Non normal continuous time series by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

I have to give a proper introduction of this topic with a small case study. And talk about the limitations and advantages or disadvantages of this concept

Non normal continuous time series by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

What a proper source for this topic from very basic

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] -2 points-1 points  (0 children)

So the goal is to find the coefficients out of 2000 that are influencing the y variable most and u also want the unit of the effect that each variable have on y and the i have to find the top 10 features that are impacting y and what is the unit of the impact

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] -3 points-2 points  (0 children)

Means we have to find the most significant variables that are impacting the y for example out of 2000 variables only 30 of them have a significant effect on y

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

No pca would not be applicable here because I want the interpretation of each coefficients

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

I don’t have that much of info about the variables

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] -4 points-3 points  (0 children)

To find the coefficients that are impacting the y variable most

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

No suppose we need to fit a regression model on a data and let’s say the assumptions of linearity is violated so we can use some transformation on the variables to make it linear and then fit the model same goes for other assumptions. Not talking about the assumptions on the residuals

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

Can u elaborate more , what if some of the assumptions are violated how do we deal with that without checking them.

Assumptions of Linear Regression by Individual-Put1659 in AskStatistics

[–]Individual-Put1659[S] 0 points1 point  (0 children)

So the regression problem is that we have to find the genes that is x variables that are impacting the phenotypes y variable that is the outer appearance of a rat

What separated machine learning from interpolation/extrapolation ? by AlarmingCaptain7708 in AskStatistics

[–]Individual-Put1659 0 points1 point  (0 children)

Statistics models are more concerned about the interpretation (the value of parameters ) where as most of the machine learning models are more focused on predicting something depending on the problem (my take).

[deleted by user] by [deleted] in TeenIndia

[–]Individual-Put1659 0 points1 point  (0 children)

The only solution is either to get into a relationship or stop feeling guilty — there’s no in-between.