1. Sumukh Deshpande n Lecturer College of Applied Medical Sciences
BIOSTATISTICS (BST 211)
Sumukh Deshpande n Lecturer College of Applied Medical Sciences
Lecture 1
Statistics = Skills for life.

2. Course Overview
Wide range of statistical topics, without too much details of the maths. You should be a GOOD user of stats tables and techniques.
You will learn how to use calculators and spreadsheets to perform statistical analysis.

3. Course Contents in Brief & Outcomes
Data Handling: Types of Data, Displaying Data, Descriptive Stats: Average & Spread, Common Distributions
Sampling: How to chose a sample? Confidence Intervals
Hypothesis Testing: Null and Alternative Hypothesis, Errors in Hypothesis Testing
Basic Techniques: Numerical & Categorical Data, Regression & Correlation,
Practice: Assignments and practical applications

4. Textbook Medical Statistics at a Glance, 3rd Edition
Aviva Petrie (University of London Eastman Dental Institute and London School of Hygiene and Tropical Medicine ),
 Caroline Sabin (University College London Medical School )
ISBN:
Paperback
180 pages
July 2009, ©2009, Wiley-Blackwell
Please BUY YOUR PERSONAL COPY.
One single copy is available at the Medical Library

5. Additional Textbook
Practical Statistics for Medical Research (Chapman & Hall/CRC) 
Douglas G. Altman
ISBN-10:   | ISBN-13: 

6. Calculator
Please BUY YOUR PERSONAL CALCULATOR
CASIO fx ms 85

7. How Can You help Yourself?
Important: We will NOT or give handouts. These, and additional learning material, will be posted on the course SkyDrive site. You must visit the site regularly. You are also encouraged to make your own notes and refer to textbooks.
Statistics is PRACTICE
You must take notes and ASK QUESTIONS!
Always bring your calculator and repeat class practice.
Try further exercises and see me to discuss your work

8. Check your uoh
You must check that your university is working. Your uoh is:
where nnnnnnnnn is your student number
Go to
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Then go to SkyDrive to login and/or register skydrive.live.com Logon using your full uoh and password

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Click on Student Login
Follow the instructions
For help contact ITC
Or call User Services Unit(USU) on ,

10. SkyDrive Screen Login using your uoh email and password.
If you have any problem try changing your password and login again
For help contact ITC or USU

11. LECTURE 1 Types of Data & Descriptive Statistics
BIOSTATISTICS
LECTURE 1 Types of Data & Descriptive Statistics

12. 2 branches of STATISTICS
Descriptive Inferential
Data known Data unknown
Direct measure Indirect ESTIMATE
Small groups Large populations Prediction & Decision

13. variables Keywords Population Sample Variable Data Frequency
Nominal: Blood group, City, marital status,..
Ordinal: strong/moderate/mild
Interval: Temperature
Ratio: Height, Weight
Categorical
Numerical

14. Nominal data 1
Nominal or categorical data is data that comprises of categories that cannot be rank ordered – values fall into unordered categories or classes.
The categories available cannot be placed in any order and no judgement can be made about the relative size or distance from one category to another.
What does this mean? No mathematical operations can be performed on the data relative to each other.
Therefore, nominal data reflect qualitative differences rather than quantitative ones.

15. Nominal data 2 Examples: What is your gender? (please tick) Male
Female
Marital Status ? (please tick)
Married
Unmarried

16. Ordinal data 1
Ordinal data is data that comprises of categories that can be rank ordered.
Similarly with nominal data the distance between each category cannot be calculated but the categories can be ranked above or below each other.
What does this mean? Can make statistical judgements and perform limited maths.
Ordinal data works with the same assumptions as nominal data but is more complex in that here, whilst still comprising of categories, now the categories
themselves can be rank-ordered i.e. one category has more or less of some underlying quality than being in another category.
What does this mean? We can make statistical judgements about the relative position of one value to another and can perform limited mathematical calculations.

17. Ordinal data 2 Example: Level of injury? (please tick) Fatal injury
Severe injury
Moderate injury
Minor injury
Classification of patient performance status? (please tick)
Patient fully active
Patient restricted in physically strenuous activity
Patient ambulatory and capable of self-care
Patient of limited self-care
Patient disabled

18. Interval and ratio data
Both interval and ratio data are examples of scale data.
Scale data:
data is in numeric format (SR 50, 100 ml, 15.2 cm)
data that can be measured on a continuous scale
the distance between each can be observed and as a result measured
the data can be placed in rank order.
Ordinal data can be ranked – similarly interval and ratio data also operate on a mechanism of scale. The key difference is that with interval and ratio data the data is numerical and has numerically equal distances between each category. For example, a 5cm difference in height between 150cm and 155cm is the same as a 5cm difference between 15cm and 20cm.
Imagine a 30cm ruler – it shows equal distance between each mark i.e. 10 1mm markers between each cm.

19. Interval data
Interval data measured on a continuous scale and has no true zero point.
Examples:
Time – moves along a continuous measure or seconds, minutes and so on and is without a zero point of time.
Temperature – moves along a continuous measure of degrees and is without a true zero.
Temperature and true zero – whilst there is a zero degrees this is a measure of temperature!

20. Ratio data
Ratio data measured on a continuous scale and does have a true zero point.
Examples:
Age
Weight
Height

21. Types of Data Interval & Ratio Variables Numerical Categorical
Nominal
Ordinal
Discrete
(counting)
Continuous
(measuring)
Interval & Ratio

22. Give 2 examples for Each type of Data…… ……

23. Descriptive Statistics
Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data.
2 types of statistics used to describe data:
Measures of central tendency
Measures of spread

24. Descriptive Statistics
Measures of Central Tendency (Centrality):
Mean
Median
Mode
Percentiles
Measures of spread (Variability):
Variance
Standard Deviation
Standard Error
Range

25. Centrality Mean Median Mode
Sum of observed values divided by number of observations
Most common measure of centrality
Most informative when data follow normal distribution (bell-shaped curve)
Median
“middle” value: half of all observed values are smaller, half are larger
Best centrality measure when data are skewed
Mode
Most frequently observed value

26. Centrality- Median & Mode
“middle” value: half of all observed values are smaller, half are larger
Best centrality measure when data are skewed
Mode
Most frequently observed value
If one mode only- UNIMODAL
More than one mode- MULTIMODAL

27. Centrality – Mean Aka AVERAGE Mean
Sum of observed values divided by number of observations
Most common measure of centrality
Most informative when data follow normal distribution (bell-shaped curve)
Aka AVERAGE

28. Practice 1 on Centrality
Group 1 data: 1,1,1,2,3,3,5,8,20
Work out the mean, median and mode?
Mean: Median: Mode: 1
Group 2 data: 1,1,1,2,3,3,5,8,10
Work out the mean, median and mode?
Mean: Median: Mode: 1

29. Variability – Range
Range is defined simply as the difference between the maximum and minimum observations
Range = Max – min
Compute the Range for the values in grams:
423, 367, 320, 471, 480
Range = 480 – 320 = 160 grams

30. Percentiles
What are percentiles ?
Suppose we arrange the data starting from smallest to largest
The value of x that has 1% of the observations lying below it and 99% lying above it is called the first percentile
1, 2, 3, ………100 Here 2 is the first percentile, 3 is called second percentile and so on

31. Quartiles
Values of x that divides the ordered set into 4 equally sized groups i.e. 25th, 50th and 75th percentiles are called quartiles
Example:
5, 8, 4, 4, 6, 3, 8
Sort them in order:
3, 4, 4, 5, 6, 8, 8
Cut the list into quarters:
Quartile 1 (25th)= 4
Quartile 2 (Median or 50th) = 5
Quartile 3 (75th) = 8

32. Variability – Interquartile Range (IQR)
Interquartile Range is defined simply as the difference between the upper quartile and lower quartile
IQR = Upper Quartile – Lower Quartile
Upper Quartile =
Lower Quartile =

33. Interquartile Range (IQR) practice example
Compute the IQR for the following set of data:
4, 7, 10, 20, 1, 6, 8, 11
Order data: 1, 4, 6, 7, 8, 10, 11, 20
Upper Quartile = 3 (8+1)/4 = 7th Value = 11
Lower Quartile = 8+1/4 = 2nd Value = 4
IQR = 11 – 4 = 7

34. Variability – SD and Variance
Most commonly used measure to describe variability is standard deviation (SD)
SD is a function of the squared differences of each observation from the mean
SD is the square root of variance
Standard Deviation
Variance

35. Variability - Coefficient of Variation
The coefficient of variation relates the standard deviation of a set of values to its mean.
It is most useful for comparing two or more sets of data.
It is used to evaluate the relative variation between any two sets of observation

36. Practice on Group 1 Data Group 1 data: 1,1,1,2,3,3,5,8,20 Mean: 4.67
What is the SD and COV? x 1 2 3 5 8 20 f 3 1 2
x-xbar
-3.67
-2.67
-1.67
0.33
3.33
15.33
(x-xbar)^2
13.47
7.13
2.79
0.11
11.09
235.01
f(x-xbar)^2
40.41
7.13
5.58
0.11
11.09
235.01
Then, (299.33/8) = 37.41
SD = 37.41 = 6.11
Total
COV = (6.11/4.67)*100% = %

37. Summary Types of Data: NOIR Centrality: Mean, Median, Mode
Variability: SD, Variance, Range, IQR, Coefficient of Variance
Descriptive stats: patterns in data, outliers, chose the right test
Inferential stats: probability of a conclusion, drawn from a sample, is true,