Agronomy4future

□ 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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Previous post!!□ Simple linear regression (1/5)- correlation and covariance□ Simple linear regression (2/5)- slope and intercept of linear regression model In my previous post, I explained how to calculate slope (β1) and intercept (β0) of linear regression model. If you well followed my previous posts, you will get the above result, y= 89.0 + 1.5x  Now our interest is how to calculate standard error in the intercept and slope (Red box). Here is the equation to obtain standard error of…

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□ Simple linear regression (1/5)- correlation and covariance In my previous post, I explained about correlation and covariance. Now, I’ll explain about slope (β1) and intercept (β0) of linear regression model. In the whole picture to explain a linear regression model, β1 is calculated as β1 = r * Sy / Sx We already know how to calculate correlation (r), and only we need to calculate the ratio between standard deviation of x and y. Let’s go back to the…

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Since today, I’ll explain simple linear regression model. There are lots of information about linear regression on websites, but I believe I’ll tell you about what most people don’t mention. My philosophy on data analysis and statistics is to fully understand the concept, not simply follow what software programs say. Therefore I usually calculate statistical concepts by hand, and only my hand calculation is exactly same as the software programs provide, I say I understand the concept. In this context,…

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Every time we discuss R2, we typically associate it with regression models. However, R2 also has a significant role in ANOVA. There seems to be less information available on how to calculate and interpret R2 in ANOVA, so today’s topic will focus on how to interpret this measure in the context of ANOVA. Let’s consider an example dataset. Suppose we measured the final yield at varying nitrogen levels. We established three replicates as a block. Consequently, this model will be…

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When you conduct PCA (Principal Components Analysis), do you simply accept the result which software programs provide? If we just accept results without any doubts, we never understand the principle of PCA. In this time, I’ll introduce how PCA is calculated step by step, and if you read this post, I believe you can fully understand the concept of PCA. Here is one data. Let’s say we measured kernel number per ear (KN), average of kernel weight per ear (KW)…

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