are numbers obtained by counting, measuring,
or by computing ratios.

4. Graphical analysis of data
Graphs help us visually understand the distribution of a variable…
The type of graph used in an analysis depends on the type of variable.
For categorical variables, we use bar graphs or pie charts
For quantitative variables, we use histograms or stemplots or … In the special circumstance that observations are ordered in time, we can make time plots to look for trends in the variable over time…
Analyze the distributions of the variables in Dr. Padgett's dataset, using JMP to see the graphics available there. Make sure you understand what they mean.

5. Once a graph of the variable is made, we can begin to understand its distribution by looking at the following:
look at the overall pattern in the graph and for striking deviations from that overall pattern. Peaks? Gaps? Symmetric? Skewed?
describe the overall pattern of the distribution by talking about its shape, center, and spread (or variation).
look for possible outliers in the distribution; i.e., those values of the variable that seem to fall outside the overall pattern you see.
These features will be important for all types of graphs…

6. For categorical variables, the best we can do is count the number of observations in each category (and compute the percent in each category). The bar graph plots the counts or percents falling in each of the categories… below is the distribution of the highest educational level of people in the U.S. aged years…see 1.1, 2/8 .
This variable is categorical but is also called ordinal since its values can be ordered in some sense.

7. Example: Top 10 causes of death in the United States 2001
Rank
Causes of death
Counts
% of top10
% of total deaths 1
Heart disease
700,142
37%
29% 2
Cancer
553,768
23% 3
Cerebrovascular
163,538 9% 7% 4
Chronic respiratory
123,013 6% 5% 5
Accidents
101,537 4% 6
Diabetes mellitus
71,372 3% 7
Flu and pneumonia
62,034 8
Alzheimer’s disease
53,852 2% 9
Kidney disorders
39,480 10
Septicemia
32,238 1%
All other causes
629,967
26%
For each individual who died in the United States in 2001, we record what was the cause of death. The table above is a summary of that information. Why are
the percentages in the two columns different??

8. Ways to graph quantitative data
Line graphs: time plots
Use when there is a meaningful sequence, like time. The line connecting the points helps emphasize any change over time.
Histograms and stemplots
These are summary graphs for a single variable. They are very useful to understand the pattern of variability in the data.
Other graphs to reflect numerical summaries.

9. Line graphs: time plots
In a time plot, time always goes on the horizontal, x axis.
We describe time series by looking for an overall pattern and for striking deviations from that pattern. In a time series:
A trend is a rise or fall that persist over time, despite small irregularities.
A pattern that repeats itself at regular intervals of time is called seasonal variation.

10.
For quantitative variables, we'll consider both stemplots and histograms - stemplots are much easier to draw and interpret… see Table 1.2 and consider the female literacy rates in Islamic countries in Ex.1.7 (1.1, 4/8) The tens digit becomes the stem, and the units digit becomes the leaf (see Fig. 1.5)

11. Histograms break up the range of values of the variable into intervals (on the horizontal axis) and displays (on the vertical axis) the count (or percent) of observations falling into those intervals - choosing the number of intervals and/or the width of the intervals can be problematic… usually histograms are made by computer programs (like JMP). See Fig. 1.7, p. 14 (1.1, 5/8) Data is in table 1.3 (1.1, 5/8).

12. Homework Carefully read sections 1.1 and 1.2
Work through the examples in those two sections…
Work on the following problems: # (these are scatted throughout section 1.1), , 1.17, 1.18, 1.20, 1.21, 1.23, 1.24, 1.25, 1.27, 1.37
Use JMP or an Applet for the following problems: , 1.39, 1.42,