Defining fixed effect and random effect in glm model The 2019 Stack Overflow Developer Survey Results Are InDo you know of any Calculus text defining the exponential and logarithm functions in an alternative way?Definition of “point” and “vector” in $BbbR^n$, and a model for $BbbA^n(BbbR)$..Changing the zero product property and defining division by zeroDefining sine and cosineQuestion about uncorrelatedness of random variables and distributionsCommon distribution function of continuous and discrete random variablesAre intervals 'reversible', and how beneficial would defining them with this property be?Why not defining random variables as equivalence classes?Defining a graph with both vertex cost and edge costDefining premise and conclusion
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Defining fixed effect and random effect in glm model
The 2019 Stack Overflow Developer Survey Results Are InDo you know of any Calculus text defining the exponential and logarithm functions in an alternative way?Definition of “point” and “vector” in $BbbR^n$, and a model for $BbbA^n(BbbR)$..Changing the zero product property and defining division by zeroDefining sine and cosineQuestion about uncorrelatedness of random variables and distributionsCommon distribution function of continuous and discrete random variablesAre intervals 'reversible', and how beneficial would defining them with this property be?Why not defining random variables as equivalence classes?Defining a graph with both vertex cost and edge costDefining premise and conclusion
$begingroup$
I'm unconfident that whether my understanding on fixed effect and random effect is correct:
Fixed effect= variable that make inferences about the specific levels.
Random effect= variable that make inferences about and generalise to a wider population.
Now I have 6 variables in my glm model:
- Channel – YouTube account the video was uploaded from
- Views – Number of times the video was viewed
- Comments_disabled – Whether the channel disabled other users from commenting on the video (no = comments enabled, yes = comments disabled)
- Theme – Category of the video (e.g. ‘Drama’, ‘Family’ etc)
- Weeks – Number of weeks available on YouTube to date
- Tags – Number of tags, key words assigned to the video that users can search for within YouTube
I defined them as:
Fixed effect: 2
Random effect: 1, 3, 4, 5, 6
Am I correct?
definition
asked Mar 23 at 18:02
BlackSkyBlackSky
408
$endgroup$
add a comment |
$begingroup$
I'm unconfident that whether my understanding on fixed effect and random effect is correct:
Fixed effect= variable that make inferences about the specific levels.
Random effect= variable that make inferences about and generalise to a wider population.
Now I have 6 variables in my glm model:
- Channel – YouTube account the video was uploaded from
- Views – Number of times the video was viewed
- Comments_disabled – Whether the channel disabled other users from commenting on the video (no = comments enabled, yes = comments disabled)
- Theme – Category of the video (e.g. ‘Drama’, ‘Family’ etc)
- Weeks – Number of weeks available on YouTube to date
- Tags – Number of tags, key words assigned to the video that users can search for within YouTube
I defined them as:
Fixed effect: 2
Random effect: 1, 3, 4, 5, 6
Am I correct?
definition
asked Mar 23 at 18:02
BlackSkyBlackSky
408
$endgroup$
add a comment |
$begingroup$
I'm unconfident that whether my understanding on fixed effect and random effect is correct:
Fixed effect= variable that make inferences about the specific levels.
Random effect= variable that make inferences about and generalise to a wider population.
Now I have 6 variables in my glm model:
- Channel – YouTube account the video was uploaded from
- Views – Number of times the video was viewed
- Comments_disabled – Whether the channel disabled other users from commenting on the video (no = comments enabled, yes = comments disabled)
- Theme – Category of the video (e.g. ‘Drama’, ‘Family’ etc)
- Weeks – Number of weeks available on YouTube to date
- Tags – Number of tags, key words assigned to the video that users can search for within YouTube
I defined them as:
Fixed effect: 2
Random effect: 1, 3, 4, 5, 6
Am I correct?
definition
asked Mar 23 at 18:02
BlackSkyBlackSky
408
$endgroup$
I'm unconfident that whether my understanding on fixed effect and random effect is correct:
Fixed effect= variable that make inferences about the specific levels.
Random effect= variable that make inferences about and generalise to a wider population.
Now I have 6 variables in my glm model:
- Channel – YouTube account the video was uploaded from
- Views – Number of times the video was viewed
- Comments_disabled – Whether the channel disabled other users from commenting on the video (no = comments enabled, yes = comments disabled)
- Theme – Category of the video (e.g. ‘Drama’, ‘Family’ etc)
- Weeks – Number of weeks available on YouTube to date
- Tags – Number of tags, key words assigned to the video that users can search for within YouTube
I defined them as:
Fixed effect: 2
Random effect: 1, 3, 4, 5, 6
Am I correct?
definition
definition
asked Mar 23 at 18:02
BlackSkyBlackSky
408
asked Mar 23 at 18:02
BlackSkyBlackSky
408
edited Mar 23 at 18:12
BlackSky
asked Mar 23 at 18:02
BlackSkyBlackSky
408
asked Mar 23 at 18:02
BlackSkyBlackSky
408
asked Mar 23 at 18:02
BlackSkyBlackSky
408
408
add a comment |
add a comment |
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