tag:blogger.com,1999:blog-7958828565254404797.post5116849402452216087..comments2019-10-22T06:55:03.328-07:00Comments on ListenData: 15 Types of Regression in Data ScienceDeepanshu Bhallahttp://www.blogger.com/profile/09802839558125192674noreply@blogger.comBlogger18125tag:blogger.com,1999:blog-7958828565254404797.post-53210057042037540002019-06-10T12:43:25.611-07:002019-06-10T12:43:25.611-07:00I have added it into this article. Thanks!I have added it into this article. Thanks!Deepanshu Bhallahttps://www.blogger.com/profile/09802839558125192674noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-83969286573181448152019-06-10T10:48:14.194-07:002019-06-10T10:48:14.194-07:00What is a Tobit regression model?What is a Tobit regression model?Unknownhttps://www.blogger.com/profile/15588539524373982172noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-8692813777197231782019-04-03T09:30:15.564-07:002019-04-03T09:30:15.564-07:00Hello, I used a Likert scale in a questionnaire an...Hello, I used a Likert scale in a questionnaire and run a model where the dependent variable is the value of the answer. Using an ordinal regression model, 2 or 3 categories are "underranked". So my model results weak. Do you have any sugestion? Actually I could sum the value of the answers value for each interviewee obtaining a result from 3 to 15. What kind of model could I use in this case?Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-27184673778628344572018-06-15T03:49:54.864-07:002018-06-15T03:49:54.864-07:00Dependent variable should be continuous in nature....Dependent variable should be continuous in nature.Deepanshu Bhallahttps://www.blogger.com/profile/09802839558125192674noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-74319292132762079082018-06-13T07:45:38.386-07:002018-06-13T07:45:38.386-07:00Hello,
Can you please post some resources about h...Hello,<br /><br />Can you please post some resources about how to deal with interactions in Regression using R? You have listed all kinds of regression models here. It would be great if you could cover Interactions and suggest how to interpret them. Maybe touching upon continuous, categorical, count and multilevel models. And giving some examples of real world data. <br /><br />Is that possible?<br /><br />Thanks,<br />KunalKDhttps://www.blogger.com/profile/14777390137440623592noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-64213769502906365072018-05-14T14:25:50.152-07:002018-05-14T14:25:50.152-07:00For what type of dependent data, support vector re...For what type of dependent data, support vector regression is applicable? Is it applicable for the case when dependent variable is discrete and bounded?Saleha Khatunhttps://www.blogger.com/profile/13283198935699250635noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-27512511452969219682018-04-23T15:07:58.465-07:002018-04-23T15:07:58.465-07:00In the elastic net regression I think there is a t...In the elastic net regression I think there is a typo. There should be a + sign in between first and second terms of the equation on RHSvaibhav khandelwalhttps://www.blogger.com/profile/06217427922432170691noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-56782424794175565602018-04-14T04:07:07.507-07:002018-04-14T04:07:07.507-07:00Hi, very good article yet there is a details you m...Hi, very good article yet there is a details you may correct if you want. The polynomial regression you are describing it is still a linear regression because the dependent variable, y, depend linearly on the regression coefficients. The fact the y is not linear versus x does not matter. From the practical point of view it means that with GNU R you can still use the "lm" function like in lm(y ~ x^2) and it will work as expected. The matrix computation of the linear regression and the matrix X is also still valid.francescohttps://www.blogger.com/profile/15190773349378936179noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-71137165508778313942018-04-13T11:58:31.109-07:002018-04-13T11:58:31.109-07:00I am not sure if I understand right. For quantile ...I am not sure if I understand right. For quantile regression the objective function is<br />q\sum | \eps_i | + (1-q) \sum | \eps_i | = \sum | \eps_i |.<br /><br />Is this equation correct?cnxhttps://www.blogger.com/profile/12614847138980399016noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-47106261954848392222018-03-30T06:02:29.762-07:002018-03-30T06:02:29.762-07:00There is something a bit off with the definition h...There is something a bit off with the definition here which you mentioned this and please correct me if I am wrong;<br />U said these<br /><br />When we use unnecessary explanatory variables it might lead to overfitting. Overfitting means that our algorithm works well on the training set but is unable to perform better on the test sets. It is also known as problem of high variance.<br /><br />When our algorithm works so poorly that it is unable to fit even training set well then it is said to underfit the data. It is also known as problem of high bias.<br /><br />But I think when we overfit covariates into our models we would end up with a perfect model for the training data as you minimize the MSE which then also increases your bias towards the model which then increase the test MSE if you are able to test it using testing data<br /><br />In my field of medical world I cannot do this training data usually cos it does not make senseHShttps://www.blogger.com/profile/05867497733002311952noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-42810137030814102382018-03-27T08:52:23.208-07:002018-03-27T08:52:23.208-07:00excellent, very helpful, thank you.
Was there a ...excellent, very helpful, thank you. <br /><br />Was there a reason that multinomial logistical regression was left out? Unknownhttps://www.blogger.com/profile/02747698425224689827noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-55605134757397187852018-03-26T12:11:37.291-07:002018-03-26T12:11:37.291-07:00This is great! I appreciate you explaining only wh...This is great! I appreciate you explaining only what's necessary to inform a choice, but not defining all technical terms. I can look those up if I think a model's worth considering.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-32549263352431779882018-03-25T22:40:25.954-07:002018-03-25T22:40:25.954-07:00Load survival package and then run command data(lu...Load survival package and then run command data(lung) Deepanshu Bhallahttps://www.blogger.com/profile/09802839558125192674noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-57351058663107500612018-03-25T22:33:10.493-07:002018-03-25T22:33:10.493-07:00Typo. Corrected! Thanks for highlighting. Cheers! ...Typo. Corrected! Thanks for highlighting. Cheers! Deepanshu Bhallahttps://www.blogger.com/profile/09802839558125192674noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-29611594769645109192018-03-25T19:45:08.897-07:002018-03-25T19:45:08.897-07:00What is the data set for Lung Cancer?What is the data set for Lung Cancer?jbhttps://www.blogger.com/profile/05204348906705216252noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-35417329945521156142018-03-25T19:27:21.394-07:002018-03-25T19:27:21.394-07:00The piece is very good but some few regressions ar...The piece is very good but some few regressions are left out. Beta regression, probit regression, tobit regression and probably a few others. For probit and tobit, it is just good to extend the treatise on logistic regression and try to explain their differences and when it might be preferable to use probit or tobit rather than logit. I have read a document where someone was trying to diffentiate between logistic regression and logit. I could not get the difference really, is there any at all? The comment by Vsoch is really important to correct.Job Nmaduhttps://www.blogger.com/profile/03040885039833168972noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-22270126749382802442018-03-25T17:42:10.183-07:002018-03-25T17:42:10.183-07:00For clarity, I recommend changing "independen...For clarity, I recommend changing "independent variables" to "regressors" or "explanatory variables" in this statement: “Elastic Net regression is preferred over both ridge and lasso regression when one is dealing with highly correlated independent variables”<br /><br />Reading "highly correlated independent variables" was initially confusing.Jared Smithhttps://www.blogger.com/profile/08388467102036294853noreply@blogger.comtag:blogger.com,1999:blog-7958828565254404797.post-45505708022956000292018-03-25T17:27:00.366-07:002018-03-25T17:27:00.366-07:00Should the part where you say "When you have ...Should the part where you say "When you have more than 1 independent variable and 1 dependent variable, it is called simple linear regression" be multiple linear regression? It would be good to clarify because it comes right after "When you have only 1 independent variable and 1 dependent variable, it is called simple linear regression" and as a reader I would expect a contrast between the two blocks. Or if this is correct, a statement to validate that it is right after.VSochhttps://www.blogger.com/profile/18162428909429146973noreply@blogger.com