Summarizing Data - University of Wisconsin-Madison

Summarizing Data - University of Wisconsin-Madison

Lecture 2 Summarizing the Sample WARNING: Todays lecture may bore some of you Its (sort of) not my faultIm required to teach you about what were going to cover today. Ill try to make it as exciting as possible

But youre more than welcome to fall asleep if you feel like this stuff is too easy Lecture Summary Once we obtained our sample, we would like to summarize it. Depending on the type of the data (numerical or categorical) and the dimension (univariate, paired, etc.), there are different methods of summarizing the data. Numerical data have two subtypes: discrete or continuous Categorical data have two subtypes: nominal or ordinal

Graphical summaries: Histograms: Visual summary of the sample distribution Quantile-Quantile Plot: Compare the sample to a known distribution Scatterplot: Compare two pairs of points in X/Y axis. Three Steps to Summarize Data 1. Classify sample into different type 2. Depending on the type, use appropriate numerical summaries 3. Depending on the type, use appropriate

visual summaries Data Classification Data/Sample: Dimension of (i.e. the number of measurements per unit ) Univariate: one measurement for unit (height) Multivariate: multiple measurements for unit (height, weight, sex) For each dimension, can be numerical or categorical Numerical variables

Discrete: human population, natural numbers, (0,5,10,15,20,25,etc..) Continuous: height, weight Categorical variables Nominal: categories have no ordering (sex: male/female) Ordinal: categories are ordered (grade: A/B/C/D/F, rating: high/low) Data Types For each dimension Numerical

Continuous Discrete Categorical Nominal Ordinal

Summaries for numerical data Center/location: measures the center of the data Examples: sample mean and sample median Spread/Dispersion: measures the spread or fatness of the data Examples: sample variance, interquartile range Order/Rank: measures the ordering/ranking of the data Examples: order statistics and sample quantiles

Summary Type of Sample Sample mean, Continuous Formula

Notes Sample variance, Continuous

Summarizes the center of the data Sensitive to outliers Summarizes the spread of the data Outliers may inflate this value Order statistic,

Continuous ith largest value of the sample Summarizes the order/ rank of the data Sample median,

Continuous If is even: If is odd: Summarizes the center of the data Robust to outliers

If for : Otherwise, do linear interpolation Sample quartiles, Sample Interquartile Range (Sample IQR) Continuous

Continuous Summarizes the order/ rank of the data

Robust to outliers Summarizes the spread of the data Robust to outliers Multivariate numerical data Each dimension in multivariate data is univariate and hence, we can use the numerical summaries from univariate data (e.g. sample mean, sample variance) However, to study two measurements and their relationship, there are numerical summaries to

analyze it Sample Correlation and Sample Covariance Sample Correlation and Covariance Measures linear relationship between two measurements, and , where Sign indicates proportional (positive) or inversely proportional (negative) relationship If and have a perfect linear relationship, or -1

Sample covariance = How about categorical data? Summaries for categorical data Frequency/Counts: how frequent is one category Generally use tables to count the frequency or proportions from the total Example: Stat 431 class composition a

Undergrad Graduate Staff Counts 17 1

2 Proportions 0.85 0.05 0.1

Are there visual summaries of the data? Histograms, boxplots, scatterplots, and QQ plots Histograms For numerical data A method to show the shape of the data by tallying frequencies of the measurements in the sample Characteristics to look for: Modality: Uniform, unimodal, bimodal, etc.

Skew: Symmetric (no skew), right/positive-skewed, left/negativeskewed distributions Quantiles: Fat tails/skinny tails Outliers Boxplots For numerical data Another way to visualize the shape of the data. Can identify Symmetric, right/positive-skewed, and left/negative-skewed distributions Fat tails/skinny tails

Outliers However, boxplots cannot identify modes (e.g. unimodal, bimodal, etc.) Upper Fence = Lower Fence = Quantile-Quantile Plots (QQ Plots) For numerical data: visually compare collected data with a

known distribution Most common one is the Normal QQ plots We check to see whether the sample follows a normal distribution This is a common assumption in statistical inference that your sample comes from a normal distribution Summary: If your scatterplot hugs the line, there is good reason to believe that your data follows the said distribution. Making a Normal QQ plot 1. Compute z-scores:

2. Plot th theoretical normal quantile against th ordered z-scores (i.e. Remember, is the sample quantile (see numerical summary table) 3. Plot line to compare the sample to the theoretical normal quantile If your data is not normal You can perform transformations to make it look normal

For right/positively-skewed data: Log/square root For left/negatively-skewed data: exponential/square Comparing the three visual techniques Histograms Advantages: With properly-sized bins, histograms can summarize any shape of the data (modes,

skew, quantiles, outliers) Disadvantages: Difficult to compare side-byside (takes up too much space in a plot) Depending on the size of the bins, interpretation may be different Boxplots Advantages:

QQ Plots Advantages: Can identify whether the Dont have to tweak with data came from a certain graphical parameters distribution (i.e. bin size in histograms) Dont have to tweak with

Summarize skew, graphical parameters quantiles, and outliers (i.e. bin size in histograms) Can compare several Summarize quantiles measurements side-byside Disadvantages: Disadvantages:

Cannot distinguish modes! Difficult to compare sideby-side Difficult to distinguish skews, modes, and outliers Scatterplots For multidimensional, numerical data: Plot points on a dimensional axis Characteristics to look for:

Clusters General patterns See previous slide on sample correlation for examples. See R code for cool 3D animation of the scatterplot Lecture Summary Once we obtain a sample, we want to summarize it. There are numerical and visual summaries Numerical summaries depend on the data type (numerical or categorical)

Graphical summaries discussed here are mostly designed for numerical data We can also look at multidimensional data and examine the relationship between two measurement E.g. sample correlation and scatterplots Extra Slides Why does the QQ plot work? You will prove it in a homework assignment

Basically, it has to do with the fact that if your sample came from a normal distribution (i.e. ), then where is a tdistribution. With large samples (, . Thus, if your sample is truly normal, then it should follow the theoretical quantiles. If this is confusing to you, wait till lecture on sampling distribution Linear Interpolation in Sample Quantiles If you want an estimate of the sample quantile that is not , then you do a linear interpolation 1. For a given , find such that

2. Fit a line, , with two points and . 3. Plug in as your and solve for . This will be your quantile.

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