By using the CDF distribution function!
All distribution functions F( ) varies between 0 and 1. So U = F(X) will give you U, an uniformly distributed random variable.
Just plug in the x values you have in the CDF and it will give you uniformly values.
On the other hand, if you have uniformly...
But not far from normal. Kind of "Bell shaped" (but I dont know if that has a formal definition). To me it looks like negatively skeved.
But the mean of this will be approximately normal, is that is what you are asking.
You forget about the residuals that infuences H5 and H7. They have an influence on H5 and H7 and there by on y. If you omit these residuals then you have a few omitted variables.
What do you mean by "better"? You will not recover the structural relations. Do you mean variance in prediction...
No, I should have made an example with say s=6.5. That would have been a better example. Of course sigma is (in general) not known.
If sigma happened to be known (maybe from a large population study or many other similar studies) then we would use 1.96*sigma/√(n)
Well if you "know" that model 2 is the correct model, then you would like to know the parameter values from that one. That is, you want to uncover the true relations among the variables.
Model 1 and model 2 are relatively simple models. y is influenced by H1 to H7, But say H1 is not influenced...
(I dont know what you mean by "sigma=s*√(n/(n-1)" )
Sigma is the standard deviation in the population.
s is the standard deviation in the sample.
s is an estimate of sigma.
The standard error is s/√(n). That gives the "uncertainty" in the sample mean.
The population mean of...
I don't understand the question.
I just tried to write down the equations for each graphical model
Model 2 is a more restrictive model than model 1. Model 2 says how all variables affect the dependent variable y, directly or indirectly.
One can say that model 2 is a "structural equation...
Well if y is coffee consumption, then model 1 is:
y = a+ b1*H1 + b2*H2 +....b7*H7 + residual
Then model 2 is a little bit more complicated
There are several equations in the model:
H5 = a+ b1*H1 + b2*H2 + residual5
H7 = c0 + c1*H3 +c2*H4 + residual7
y = d0 + d1*H5 + d2*H6 + d3*H7 +...
I did not understan this part. No! Wait! Is the 160 cm the hight above the ground?
That would just mean that you have two response variables: y1 = the weight of leaves below 160cm and y2 = the weight above 160 cm.
Then you can just do a t-test for each of the variables (or a WMW if you...
But you can not replace a missing value with zero. (If I refuse to tell you my length it does not mean that it is zero.)
#change NA’s to 0's
my_data[is.na(my_data)] <-‐ 0
It seems like most of your data are positive values. To impose zero values will increase the...
What kind of numbers does that give?
So the ESG scores will explain the stock value? So that you have a model like:
Stock_value = a + b* ESG + other_things
Is not every stock valued at completely different scales? How can you compare them?