Course Overview
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Course Synopsis
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The purpose of this course will be to provide advanced undergraduate and graduate students a solid background in the core concepts of applied biological statistics. The course will review basic statistical concepts used in biological and medical research, probability theory, basic concepts of statistical inference, regression and correlation methods are covered, , the use of the software SPSS for data analysis.
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Course Learning Outcomes
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This course aims to enable you to apply advanced bio statistical methods to address research questions.
- • Analyze quantitative and qualitative data using biostatistics, • Define the principal concepts about biostatistics. • Collect data relating to variable/variables which will be examined and calculate descriptive statistics from these data. • Identify distribution form relating to the variable/variables. • Apply hypothesis testing via some of the statistical distributions.
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Course Calendar
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Week 01
1
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Introduction to Biostatistics
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7
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Measurement Scales - II
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Week 02
9
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Sampling and Statistical Inference
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10
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Basic Terminology for sampling
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11
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Types of Sampling Methods
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12
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Simple Random Sampling
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13
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Stratified Random Sampling
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Week 03
16
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Non-Probability Sampling Methods
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Quiz 1
18
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Describing Data with Tables - I
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19
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Describing Data with Tables - II
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Week 04
20
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Describing Data with Tables - III
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22
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Describing Data with Charts - I
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23
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Describing Data with Charts - II
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Week 05
24
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Describing Data with Charts - III
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Assignment 1
27
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Describing Data: From its Shapes
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28
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Describing Data: From its peak
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29
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Describing Shape: From Box and Whisker Plot
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30
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The Central Tendency of a data-set
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Week 06
31
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Measures of Dispersion
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32
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Mean Deviation, Standard Deviation and Variance & Coefficient of variation
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33
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Chebychev's Inequality, The Empirical Rule & The Five-Number Summary
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34
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Box and Whisker Plot, Pearson’s Coefficient of Skewness
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35
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Set Theory,Counting Rules
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36
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Permutations,Combinations
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Week 07
37
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Definitions of Probability
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38
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Application of Addition Theorem,Conditional Probability & Multiplication Theorem
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QUIZ 2
39
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Bayes’ Theorem,Discrete Random Variable
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40
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Graphical Representation of the Distribution Function of a Discrete Random Variable
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41
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Properties of Expected Values,Covariance & Correlation& Discrete Probability Distributions
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Week 08
42
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Binomial Distribution,Introduction to the Hypergeometric Distribution
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43
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Hypergeometric Distribution ,Poisson Distribution & Continuous Uniform Distribution
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44
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Appropriate Use of Measures of Location
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MID TERM EXAMS
45
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Quantiles (Cut Points)
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Week 09
47
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Measures of Dispersion.
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48
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Absolute Measures of Dispersion
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49
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Examples for Absolute Measures of Dispersion
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50
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Relative Measures of Dispersion
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51
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Examples of Measures of Dispersion
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52
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Appropriate usage of the Measures of Dispersion
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Week 10
53
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Calculating Skewness and making decision using Numerical measures
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54
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Calculating Kurtosis and making decision using Numerical measures
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58
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Bernoulli Distribution
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59
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Bernoulli Trials and Bernoulli Distribution
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Week 11
61
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Sampling Distribution of the sample mean
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62
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Expectated value and variance of the sampling distribution of the sample mean
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Week 12
ASSIGNMENT 2
67
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Assumptions used for Correlation
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68
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Types of correlation and their usage
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Week 13
71
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Calculating and interpreting Correlation
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72
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Testing Hypothesis about Correlation coefficient
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74
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Assumptions for Regression Model
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75
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Steps in Regression Analysis
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QUIZ 03
76
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A Tutorial to SPSS - I
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77
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A Tutorial to SPSS - II
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Week 14
78
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A Tutorial to SPSS - III
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79
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A Tutorial to SPSS - IV
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80
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A Tutorial to SPSS - V
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81
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A Tutorial to SPSS - VI
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82
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A Tutorial to SPSS - VII
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83
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A Tutorial to SPSS - VIII
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Week 15
84
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A Tutorial to SPSS - IX
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85
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A Tutorial to SPSS - X
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86
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A Tutorial to SPSS - XI
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87
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A Tutorial to SPSS - XII
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88
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A Tutorial to SPSS - XIII
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FINAL TERM EXAM
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