Recent content by bbb999

@statdad Sorry is that yes for the original posts about upper and lower bounds being \pm \sqrt{10}?

I used scilab to work out the iterations of the sequence and my above post is correct I think?

The sequence converges to \sqrt{10} for any initial value chosen other than -\sqrt{10}

Well I know the limits of s_{n+1} and s_n are the same. Solving for the limit fives the limit to be \pm \sqrt{10} so does that not mean the upper and lower bounds are these?

I have a sequence where s_1 can take any value and then s_{n+1}=\frac{s_n+10}{s_n+1} How do I show a sequence like this is bounded?

Can anyone confirm that this is correct: that for every initial value other than -root 10, the sequence converges to root 10 and in the case of -root 10 it remains at that point?

thanks I just wanted to make sure I didn't need to mention the f statistic

What I mean is, can I just say that even though the last model shows height and weight not to be significant, the first two shows they are and the last model is just adding more information to these intial models. So despite the 't' values the third model is still a good model of calorie intake?

thanks again, just to check, would I need to talk about the F statistic or would I be able to say the above without it?

thanks for all the help. So can you just check this: I need to use the F statistic to show that even though neither are significant due to the 't' values being lower because of the correlation, the fact that we have more information than the two models containing just height or weight means...

Would it be the F statistic? So I can say that even though the correlation causes neither to be significant, the fact that we have more information means it is a better model?

No are you not gaining information?

Are the conclusions not just that calorie can be modeled using height as a variable and using weight but not using both together?

can anyone help me with how the results if interpreted correctly can still draw the right conclusion?

Sorry, it says to explain why the contradiction occurs and why if the results are interpreted correctly the right conclusion can be drawn with no contradictions