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Linear Regression: Understanding Linear Relationships, Assumptions and Interpretation, Practical Examples in Advanced Biostatistics

Leave a Comment / Biostatistics, Public Health, Statistics /

1. Understanding Linear Relationships: Definition: Linear regression is a statistical method used to model the relationship between a dependent variable (Y) and one or more independent variables (X) by fitting a linear equation (a straight line) to the data. Understanding Linear Relationships: In a linear regression model, the relationship between the dependent variable and the […]

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Methods of Regression: Overview of Different Regression Methods, Use Cases, and Applicability in Advanced Biostatistics

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Methods of Regression: Overview of Different Regression Methods, Use Cases, and Applicability Regression analysis encompasses a variety of methods that can be applied depending on the characteristics of the data and the research objectives. Below is an overview of different regression methods, along with their common use cases and applicability: 1. Linear Regression: Overview: Linear

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z-test: Definition, Assumptions, Advantages, Disadvantages, and Real-Life Example

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Definition: The z-test is a statistical hypothesis test used to assess whether the means of two populations are significantly different when the population standard deviation is known. It’s particularly suitable for large sample sizes. The test statistic follows a standard normal distribution (z-distribution), and it’s calculated by comparing the difference between sample means to the

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t-test – Definition, Assumptions, Advantages, Disadvantages, Application, and Real-life Example

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Definition The t-test is a statistical method used to compare the means of two groups and determine if the observed differences between them are statistically significant. It assesses whether the means of the two groups are different enough to conclude that the observed difference is not due to random chance. The t-test is calculated as:

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F-test: Definition, Assumptions, Advantages, Disadvantages, Application, and Real-Life Example

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Definition The F-test is a statistical test used to compare the variances of two or more samples. It is based on the F-distribution, which arises when the ratio of two independent chi-squared distributions (each divided by its degrees of freedom) follows an F-distribution. The F-test is often used to determine if the variability within groups

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Monitoring Information System

Leave a Comment / Monitoring Evaluation & Learning (MEL) /

Definition: A Monitoring Information System (MIS) is a structured framework that enables organizations to collect, process, store, and analyze data related to their operations, performance, and other relevant factors. It provides a comprehensive view of an organization’s activities, helping decision-makers monitor key metrics and trends in real-time or near-real-time, facilitating informed decision-making and strategic planning.

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Statistical Approaches for Evaluating Intervention Programs: Methods and Applications

Leave a Comment / Monitoring Evaluation & Learning (MEL) /

Introduction: Intervention programs are designed to bring about positive changes in specific target populations, whether they aim to improve health outcomes, enhance educational attainment, or address socioeconomic disparities. Evaluating the effectiveness of these intervention programs is crucial to determine their impact and guide future policy decisions. In this article, we delve into various statistical approaches

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Evaluation at Different Points in a Study: Baseline, Mid-Point, Concurrent, and End-Line Evaluation

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Evaluation plays a crucial role in assessing the effectiveness and progress of interventions, programs, or studies. Various evaluation points, such as baseline, mid-point, concurrent, and end-line evaluations, provide valuable insights into changes over time. Let’s delve into each of these evaluation points, along with real-life examples and mathematical definitions. Baseline Evaluation: Baseline evaluation is conducted

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Explore how to effectively use Descriptive statistics in your analysis

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Descriptive statistics are used to summarize and describe the main features of a dataset. Depending on the type of data and the specific research or analysis objectives, different descriptive statistics can be employed. Here are some common descriptive statistics and where to use them: Mean The arithmetic average of a set of values. Use the

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R & Python: Public Health Data Analysis

Leave a Comment / Data Analysis, Data Science, Public Health /

Are you passionate about leveraging data-driven insights to make a positive impact on global health? Do you thrive on using cutting-edge tools to address public health challenges?If yes, then this post is for YOU! In the fast-evolving world of public health, data analysis is a key driver for informed decision-making and policy formulation. 💡 R

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