Course Overview
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Course Synopsis
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This course will provide researchers with the practical skills to use to cutting-edge methods in Epidemiology and Biostatistics. They will be introduced to the concept of causal inference using observational data. Thestudents will be able to correctly select the appropriate statistical analytical method to address specific research questions, conduct the selected statistical analysis using SAS software for statistical analysis, present and interpret the results appropriately and draw valid and insightful conclusions.
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Course Learning Outcomes
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This course aims to enable you to apply advanced biostatistical methods to address research questions.
- Determine the appropriate statistical analytical technique for different epidemiological study designs and datasets
- Conduct statistical analysis using advanced techniques on complex datasets with different types of variables.
- Correctly interpret results and draw valid conclusions addressing the research question.
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Course Calendar
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1
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Introduction to Biostatistics
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7
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Measurement Scales - II
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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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16
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Non-Probability Sampling Methods
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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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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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24
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Describing Data with Charts - III
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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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Descriptive Statistics: Measures of Central Tendency
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34
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Appropriate Use of Measures of Location
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35
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Quantiles (Cut Points)
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37
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Measures of Dispersion
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38
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Absolute Measures of Dispersion
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39
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Examples for Absolute Measures of Dispersion
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40
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Relative Measures of Dispersion
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41
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Examples of Measures of Dispersion
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42
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Appropriate usage of the Measures of Dispersion
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43
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Calculating Skewness and making decision using Numerical measures
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44
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Calculating Kurtosis and making decision using Numerical measures
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48
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Probability: Introduction
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49
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Probability: Basic Terminology - I
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50
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Probability: Basic Terminology - II
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51
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Probability: Axioms of Probability
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52
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Probability: Additive rule
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53
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Probability: Multiplicative Rule for Independent Events
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54
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Conditional Probability
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55
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Bernoulli Distribution I
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56
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Bernoulli Distribution II
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57
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Bernoulli Distribution III
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58
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Bernoulli Distribution IV
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59
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Bernoulli Distribution V
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60
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Continuous Probability Distribution I
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61
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Continuous Probability Distribution II
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62
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Continuous Probability Distribution III
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63
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Continuous Probability Distribution IV
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64
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Continuous Probability Distribution V
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65
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Continuous Probability Distribution VI
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66
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Continuous Probability Distribution VII
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67
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Continuous Probability Distribution VIII
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68
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Introduction to Sampling
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69
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Introduction to Sampling I
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70
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Introduction to Sampling II
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71
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Introduction to Sampling IV
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72
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Stratified Random Sampling.
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73
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Stratified Random Sampling I
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74
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Stratified Random Sampling II
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75
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Stratified Random Sampling III
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76
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Stratified Random Sampling IV
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78
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Sampling Distribution of the sample mean
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79
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Expectated value and variance of the sampling distribution of the sample mean
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80
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Sampling Distribution of the differnece between two means
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81
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Expected value and variance of the sampling distribution of the difference between two sample means
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82
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Sampling Distribution of a sample Proportion
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83
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Expected value and variance of the sampling distribution of a sample proportion
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84
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Sampling distribution of the difference of the two sample proportion
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88
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Using Tables and MS Excel to obtain probability
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89
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Confidence Interval for Single Mean
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90
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Confidence Interval for the difference between Two population Means
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91
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Confidence Interval for the Single Proportion
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92
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Confidence Interval for the Difference of two proportions
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93
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A Tutorial to SPSS - I
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94
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A Tutorial to SPSS - II
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95
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A Tutorial to SPSS - III
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96
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A Tutorial to SPSS - IV
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97
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A Tutorial to SPSS - V
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98
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A Tutorial to SPSS - VI
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99
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A Tutorial to SPSS - VII
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100
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A Tutorial to SPSS - VIII
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101
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A Tutorial to SPSS - IX
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102
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A Tutorial to SPSS - X
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103
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A Tutorial to SPSS - XI
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104
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A Tutorial to SPSS - XII
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105
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A Tutorial to SPSS - XIII
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106
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Using SPSS to report Descriptive Statistics
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107
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Basic Concepts of Hypothesis Testing
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113
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Assumptions for Testing of Hypothesis
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114
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Flow Chart for Hypothesis Testing
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115
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Testing Normality Assumption
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116
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Testing Hypothesis for Single Mean - I
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117
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Testing Hypothesis for Single Mean - II
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118
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Testing Hypothesis for Single Mean - III
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119
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Testing Hypothesis for Single Mean - IV
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120
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Testing Hypothesis for Single Mean
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121
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Testing Difference of Means in two independent samples
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122
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Testing Difference of Means in two independent samples (Equal Variances Assuming)
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123
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Testing Difference of Means in two independent samples (UnEqual Variances Assuming)
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124
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Testing Difference of Means in two independent samples(non-Parametric)-I
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125
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Testing Difference of Means in two independent samples (non-Parametric) - II
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126
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Testing Difference of Means in Two dependent samples - I
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127
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Testing Difference of Means in Two dependent samples - II
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128
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Testing Difference of Means in two dependent samples - III
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129
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Testing Difference of Means in two dependent samples - IV
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131
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Testing Assumptions of ANOVA
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133
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Multiple Comparison Tests
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134
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Types of Multiple Comparison Test
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135
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Performing and Interpreting Multiple Comparison Tests
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136
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Bonferroni Correction
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137
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Kruskal Wallis Test - I
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138
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Kruskal Wallis Test - II
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140
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Discussion of dataset for Two-way ANOVA
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141
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Performing and Interpreting Two-Way ANOVA
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142
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Multiple Comparison Tests in Two-Way ANOVA
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143
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Performing and Interpreting Multiple Comparison tests for Two-Way ANOVA
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144
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Testing Hypothesis for Single Proportion
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145
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Testing Hypothesis for Two Proportions
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146
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Testing Hypothesis for More than Two Proportions
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147
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Contingency table analysis
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148
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Fisher's Exact Test - I
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149
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Fisher's Exact Test - II
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151
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Tests for Goodness of fit - I
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152
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Tests for Goodness of fit - II
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153
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Measuring Relative Risk
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155
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Comparing Odds Ratio and Relative Risk
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157
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Measuring Mortality 1
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159
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Assumptions used for Correlation
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160
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Types of correlation and their usage
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163
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Calculating and interpeting Correlation
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164
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Testing Hypothesis about Correlation coefficient
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166
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Assumptions for Regression Model
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167
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Steps in Regression Analysis
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168
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Understanding Dependence relationship using Scatter Plots
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169
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Principle of Leasst Squares
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171
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Obtaining a regression Line using SPSS
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173
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Decision making Using ANOVA in regression
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174
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Calculating and Interpreting results for ANOVA in regression usign SPSS
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175
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Evaluating a regression Equation
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176
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Confidence intervals for individual parameters
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177
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Coefficient of Determination
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178
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Making Prediction using regression Model
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179
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Testing the regression Assumptions
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180
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Multiple Linear Regression Model
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181
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Obtaining and Interpreting a multiple linear regression equation
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182
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Detecting Outliers, Influential and leverage values
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183
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SPSS tutorial to Multiple regression
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184
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Regression with Qualitative Independent variable
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185
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Performing and Interpreting Regression with Qualitative Independent Variable
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186
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Variable Selection procedures
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190
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An SPSS Tutorial to Variable selection procedures
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191
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Introduction to Survival Analysis
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192
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Survival Analysis: Basic Terminology
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198
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SPSS tutorial to model building
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199
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SPSS Tutorial to Assumption testing
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200
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SPSS Tutorial to variable Selection
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