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

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

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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Rank Correlation – Definition, Real-Life Example, Formula

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Definition Spearman’s rank correlation coefficient is calculated as:     where is the difference between the ranks of corresponding data points and is the number of data points. Real-Life Example Sure, let’s create a hypothetical example to illustrate the concept of Spearman’s rank correlation coefficient. In this example, we’ll consider a small dataset of individuals’

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Correlation Coefficients and Bivariate data analysis

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Introduction Bivariate data analysis is a fundamental aspect of statistics that involves studying the relationship between two variables. It provides insights into how changes in one variable are associated with changes in another. In this article, we will explore the concepts of bivariate data, scatter diagrams, correlation coefficients, and rank correlation. Bivariate Data Bivariate data

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Moments – An Important Statistical Concept for Data Science

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Introduction Statistical moments are essential statistical descriptors that provide insights into the characteristics of a probability distribution. They are used to quantify the shape, central tendency, and dispersion of a data set. In this section, we will discuss moments, absolute moments, factorial moments, skewness, kurtosis, and Sheppard’s corrections. Moments In probability theory and statistics, the

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Mean, Median, Mode – Definition, Types, Applications, and Drawbacks

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Mean  In the realm of statistics, the “mean” holds a prominent place as a fundamental measure of central tendency. It is widely used to describe a set of data by providing a single representative value around which the data points tend to cluster. The mean is a crucial concept that enables statisticians, researchers, and analysts

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Word Clouds: A Visual Representation of Textual Data in Python

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Word clouds, also known as tag clouds or text clouds, are graphical representations of text data where words are displayed in varying sizes, with the size of each word indicating its frequency of occurrence in the given text. This popular visualization technique provides an immediate and intuitive overview of the most prominent words within a

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Checking assumptions in Linear Regression

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Linear regression is a powerful statistical method used to model the relationship between a dependent variable and one or more independent variables. To derive accurate and meaningful insights from the analysis, it’s important to understand and validate the assumptions that underlie this technique. In this article, we’ll delve into the key assumptions in linear regression

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Understanding the Difference Between Difference-in-Differences and Propensity Score Matching: A Comparative Overview

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

In the realm of causal inference and program evaluation, two widely employed methodologies stand out: Difference-in-Differences (DID) and Propensity Score Matching (PSM). These techniques aim to uncover causal relationships from observational data, but they operate on different principles and assumptions. Let’s delve into the key aspects that differentiate DID and PSM, and explore when each

Understanding the Difference Between Difference-in-Differences and Propensity Score Matching: A Comparative Overview Read More »

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