ctrc core curriculum seminar series descriptive statistics: data types and measures, central...
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CTRC Core Curriculum Seminar Series
Descriptive Statistics: Data Types and Measures, Central
Tendency, Variability
Chang-Xing Ma, PhDAssociate Professor
Department of Biostatistics, UB
January 4, 2012
Disclosure Statement
• Chang-Xing Ma, PhD– Nothing to disclose
Goals and Objectives
• Goals: Gain the knowledge of basic statistics and how to describe the data
• Objectives: – Describe the data type– Summarize data – Understand Measure of Central Tendency– Understand Measure of Dispersion
Outline
• Basic concepts of biostatistics• Data type• Summarize data• Measure of Central Tendency• Measure of Dispersion
Some terminology
• Statistics is the study of how to collect, organize, analyze, and interpret numerical information from data
• Biostatistics—the theory and techniques for collecting, describing, analyzing, and interpreting health data.
Some terminology
• Population refer to all measurements or observations of interest
• Sample is simply a part of the population. But the sample MUST represent the population. – A random sample is such a representative sample
• The sample must be large enough• The sample should be selected randomly
Some terminology
• Parameter is some numerical or nominal characteristic of a population– A parameter is constant, e.g. mean of a population– Usually unknown
• Statistic is some numerical or nominal characteristic of a sample.– We use statistic as an estimate of a parameter of the
population– It tends to differ from one sample to another– We also use statistic to test hypothesis
Population: all U.S. persons ~ Normal (µh,σh2),
A random sample: sample size =
Gender Height Weight
mean height:
mean weight
Parameters
A sample
std height:
std weight
statistics
% of male (=1)
(µw,σw2),
Generate
True Parameters
Sources of data
Records Surveys Experiments
Comprehensive Sample
Quantitative
continuous
Types of variables
Quantitative variables Qualitative variables
Quantitative
discrete
Qualitative
nominal
Qualitative ordinal
Data Types
• Numerical (Quantitative)– numerical measurement
• Height• Weight
• Categorical (Qualitative)– with no natural sense of ordering
• Gender• Hair color • Blood type
Numerical Variable
• Continuous– Range of values
• Height in inch
• Discrete– Limited possible values
• # of smoking per day• # of children in a family
• Age -
• Ordinal (Categorical) vs. Discrete (Numerical)• Ordinal
– Cancer Stage I, II, III, IV– Stage II ≠ 2 times Stage I– Categories could also be A, B, C, D
• Discrete– # of children: 0, 1, 2, …– 4 children = 2 times 2 children
Determining Data Types
Descriptive Statistics – reducing a complex mass of data to a manageable set of information
• Descriptive Statistics: the summary and presentation of data to:– simplify the data– enable meaning full interpretation– support decision making
• Numerical descriptive measures (few numbers)
• Graphical presentations
Inferential statistics
From a sample • to estimate population parameters• to test hypothesis • to build the model to reflect the population• …
The student test score (FCAT)
Student ID Race Sex Reading Math PovertyCode:
Race:W – WhiteB – BlackH – HispanicA – Asian
Sex:F – FemaleM – Male
Poverty:0 – not poor1 – poor
Problem 1
1.Among the 6 variables, which ones are qualitative and which ones are quantitative?2.Is Race nominal or ordinal?
Descriptive Statistics
• Categorical variables: – Frequency distribution– Bar chart, pie chart– Contingency tables
• Continuous variables:– Grouped frequency table– Central Tendency– Variability
Simple Frequency DistributionAn ordered arrangement that shows the
frequency of each level of a variable.race Frequency Percent-----------------------------A 7 4.07 B 42 24.42 H 8 4.65 W 115 66.86
sex Frequency Percent----------------------------F 86 50.00 M 86 50.00
Simple Frequency Distribution
• It is useful for categorical variable• For continuous variable,
– it allows you to pick up at a glance some valuable information, such as highest, lowest value.
– ascertain the general shape or form of the distribution
– make an informed guess about central tendency values
Bar Chart
• summarizing a set of categorical data - nominal or ordinal data
• It displays the data using a number of rectangles, each of which represents a particular category. The length of each rectangle is proportional to the number of cases in the category it represents
• can be displayed horizontally or vertically
• they are usually drawn with a gap between the bars
• Bars for multiple (usually two) variables can be drawn together to see the relationship
0
20
40
60
80
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120
A B H W
Race
BY
Horizontally
Pie Chart
• summarizing a set of categorical data - nominal or ordinal data
• It is a circle which is divided into segments.
• Each segment represents a particular category.
• The area of each segment is proportional to the number of cases in that category.
Female Male
Complex frequency distribution Table
Distribution of 20 lung cancer patients at the chest department of Alexandria hospital and 40 controls in May 2008 according to smoking
Smoking
Lung cancerTotal
Cases Control
No. % No. % No. %
Smoker15 75% 8 20% 23
38.33
Non smoker
5 25% 32 80% 3761.6
7
Total 20 100 40 100 60 100
How about continuous variables?
• How data is distributed?
• Measure of Central Tendency
• Measure of Variability
Grouped Frequency Distribution – for continuous variable
DATA: Frequency Table
Interval Size:
0
5
10
15
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35
150 165 180 195 210 225 240 255 270 285
N:µ:σ:
New Data
HISTOGRAM
POLYGON
15
Example Data
Grouped Frequency Distribution
• BUT the problem is that so much information is presented that it is difficult to discern what the data is really like, or to "cognitively digest" the data.
• the simple frequency distribution usually need to condense even more. – It is possible to lose information (precision) about the data to gain
understanding about distributions. • This is the function of grouping data into equal-sized intervals
called class intervals.• The grouped frequency distribution is further presented as
Frequency Polygons, Histograms, Bar Charts, Pie Charts.
Describing Distributions• Bell-Shaped Distribution
– Normal distribution N (µ=0, σ2 =1)
– t-distribution
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-3 -2 -1 0 1 2 3
Describing Distributions• Skewed Distribution – positively skewed distribution
0 5 10 15 20 25 300
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Describing Distributions• Skewed Distribution – negatively skewed distribution
0 5 10 15 20 25 300
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Describing Distributions• Other Shapes
Rectangular Bimodal
Describing Distributions• Other Shapes
J-curve
0 5 10 15 20 25 300
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0 5 10 15 20 25 300
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Probability density function - Normal
green curve isstandard normaldistribution
z-transform
Measure of Central TendencyMean, Median, Mode
• The Mean– average value– not robust to outlying value
• Length of hospital stays:6, 4, 5, 9, 10, 7, 1, 4, 3, 4
• Mean=(6+4+5+9+10+7+1+4+3+4)/10=5.3
N
XX
N
ii
1
Measure of Central TendencyMean, Median, Mode
• The Median– is the point that divides a distribution of data into
two equal parts– robust to outlying value
• Length of hospital stays: sort data1 3 4 4 4 5 6 7 9 10
• median=4.5Split Data
Measure of Central TendencyMean, Median, Mode
• The Mode– is the midpoint of the interval that has highest
frequency– robust to outlying value, but sometimes
misleading• Length of hospital stays: sort data
1 3 4 4 4 5 6 7 9 10
• Mode=4, which occurred 3 times.Most frequently
Comparison between mean and median
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-3 -2 -1 0 1 2 3
Mean Median
0 5 10 15 20 25 300
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Comparison between mean and median
MeanMedian
0 5 10 15 20 25 300
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Comparison between mean and median
Mean Median
Summary
• Frequency distribution• Histogram, Polygon graph• Bar Chart, Pie Chart• Describing Distributions• Mean, Median, Mode
DATASET: http://128.205.94.145/STA2008/FL_School0022.xls
Problem 2
• In a study, we collected a medical measurements X for 4 patients
• Data of X: 2, 3, 5, 6
• Mean of X? • Median of X?• Mode of ?
Descriptive StatisticsVariability
• The sample range• Interquartile range• The sample standard deviation (SD), variance• Standard error of mean (SEM)
Measures of Dispersion - Range
• Range – the difference between the lowest and highestFor example, Age of Patients (years): 6 13 7 14 10 14 15 9 7 2 7 13 16 9 8 3 3 17 8 5 4 9 9 6lowest 2, highest 17Range=2 -17 years
• When sample size increases, the range tends to increase as well. (not robust)
Measures of Dispersion - Range
• All of curves have the same range
• Mean?• Median?
Measures of DispersionPercentiles, Deciles, Quartiles
• Percentiles: based on dividing a sample or population into 100 equal parts.
• Deciles divide the distribution into 10 parts• Quartiles divide the distribution into 4 equal parts.
– 1st quartile includes the lowest 25% of the values (Q1)– 2st quartile includes the values from 26 percentile through 50
percentile (Q2) - median– 3st quartile includes the values from 51 percentile through 75
percentile (Q3)
Measures of DispersionInterquarile Range
• Interquarile Range – the 25 percentile (1st quartile) to 75 percentile (3rd quartile)
• Age of Patients (years): 2 3 3 4 5 6 6 7 7 7 8 8 9 9 9 9 10 13 13 14 14 15 16 17– 1st quartile 6, 2nd quartile 8.5, 3rd 13– Interquarile Range = 6 -13 years
• Interquarile Range is a robust estimate of data variability
Measures of DispersionInterquarile Range
Robust estimate, less efficient
Deviations from the meanVariance and Standard Deviation
• deviation: observation - mean• “sum” of deviation
)( xxi 0)( xxiBUT
Deviations from the meanVariance and Standard Deviation
• Measure of how different the values in a set of numbers are from each other
• Variance:
• Standard Deviation:
22 )(1
1xx
ns i
2)(1
1xx
ns i
Deviations from the meanVariance and Standard Deviation
• Data set: 2,3,5,6Calculation:
22 )(
1
1xx
ns i
83.133.3)(1
1 2
xxn
s i
0.44/)6532(/ nxx i
Value of X (X- ) (X- )2
2 -2 4 3 -1 1 5 1 1 6 2 4
∑=0 ∑=10
x x
33.3)14/(10)(1
1 22
xxn
s iVariance
Standard Deviation
Three normal distributions: mean=0 s2=1 s2=2 s2=0.5
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-3 -2 -1 0 1 2 3
0,1 0,2 0,0.5Central Tendency
mean=0
LeptokurticHomogenous
Narrow scatter
PlatykurticHeterogeneous
wide scatter
Mesokurtic
Example 2: FEV1 (litres) of 57 male medical students
Table: FEV1 (litres) of 57 male medical students 2.85 3.19 3.50 3.69 3.90 4.14 4.32 4.50 4.80 5.202.85 3.20 3.54 3.70 3.96 4.16 4.44 4.56 4.80 5.302.98 3.30 3.54 3.70 4.05 4.20 4.47 4.68 4.90 5.433.04 3.39 3.57 3.75 4.08 4.20 4.47 4.70 5.00 3.10 3.42 3.60 3.78 4.10 4.30 4.47 4.71 5.10 3.10 3.48 3.60 3.83 4.14 4.30 4.50 4.78 5.10
Example 2: FEV1 (litres) of 57 male medical students
Mean: 4.06 Variance: 0.45
SD: 0.67 Q1: 3.54
Q2 (Median): 4.10 Q3: 4.52
Percentile 5.16 Range: 2.85 to 5.43
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2.5 3 3.5 4 4.5 5 5.5 60
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FEV1 (litre)
Fre
quen
cy
The Meaning of Standard Deviation
• How the data are dispersed around mean• Mean ± 1 SD represent 68.3% of the
population• Mean ± 2 SD represent 96% of the population• Mean ± 3 SD represent 99.7% of the
population
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-3 -2 -1 0 1 2 3
The Meaning of Standard Deviation
±SD % of Pop
1 68.3
1.96 95
2 95.5
2.58 99
3 99.71SD 1SD
34% 34%
2SD 48% 2SD 48%
Standard Error of Mean (SEM)
• How confident can we be that the sample mean represents the population mean µ?
• SEM=SD/– SEM must be much smaller than the SD
• mean ± 1.96*SD cover 95% of the data• mean ± 1.96*SEM cover 95% of the
population mean• SEM and SD are different!
n
Standard Error of Mean (SEM)
• Describing the scatter or spread of data, use SD• Estimate population parameters, use SEM
• Epidemiologic study, SEM• Clinical or laboratory research, SD
Summarizing Data - CalculatorPut DATA below:
Interval Size:
0102030405060708090
N:µ:σ:
Mean: 4.06 Variance: 0.45
SD: 0.67 Q1: 3.54
Q2 (Median): 4.10 Q3: 4.52
Percentile 5.16 Range: 2.85 to 5.43
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2.5
3
3.5
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4.5
5
5.5
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Ylim:
New Data
HISTOGRAM
POLYGON
1
Example Data
RUN
ReDraw
Box-Plot• The box itself contains the middle 50% of the
data. The upper edge (hinge) of the box indicates the 75th percentile of the data set, and the lower hinge indicates the 25th percentile. The range of the middle two quartiles is known as the inter-quartile range.
• The line in the box indicates the median value of the data.
• The + indicate mean value• The ends of the vertical lines or "whiskers"
indicate the minimum and maximum data values, unless outliers are present in which case the whiskers extend to a maximum of 1.5 times the inter-quartile range.
• The points outside the ends of the whiskers are outliers or suspected outliers. 0
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Box Plot – Example 2
• FEV1 of 57 students Serum triglyceride measurements in cord blood from 282 babies
What you can get from a box-plot?
• Graphically display a variable's location and spread at a glance. [Q1, Q2 (median), Q3, interquartile range]
• Provide some indication of the data's symmetry and skewness.
• Unlike many other methods of data display, boxplots show outliers.
• By using a boxplot for each categorical variable side-by-side on the same graph, one quickly can compare data sets.
• One drawback of boxplots is that they tend to emphasize the tails of a distribution, which are the least certain points in the data set. They also hide many of the details of the distribution. Displaying histogram in conjunction with the boxplot helps
Transformations
-2 -1.5 -1 -0.5 0 0.5 10
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log(triglyceride)
frequ
ency
0 0.5 1 1.5 20
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triglyceride
frequ
ency
LOG (triglyceride)triglyceride
Summarizing data
• Univariate – categorical variable– Frequency distributions– Bar Chart, Pie Chart
Summarizing data• Univariate – continuous variable
– Grouped frequency distributions– Polygon or histogram– Mean, Median, Mode, Percentile, Q1, Q2, Q3,
extreme values– Standard deviation, variance, range, interquartile
range– Box-Plot– Normality test statistics
Next lecture ( Lecture 2)
• Bivariate – one is categorical and the other is continuous variable– t-test– ANOVA
Lecture 3 – categorical data analysis
• Bivariate – both are categorical– Contingency tables– Chi-square test
• Response is categorical, predictors could be both types.– Logistical regression
Lecture 4 – Continuous response
• Correlation• Multiple linear regression
• Thanks.
• Question?