[amp_mcq option1=”dividing ssr by sst” option2=”dividing sst by ssr” option3=”dividing sst by sse” option4=”none of the above” correct=”option1″]<\/p>\n
The correct answer is: A. dividing ssr by sst.<\/p>\n
The multiple coefficient of determination, denoted by $R^2$, is a measure of how well the data fit the model. It is calculated by dividing the sum of squares of the residuals (SSR) by the total sum of squares (SST). The closer $R^2$ is to 1, the better the model fits the data.<\/p>\n
The sum of squares of the residuals is the sum of the squared distances between the observed values and the predicted values. The total sum of squares is the sum of the squared distances between the observed values and the mean of the data.<\/p>\n
Option B is incorrect because it divides SST by SSR. This would give a measure of how much of the variation in the data is explained by the model, but it would not be a measure of how well the model fits the data.<\/p>\n
Option C is incorrect because it divides SST by SSE. This would give a measure of how much of the variation in the data is not explained by the model, but it would not be a measure of how well the model fits the data.<\/p>\n
Option D is incorrect because it is not a valid way to calculate the multiple coefficient of determination.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”dividing ssr by sst” option2=”dividing sst by ssr” option3=”dividing sst by sse” option4=”none of the above” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-46891","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n