# Variables. Exploratory Data Analysis

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1 Exploratory Data Analysis Exploratory Data Analysis involves both graphical displays of data and numerical summaries of data. A common situation is for a data set to be represented as a matrix. There is a row for each unit and a column for each variable. A unit is an object that can be measured, such as a person, or a thing. A variable is a characteristic of a unit that can be assigned a number or a category. In the data set I collected from the survey of students in the class, each student who responded to the survey is a unit. Variables include sex, major, year in school, miles from home, height, and blood type. Variables We can also make a categorical variable from a continuous variable by dividing the range of the variable into classes (So, for example, height could be categorized as short, average, or tall). Identifying the types of variables can be important because some methods of statistical analysis are appropriate only for a specific type of variable. Statistics 371, Fall Statistics 371, Fall Variables Statistics 371, Fall 2003 Exploratory Data Analysis Bret Larget Department of Statistics University of Wisconsin - Madison September 8, 2003 Variables are either quantitative or categorical. In a categorical variable, the measurement for each unit is a category. For example, for the data I collected in the course survey, the variables blood type and sex are categorical. The variable year in school is an example of an ordinal categorical variable, because there is a meaningful ordering of the levels of the variable. Quantitative variables record a number for each unit. The variable height is an example of a continuous variable because it could take on a continuum of possible values. The variable number of sisters is discrete because we can list the possible values. Often, continuous variables are rounded to a discrete set of values. Most people round their height to the nearest inch (or half inch). Statistics 371, Fall

2 Summaries of Categorical Variables A frequency distribution is a list of the observed categories and a count of the number of observations in each. A frequency distribution may be displayed with a table or with a bar chart. For ordinal categorical random variables, it is conventional to order the categories in the display (table or bar chart) in the meaningful order. For non-ordinal variables, two conventional choices are alphabetical and by size of the counts. The vertical axis of a bar chart may show frequency or relative frequency. It is conventional to leave space between bars of a bar chart of a categorical variable. Statistics 371, Fall Summary of Majors > cbind(summary(major)) [,1] Animal Science 2 Bacteriology 1 Biochemistry 3 Biological Aspects of Conservation 2 Biology 19 Biology/Zoology 1 Biomedical Engineering 9 Botany 1 Dairy Science 1 Forestry 1 Genetics 31 Graduate student in Bacteriology 1 Kinesiology 4 Medical MicroBiology and Immunology 1 Molecular Biology 1 Nursing 1 Plant Pathology 1 Possibly Kinesiology 1 Undecided 1 Wildlife Ecology 5 Wildlife Ecology - Natural Resources 1 Zoology 11 Statistics 371, Fall Samples A sample is a collection of units on which we have measured one or more variables. The number of observations in a sample is called the sample size. Common notation for the sample size is n. The textbook adopts the convention of using uppercase letters for variables and lower case letters for observed values. Summary of Blood Type Data Here is a frequency table. > summary(bloodtype) A AB B O NA s Here is a bar chart. > barplot(summary(bloodtype)) A AB B O NA s Statistics 371, Fall Statistics 371, Fall

3 Summaries of Quantitative Variables Quantitative variables from very small samples can be displayed with a dotplot. Histograms are a more general tool for displaying the distribution of quantitative variables. A histogram is a bar graph of counts of observations in each class, but no space is drawn between classes. If classes are of different widths, the bars should be drawn so that areas are proportional to frequencies. Selection of classes is arbitrary. Different choices can lead to different pictures. Too few classes is an over-summary of the data. Too many classes can cloud important features of the data with noise. Summary of Miles from CSSC > hist(milescssc, col = 3) Histogram of MilesCSSC MilesCSSC Statistics 371, Fall Statistics 371, Fall Summary of Second Majors > cbind(summary(major2)) [,1] 69 Anthropology 1 Bacteriology 4 Biochemistry 2 Biological Aspects of Conservation 2 Biology 2 Dietetics 1 Economics 1 Entomology 1 French 1 Genetics 1 Horticulture 1 IES 2 Life Science Communication 1 Math 2 Physics 1 Plant Pathology 1 Political Science 1 Psychology 1 Scandinavian Studies 1 Spanish 1 Wildlife Ecology 2 Statistics 371, Fall A Dotplot of Hours of Sleep > source("../r/dotplot.r") > dotplot(sleep) Statistics 371, Fall

4 Summary of Miles from Home for Students within 150 miles > hist(mileshome[mileshome < 150], col = 3) Histogram of MilesHome[MilesHome < 150] Stem-and-Leaf Diagrams Stem-and-Leaf diagrams are useful for showing the shape of the distribution of small data sets without losing any (or much) information. Begin by rounding all data to the same precision. The last digit is the leaf. Anything before the last digit is the stem. In a stem-and-leaf diagram, each observation is represented by a single digit to the right of a line. Stems are shown only once. Show stems to fill gaps! Combining or splitting stems can lead to a better picture of the distribution MilesHome[MilesHome < 150] Statistics 371, Fall Statistics 371, Fall Summary of Miles from Home > hist(mileshome, col = 3) Histogram of MilesHome Summary of Height > par(mfrow = c(1, 2)) > hist(height[keep], col = 2) > hist(height[keep], breaks = breaks, col = 3) Histogram of Height[keep] Histogram of Height[keep] MilesHome Height[keep] Height[keep] Statistics 371, Fall Statistics 371, Fall

5 Stem-and-Leaf of Miles from CSSC The decimal point is at the Statistics 371, Fall Measures of Center There are two common measures of center for quantitative variables, the mean and the median. For sample data y 1, y 2,..., y n, the sample mean is n y i i=1 ȳ = n The sample mean is the balancing point of the variable. The sample median is the middle value at least half the values are larger and at least half are smaller. If a sample size is odd, only one number will be the median. If a sample size is even, any number between the middle two numbers could be the median. By convention we take the average of the middle two numbers. Imagine a histogram made of a uniform solid material. The mean is about the point at which the solid would balance. The median is about at a point that would divide the area of the histogram exactly in half. Statistics 371, Fall Stem-and-Leaf Diagram of Brothers and Sisters > stem(brothers, scale = 0.5) The decimal point is at the > stem(sisters, scale = 0.5) The decimal point is at the Skewness Histograms show several qualitative features of a quantitative variable, such as the number of modes and skewness. A distribution is approximately symmetric if the left and right halves are approximately mirror images of each other. A distribution is skewed to the right if the right half of the data (the larger values) are more spread out than the left half of the data. A distribution is skewed to the left if the left half of the data (the smaller values) are more spread out than the right half of the data. It is fairly common for biological data to be skewed to the right. Often times there is a barrier below which there can be no values, but no upper limit. Statistics 371, Fall Statistics 371, Fall

6 Quantiles Quantiles are a generic name for positions in the distribution of a quantitative variable. For example, percentiles divide a distribution into 100 equal sized parts. Quartiles. which divide a distribution into four equal sized parts are a common statistical tool. The first quartile, Q 1, is the location that separates the smallest quarter of the data from the rest. This is also known as the 25th percentile. The third quartile, Q 3, is the location that separates the top quarter of the data and is also known as the 75th percentile. The median is the second quartile. Different authors and statistical software packages have different definitions of quantiles. The definition I prefer is that a value x is a p quantile of a sample if the proportion of observations less than or equal to x is at least p and if the proportion of observations greater than or equal to x is at least 1 p. Statistics 371, Fall Boxplots In a simple boxplot, a box extending from the first to third quartiles represents the middle half of the data. The box is divided at the median, and whiskers extend from each end to the maximum and minimum. It is common to draw more sophisticated boxplots in which the whiskers extend to the most extreme observations within upper and lower fences and individual observations outside these fences are labeled with individual points as potential outliers. The most common rule defining the fences are that they are 1.5 IQR below the first quartile and 1.5 IQR above the third quartile. Statistics 371, Fall Comparing the mean and the median The mean and median of a symmetric distribution are the same. The median is more resistant to outliers than the mean. For example, the mean and median of the numbers 1, 2, 3 are 2, but for the data set 1, 2, 30, the median is still 2, but the mean is 11, far away from each observation. The median can be a better measure of a typical value than the mean especially for strongly skewed variables. If a variable is skewed to the right, the mean will typically be larger than the median. The opposite is true if the variable is skewed to the left. Example: > mean(mileshome) [1] > median(mileshome) [1] Statistics 371, Fall Number Summary The minimum, first quartile, median, third quartile, and maximum are called the five-number summary of a quantitative variable. The interquartile range (IQR) is the difference between the third and first quartiles. IQR = Q 3 Q 1 The range is the difference between the maximum and the minimum. Graphical displays of five-number summaries are called boxplots. Example: > fivenum(milescssc) [1] Statistics 371, Fall

7 Boxplot of Miles from CSSC > boxplot(milescssc) Boxplot of Hours of Sleep > boxplot(sleep) Measures of Dispersion The standard deviation or SD is the most common statistical measure of dispersion. A deviation from the mean is the signed distance of an observation from the mean. deviation = value of observation mean Observations greater than the mean have positive deviations while those less than the mean have negative deviations. The standard deviation is a special type of average deviation n (y i ȳ) 2 i=1 s = n 1 This is almost the square root of the mean squared deviation from the mean. Statistics 371, Fall Statistics 371, Fall Side-by-side boxplot of height versus sex > boxplot(split(height, Sex)) Female Male Statistics 371, Fall Statistics 371, Fall

8 The Empirical Rule For many variables (especially those that are nearly symmetric and bell-shaped), the following empirical rule is often a very good approximation. About 68% of the observations are within 1 SD of the mean. About 95% of the observations are within 2 SDs of the mean. Nearly all observations are within 3 SDs of the mean. Example: > m = mean(sleep) > s = sd(sleep) > m [1] > s [1] > sum(abs(sleep - m) < s)/length(sleep) [1] > sum(abs(sleep - m) < 2 * s)/length(sleep) [1] > sum(abs(sleep - m) < 3 * s)/length(sleep) [1] 1 Statistics 371, Fall Measures of Dispersion Statisticians use n 1 instead of n in the denominator for a technical mathematical reason of historical, if not practical, importance. The standard deviation can often be interpreted as the size of a typical deviation from the mean. Samples and Populations The previous techniques are useful for describing a data set, or a sample of data. It is often of interest to generalize findings from a sample to a larger group that statisticians call a population. This generalization is called statistical inference. Statistical inference is often concerned with using statistics, characteristics that can be calculated from sample data, to estimate parameters, characteristics of populations. Examples: p = population proportion, ˆp = sample proportion µ = population mean, ȳ = sample mean σ = population standard deviation, s = sample standard deviation Statistics 371, Fall Statistics 371, Fall

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