tag:blogger.com,1999:blog-5964889903484807623.post4205340247953446760..comments2023-12-18T04:44:25.358-08:00Comments on Questions?: The DataDavid Coxhttp://www.blogger.com/profile/06277427735527075341noreply@blogger.comBlogger4125tag:blogger.com,1999:blog-5964889903484807623.post-42285119684333739702013-07-24T11:43:58.040-07:002013-07-24T11:43:58.040-07:00I love the idea of this post, but I couldn't g...I love the idea of this post, but I couldn't get the applet to run in Safari or Chrome (my Java is updated). Not that you have time to trouble shoot, but what does the applet show and could you send the file my way?Personal Financehttps://www.blogger.com/profile/11116223612030882137noreply@blogger.comtag:blogger.com,1999:blog-5964889903484807623.post-40908794012866490792013-07-13T05:40:42.658-07:002013-07-13T05:40:42.658-07:00Hi David,
Awesome blog!
I have nominated you for a...Hi David,<br />Awesome blog!<br />I have nominated you for a Liebster Award. The award is a way to recognize bloggers, offer encouragement, and possibly get more readers. Check out my blog post at http://fliplearnshare.blogspot.com/2013/07/an-awesome-surprise.html<br /><br />Play along if you want to! I appreciate your work online!<br />Mrs.Nehilahttps://www.blogger.com/profile/13446790876342568219noreply@blogger.comtag:blogger.com,1999:blog-5964889903484807623.post-79485214130464933222013-06-03T21:45:45.896-07:002013-06-03T21:45:45.896-07:00If you have a set of points (x_i, y_i), and you do...If you have a set of points (x_i, y_i), and you do linear regression with a y-intercept of 0, then the least-square error slope is<br /><br />sum (x_i y_i) / sum (x_i)^2<br /><br />Compared to averaging the slopes, you can see that points with large x and y will dominate the calculation.Anonymoushttps://www.blogger.com/profile/12570655611553584218noreply@blogger.comtag:blogger.com,1999:blog-5964889903484807623.post-88045689610118489032013-05-24T06:51:50.731-07:002013-05-24T06:51:50.731-07:00I think if you are modelling a proportional relati...I think if you are modelling a proportional relationship, it is appropriate to use (0,0) as a starting point.<br /><br />Averaging slopes will not give you the same answer as regression. Surprisingly it is substantially worse for determining the slope in many cases than a linear regression which allows for a nonzero y-intercept, even if the data is truly proportional + some noise. But I think its statistically consistent and won't be too far off. <br /><br />Anonymoushttps://www.blogger.com/profile/12570655611553584218noreply@blogger.com