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mastering-regression-analysis-from-linear-to-nonlinear-models

Mastering Regression Analysis: From Linear to Nonlinear Models

Leave a Comment / Applied Statistics /

Introduction: Regression analysis is a fundamental statistical technique used in various fields, from economics to machine learning, for modelling and predicting relationships between variables. In this article, we’ll explore the diverse world of regression analysis, covering linear regression models, nonlinear regression, polynomial models, multiple regression analysis, and the art of predictive modelling using regression. Linear […]

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Feature selection techniques in regression analysis

Leave a Comment / Data Analysis, Data Science, Econometrics, Statstics /

Feature selection techniques in regression analysis aim to identify the most relevant and informative subset of features (variables) to improve model performance, reduce overfitting, and enhance interpretability. Here are five popular feature selection techniques for regression analysis: Recursive Feature Elimination (RFE): RFE is an iterative technique that starts with all features and gradually eliminates the

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

Leave a Comment / Statstics /

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

Leave a Comment / Statstics /

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

Leave a Comment / Statstics /

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

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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Checking assumptions in Linear Regression

Leave a Comment / Statstics /

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

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