The document discusses predictive modeling and regression analysis using data. It explains that predictive modeling involves collecting data, creating a predictive model by fitting the model to the data, and then using the model to predict outcomes for new input data. Regression analysis specifically aims to model relationships between input and output variables in data to enable predicting outputs for new inputs. The document provides examples of using linear regression to predict exam scores from study hours, and explains that the goal in model fitting is to minimize the sum of squared errors between predicted and actual output values in the data.
The document discusses predictive modeling and regression analysis using data. It explains that predictive modeling involves collecting data, creating a predictive model by fitting the model to the data, and then using the model to predict outcomes for new input data. Regression analysis specifically aims to model relationships between input and output variables in data to enable predicting outputs for new inputs. The document provides examples of using linear regression to predict exam scores from study hours, and explains that the goal in model fitting is to minimize the sum of squared errors between predicted and actual output values in the data.
At Nagoya.R #10, I introduced basic nonparametric statistical methods, sign test and Wilcoxon signed-rank test. These slides show that they can also be used in some special cases such as test for contigency table.
Nagoya.R #10において、符号検定とウィルコクソンの符号付き順位和検定という、基本的なノンパラメトリックな統計手法について紹介した。スライドでは、これらの手法が例えば分割表に対する検定などのような、特別な場合においても利用することもできることを示した。
At Nagoya.R #10, I introduced basic nonparametric statistical methods, sign test and Wilcoxon signed-rank test. These slides show that they can also be used in some special cases such as test for contigency table.
Nagoya.R #10において、符号検定とウィルコクソンの符号付き順位和検定という、基本的なノンパラメトリックな統計手法について紹介した。スライドでは、これらの手法が例えば分割表に対する検定などのような、特別な場合においても利用することもできることを示した。