Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
0
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2answers
31 views
What is endogeneity and what does it mean substantively? As an extension what is exogeneity?
My apologies if this is an obtuse question, I am neither a statistician nor a econometrician but as a student is empirical methods this question plagues me.
I understand that $$X'\epsilon=0$$ not ...
3
votes
1answer
59 views
Is the exponential distribution a good model for this data?
I'm trying to determine if the exponential distribution is a good model for a data set that I'm exploring. It doesn't have to be precise. I'm using the data for capacity planning (if it's a good fit) ...
0
votes
0answers
12 views
OK to use residual sum of squares for cross-validation of binary outcome?
For an OLS model the mean squared error can be used to assess the fit of the trained model on the validation data.
What is the equivalent for a logistic regression model? Can I simply use the ...
1
vote
1answer
25 views
Standard errors of regression coefficients based on sample size
For any particular nonlinear regression: $$Y_i = f(\mathbb{x_i},\theta) + \epsilon_i, i=1,...,n$$ I currently have standard errors for each of the $\theta_j$ obtained via the Gauss-Newton algorithm
...
0
votes
1answer
15 views
can EM algorithm be applied to my problem? Input data set is based on a function of parameter
I understand EM algorithm is often used for missing data/mixture problem. But can it be used to optimize a particular type of likelihood based on jointly fitting variables and transformations of those ...
0
votes
0answers
31 views
Differences between minimizing euclidean distance and difference square in linear regression?
i need to solve a question and it asks me using two different approaches,
minimizing Euclidean distance between ($x_t$, $r_t$) and its projection on the line
minimizing the difference square between ...
1
vote
1answer
43 views
Ordinary Least Squares method: why are my regression results insignificant?
I have a problem in my thesis results of OLS regression being insignificant.
I have 3 sectors and each sector has 130 observations.
Is this sample size is sufficient or not ?
Can anyone suggest ...
0
votes
0answers
36 views
How to compare 2 regression slopes with R?
I have to compare the slopes of 2 regression lines with R. The 2 regressions are made with the same parameters in 2 different locations.
I did my regressions with the function lm(). Now I have the ...
1
vote
1answer
49 views
Regression model for periodic data
Moved from Stack Overflow
Could someone help me to find adequate regression model for my data?
I tried to find one by changing the model and initial approximation (ln 15-16) in this simple Python ...
0
votes
0answers
29 views
How can i find code( matlab, vb, c++) for running fuzzy regression?
I am new in fuzzy regression, i knew about its theory but have problems in running it in matlab or other programs. I will be so helpful if someone helps me in finding codes for fuzzy regression.
1
vote
2answers
23 views
Calculate the tendency of a set of samples
I develop an application in which i constantly get samples of heart pulse.
I defined an interval of t seconds.
In each t seconds I have n samples.
In every interval, I want to calculate the ...
2
votes
1answer
109 views
How to compare two regression slopes for one predictor on two different outcomes?
I need to compare two regression slopes where:
y ~ a + b1 x
y ~ a + b2 x
How can I compare b1 and b2?
Or in the language of my specific example in rodents, I ...
1
vote
0answers
22 views
Breakpoint for bivariate data
The breakpoint(s) estimation approach implemented in the strucchange package (Zeilei & al) seems to work very well (based on my little experience with this package on real case studies).
Is ...
2
votes
1answer
43 views
First difference or log first difference?
I am evaluating the effect of covariances between series on returns. That is I run the following regression: $$ r_t = \beta_0 + \beta_1\text{Cov}(Y_t,r_t) + ...$$
I have conducted my analysis with ...
1
vote
0answers
22 views
R module for creating plots of prototypical individuals from fitted models?
In order to help interpret fitted models — especially those with interaction terms and non-linear components — I've found it useful to plot predicted values of a dependent variables for what we might ...
1
vote
0answers
14 views
Addressing multicollinearity with key driver analysis
I am trying to determine the key drivers from a series of 30 Independent Variables (IVs) (attributes rated on 10 pt scale) on 3 Dependent Variables (DVs) (i.e. purchase intent). The 30 IVs are pretty ...
0
votes
1answer
40 views
Conditional expected value from a regression model using ordinary least squares
I have a query regarding part (a) of the following question. I cannot figure out how to calculate the conditional expected value of collections for the month of Easter. Is it not possible to calculate ...
1
vote
1answer
50 views
Can I use a regression model with ANOVA significance greater than 0.05?
I have a multiple regression using SPSS. The significance of my model in the ANOVA table is p=0.174 which is >0.05. what does this mean for my model? Can I still use it and proceed in the ...
0
votes
2answers
76 views
How to interpret the output of the summary method for an lm object in R? [duplicate]
I am using sample algae data to understand data mining a bit more. I have used the following commands:
...
1
vote
1answer
31 views
Do you always measure reliability through the Cronbach's alpha coefficient? Or is there another way? (multiple linear regression)
I started doing a quantitative research for my thesis without any previous SPSS or proper statistic experience, and the first thing my professor told us in the spss workshop is that you start by ...
1
vote
1answer
27 views
Heckman's two stage: multiple selection models
If a sample is likely to be self-selected on multiple selection criteria, does it make sense to include Inverse Mill's ratios for multiple selection models in the same second stage OLS model?
1
vote
1answer
47 views
How to calculate confidence intervals of $1/\sqrt{x}$-transformed data after running a mixed linear regression in stata?
I have run a series of mixed linear regressions in Stata, some with inverse-square-root ($1/\sqrt{x}$) transformations and others with square root ($\sqrt{x}$) transformations.
How do I calculate ...
0
votes
0answers
28 views
Performing Linear Regression on Rolling Averaged Data
I have multiple years of daily aggregated data for which I want to conduct a multi-variable linear regression. Auto correlation is high with one particular variable included and there is a strong ...
2
votes
1answer
48 views
Linear Regression with a Dependent Variable that is a Ratio
I'm doing linear regressions where the dependent variable is a ratio that can range from 0.01 to 100.
Is it ok to take the log of the dependent variable and the regression on that?
I'm matching the ...
1
vote
0answers
24 views
Regret in the linear regression setting
I've seen the concept of regret apply mostly to online learning problems, but while going through the definition it does seem that is not bounded to this setting.
I'm trying to come with a simple ...
13
votes
1answer
115 views
Random forest assumptions
I am kind of new to random forest so I am still struggling with some basic concepts.
In linear regression, we assume independent observations, constant variance…
What are the basic ...
0
votes
1answer
51 views
Logistic regression: controlling variables not significant, what should I conclude/further test? [closed]
I ran annual logisitic regression on time-series datas. The most important independant variable have coefficient that are significant in a lot of years, that's a relief. But the "controlling ...
3
votes
0answers
34 views
How to build a model where variance depends on covariate?
I have what I believe is a very simple problem for anyone used to modelling with unequal variances (which I am unfortunately not). I have a dependent variable "totrich" which I want to model as a ...
1
vote
0answers
9 views
Error function of noisy input and target variables
Why is the sum of squares error function of noisy input and noisy target variables very similar to the error function for only noisy input?
This is the relevant part in Bishop's book:
Another ...
10
votes
2answers
254 views
How are regression, the t-test, and the ANOVA all versions of the general linear model?
How are they all versions of the same basic statistical method?
0
votes
0answers
32 views
Appropriate method for supervised learning of small data set with few variables
What method exc. for regression can be used in order to get y=f(x1,x2) on a training set of 800 to 2000 samples? y is a whole number <0,15>, x1,x2 are real <0,40>?
I'm interested in prediction ...
0
votes
1answer
34 views
Weighted multiple regression in R with prespecified weights
I would like to run a regression of the following form:
Y ~ B1*predictor1 + B2*predictor2 + B3*predictor3
I would like to specify ...
0
votes
1answer
40 views
Basic questions concerning the interpretation of results from summary(lm(…~…)) in R [duplicate]
set.seed(11)
a = runif (12)
b = rep(c(1,2,3),4)
summary(lm(a~b))$coeff
summary(lm(a~b-1))$coeff
What does a p.value for the intercept means ?
What differences ...
2
votes
2answers
71 views
Does the intercept count as a parameter for the n/parameters sample size rule for multiple regression?
When estimating parameters, I know the general rule of thumb is n/parameters should be >10. Does the intercept in a model count as one of the estimated parameters in this "rule"?
For example, if I ...
1
vote
1answer
49 views
R Forward and backward Selection
I have a data set with large number of attributes some are not relevant and some are relevant for the regression model. My approach was to do forward and backward selection to identify a starting ...
0
votes
0answers
19 views
Changing prior for a regression model
I have a regression model trained on a particular output distribution (for example N(0, 1)). I now have to do a prediction on a test set, with a caveat that I know that the distribution of the test ...
0
votes
0answers
20 views
Strategy for building best fit multiple regression model with time lagged variables
I am building a multiple regression model - wrapped in a function - with one dependent variable and a dozen independent variables. The reason why I am building a function is that I need to do this ...
2
votes
0answers
33 views
OLS standard error log log regression
I am estimating the following Power Law relationship:
$$\ln(\text{Rank}) = \text{constant} + \alpha \ln(\text{Size})$$
where $\text{Rank}$ is $1,~2,~3,~...,~n$, and $\text{Size}$ is the raw value.
...
0
votes
1answer
51 views
How many observations are enough to perform linear regression with fixed effects
I am new to econometrics. I am studying earnings management in banks during the financial subprime crisis.
I manage to collect data from 23 banks from 2005 to 2010. Few years of them are missing but ...
6
votes
1answer
107 views
+50
What does the residual higher level variance tell me?
I have a multilevel logistic regression model predicting the probability of item nonresponse, where the random intercept variance at country level takes on the following distribution for the different ...
2
votes
3answers
71 views
Which glm algorithm to use when predictors are numerical as well as categorical?
I just need a direction on which regression algorithm (preferably glm or similar) algorithm to use when the predictor variables are a mix of numerical and categorical variables. The output is ...
0
votes
1answer
77 views
Multiple Choice on Linear Regression
1. Which one is NOT a linear regression models? Please give a 1-2 sentences brief
explanation to your choice.
(a) $y_i = β_0 +\exp(β_1x_i)+E_i, i = 1, 2, \ldots, n$
(b) $y_i = β_0 + β_1x_i + β_2 ...
2
votes
0answers
69 views
Universal Approximation Theorem — Neural Networks
I have posted this question elsewhere--MSE-Meta, MSE, TCS, MetaOptimize. Previously, no one had given a solution. But now, here is a really excellent and comprehensive answer.
Universal approximation ...
3
votes
1answer
52 views
Model Building: Missing Data or Large Gap between data points
I am currently trying to build a model using a data set that has large gap between data points. When I look for the correlation I clearly see a negative regression line. But I am worried about the gap ...
1
vote
1answer
68 views
normality distribution
I have a problem with normality test. In order to make sure that I can use parametric test, I need to make sure that my residual distribution is normal. However, when I refer to the value of skewness ...
3
votes
1answer
45 views
Hold-one-out linear regression : a shortcut?
For a series of observations $(\vec{x}_i, y_i), i = 1 \cdots N$ from the linear model $Y = \beta^T X + \epsilon$, the least squares estimate of $\beta$ is: $\hat{\beta} = (\mathbf{X}^T ...
3
votes
0answers
34 views
Brant test in R
In testing the parallell regression assumption in ordinal logistic regression I find there are several approaches. I've used both the graphical approach (as detailed in Harrell´s book) and the ...
2
votes
1answer
42 views
Polynomial regression using scikit-learn
I am trying to use scikit-learn for polynomial regression. From what I read polynomial regression is a special case of linear regression. I was hopping that maybe one of scikit's generalized linear ...
1
vote
1answer
67 views
Is $R^2$ value valid for insignificant OLS regression model?
I am interested in stating that ___ % of the variance in Y is explained uniquely by $X_1$ and ___ % is explained uniquely by $X_2$.
Is there some way to obtain this from a multiple regression ...
0
votes
1answer
82 views
How to handle Regression data thats not linear
I'm new to stats and am using Python 2.7 to fit a regression model (Random Forest). When I plot the percentile plot of the prices before and after a log ...
