Agronomy4future

□ Quantifying Phenotypic Plasticity of Crops In my previous post, I explained how to quantify phenotypic plasticity in crops and described three different approaches: 1) Responsiveness, 2) Reaction Norm, and the 3) Finlay-Wilkinson Regression Model. Responsiveness is calculated as (Treatment − Control) / Control. It indicates how the dependent variable (e.g., yield) responds to a given treatment relative to the control. The responsiveness value can range from −1 (complete reduction) to values greater than 0, depending on the magnitude of…

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In my previous post, I explained how to calculate the Log-Likelihood, AIC, and BIC in a regression model. In this post, I will demonstrate the same concepts, but in the context of an ANOVA model. Here I have one dataset. Let’s say this data represents yield in response to different fertilizer types (Control, Slow, and Fast), and I want to determine the effect of fertilizer type on yield. Therefore, I will perform a one-way ANOVA. Now, I observe that the…

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Here I have one dataset. I want to predict grain weight using grain dimension data such as length, width, and area, and identify the best prediction model for estimating grain weight. As a result, I developed the following models. and I’ll calculate Log-likelihood for each model. To do that, I need to know each model equation. Now, I obtained each model equation, and I’ll calculate Log-likelihood For a linear regression model, the Log-Likelihood (LL) is defined as: where:n is the…

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Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction while preserving as much variability in the data as possible. It transforms the original variables in a dataset into a new set of uncorrelated variables called principal components, ordered by the amount of variance they capture from the original dataset. Here’s the step of Principal Component Analysis (PCA). 1. Standardize the Data: Since PCA is affected by the scale of the variables, it often begins with standardizing the…

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Mean Absolute Error (MAE) is a metric used to measure the accuracy of a model’s predictions. It calculates the average magnitude of the errors in a set of predictions, without considering their direction. In other words, MAE measures the average absolute difference between the actual values and the predicted values. MAE is typically used in the context of regression analysis and prediction error evaluation, rather than in ANOVA (Analysis of Variance), which focuses on comparing the means of different groups….

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최근에는 베이지안 통계에 집중하고 있습니다. 그래서 개념 정리도 할 겸 베이즈 정리 (Bayes’ theorem)에 대해 최대한 쉽게 한번 설명해 보겠습니다. Kaggle 에서 데이터를 하나 가져오겠습니다. https://www.kaggle.com/datasets/cameronseamons/electronic-sales-sep2023-sep2024 이 데이터는 전자 마트에서 고객의 소비 성향을 분석한 데이터 입니다. Kaggle 에서 회원가입 후 데이터를 다운로드 받을 수 있습니다. 저는 파이썬 코드로 바로 데이터를 가져오겠습니다. 참고로 저는 구글 코랩 (Google Colab) 을 사용합니다. 복잡해 보이지만 어려울게 하나도 없습니다. 아래 코드를 본인의 구글 코랩에 복사/붙여넣기 하고 본인 구글 코랩의 파일 경로만 수정하면 됩니다. 자 이렇게…

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□ Understanding Multiple Linear Regression Easily (Part 1: Calculating the Regression Equation Manually) In the previous post, we explained how to manually calculate the regression equation in multiple linear regression analysis. Now, in this post, I will explain how to calculate the coefficient of determination (R2) in multiple linear regression analysis. No. Yield (yi) Time (xi1) Moisture (xi2) 1 4.3 4 0.2 2 5.5 5 0.2 3 6.8 6 0.2 4 8.0 7 0.2 5 4.0 4 0.3 6 5.2…

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In my previous posts, I explained the simple linear regression model as five categories. I recommend reading the following posts first. □ Simple linear regression (1/5)- correlation and covariance□ Simple linear regression (2/5)- slope and intercept of linear regression model□ Simple linear regression (3/5)- standard error of slope and intercept□ Simple linear regression (4/5)- t value on the slope and intercept    □ Simple linear regression (5/5)- R_squared In this session, I will explain multiple regression analysis. Multiple regression analysis refers to…

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Today, I’ll try to explain factorial experiments in the simplest way. When you apply multiple different factors simultaneously to derive experimental results, it’s called factorial experiments. The different treatments within the experiment are referred to as ‘factorials.’ In other words, a factorial is a combination of factors. [Note 1] A factorial experiment is a research design in which multiple independent variables, also known as factors, are manipulated simultaneously to analyze their combined effects on a dependent variable. The goal of…

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Here is data for x and y. I would like to perform regression analysis to understand how y changes with x. n x y 1 10 30 2 20 40 3 30 50 4 40 80 5 50 90 6 60 100 7 70 120 I have data for x and y as described above, and want to determine the regression model for this data, where the dependent variable y changes according to the independent variable x, in the form…

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Odds and logit is the basic concept to understand logistic regression. Today I’ll explain what it is as much as easily. Do you know a comic book, ‘Slam Dunk’? I’ll explain odds with this story. 1) Odds Now, Shohoku high school is playing games with other high schools in the tournament. In the first round, Shohoku high school won 4 games and lost 6 games out of 10 games. Now the winning odds of Shohoku high school is 4/6 ≈…

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