Vectoronica

In this blog, i'm going to talk about overall test and individual tests. 1. Overall test : This is also referred to as the overall F-test of a multiple regression model. An overall test helps us decide whether predictors are related to the response in the population. 1-1) hypotheses : we also specify the null hypothesis when we conduct overall test. If there's no relation between the predictors ..

In this page, I'm going to talk about multiple regression and how to calculate R and R squared in multiple regression. 1. Multiple regression : you might guess what it means when you see the name. It doesn't have a big difference compared to linear regression. It just has more variables than simple linear regresison. It also means that as you consider more variables, you could get higher probabi..

this time, I'm going to talk about Confidence Interval and Prediction Interval for predicted values and Exponential regression. 1. CI and PI for predicted values : The underlying question to distinguish those two is, are we talking about the predicted mean score for the group of all cases in the population? or are we talking about the predicted score for an individual case? there are a couple of..

In this blog, I want to talk about testing the model and checking assumptions. 1. Testing the model : It's literally about how to test a regresison model. Fortunately, we're going to use the same test way as we mostly do for samples. When we test means for samples, we use the t test. Depending on what you want to test like one sided test or two sided test, you can choose one of them. And testing..

In this blog, I want to talk about the predictive power and the pitfalls of the linear regression. 1. Predictive power : It's related to the correlation coefficient. More specifically, It is used when you want to describe how much your sample approximate a regression line(the best fitting straight line). You can think of the predictive power as a relative ratio of the linear regression that you ..

In this blog, I'm going to talk about the regression equation and the regression model. 1. The regression equation : In simple linear regression, we assume the relation between the two quantitative variables is linear, so first of all, the line must be straight. Second, we get the best predictions from the line that produces predicted scores. So we need to find the straight line that minimizes t..

In this blog, i'm going to talk about Fisher's exact test and linear regression. 1. Fisher's exact test : When expected frequencies in a contingency table are small, you cannot use a chi-square test for independence. However, in case of a two by two table, there's an alternative test that is designed for small samples. It's called Fisher's exact test. 1-1) How to do Fisher's exact test? Fisher's..

1. Chi-suqared as goodness of fit : It means to compare an observed frequency distribution with a frequency distribution you expect on the basis of a theory. Or The Chi-square goodness of fit test is a statistical hypothesis test used to determine whether a variable is likely to come from a specified distribution or not. (The second definition comes from someone's blog. I searched it on Google b..