A statistical model is a formalization of relationships between variables in the form of mathematical equations. The model is statistical as the variables are not deterministically but stochastically related.

learn more… | top users | synonyms

1
vote
0answers
5 views

reference for regime shifting models

In looking for a good introduction to regime shifting models. It would be nice to see things like simple example of regime shifting models, ways to detect a regime shift in data, fitting regime ...
1
vote
0answers
29 views

Regression with dependent variable which ranges from -1 to 1

I performed a series of Pearson correlations which give me as expected values between -1 and 1 (actually very few below zero). I'd like now to see if some factors are linked to these correlation ...
1
vote
1answer
38 views

Creating an interaction term with 2 continuous variables: What to do?

I want to create an interaction term in SPSS on two continuous variables (ticket price and household income) in order to use this interaction term in a multiple regression model and test whether my ...
1
vote
1answer
30 views

Definition of 'Model Diagnostics'

Can anyone help me out with explaining what the term 'model diagnostics' refers to when applied to multiple regression please? In particular, what tests are necessary to check whether your estimated ...
0
votes
0answers
7 views

Train model based on correlations

I have a dataset of all trains in my country for a period in time, in a MySQL database. The form of this data is the following: ...
0
votes
0answers
11 views

Linear models comparison

I created two linear models to see if a treatment (switching an instrument) significantly affects the relation between two variables. The variables in the model are the same (Concentration ~ Signal), ...
0
votes
0answers
14 views

How to apply diagnostics to regression model from FactoMineR

Many diagnostics to assess regression models are listed on this page: http://www.statmethods.net/stats/rdiagnostics.html ...
0
votes
0answers
10 views

How to obtain design matrix for log-linear models?

I need to solve the question below by hand. I think the appropriate log-linear model is $Y_{ij} = \mu + \alpha_i + \tau_j + \epsilon_{ij}$. So, after researching, I realized that I should ...
0
votes
0answers
11 views

Why is the Multivariate RMSD “normalised” differently to the NMB?

For the multivariate case in regression, and also in other model predictions, the Root Mean Squared Deviation (RMSD or RMSE) is normalised by $n-p-1$, giving, $$RMSD = \sqrt{\frac{\sum_{i=1}^n ...
0
votes
1answer
20 views

ANOVA Table for Model In R

I'm trying to figure out how to produce an ANOVA Table in R for a multiple regression model. So far I can only produce it for each regressor, and the Mean Square is calculating as the same as Sum Of ...
0
votes
0answers
10 views

Filling in “gaps” in Sample data from universal data

Quick background - I have a BS in mathematics, and have just recently started a position related to data science (nothing heavy, but requires enough simple statistical analysis that I find myself ...
0
votes
1answer
78 views

Fit data to a bivariate function

I want to fit my (x,y,z) data points to a function. You can see the data on Fig.1. The data is symmetric along the main diagonal. To understand my data I have studied (y,z) curves at different ...
0
votes
0answers
6 views

In proc glimmix covariance parameter estimates, what is “scale”? Is it equivalent to residual error?

I conducted data analysis using proc glimmix for my proportional data. Below is the sas code I used and covariance parameter estimates from the output. The experiment was conducted by split plot in ...
0
votes
1answer
13 views

Combing Models for different levels of target variable in R

Beginner question please go easy. I have a classification variable with 7 levels. The crux of the problems comes down to the splitting of levels 1 and 2. Below is the output of a random forest in R. ...
0
votes
0answers
9 views

Question about longitudinal binary datanalysis

I am going to conduct longitudinal binary data analysis on a project since depend variable have multiple outcomes based on different time points. I am familiarly with traditional logit model and ...
1
vote
0answers
15 views

SEM design on dyadic data. Please help!

I have two surveys, and one is implemented to counselors. The questions ask about they feel about their relationship with their administrator. It has have two dimensions, let's say d1 and d2. I have ...
1
vote
0answers
17 views

Independence of residuals over time

My plots of conditional weighted residuals (CWRES) plotted against time show some sort of time trend (image attached). The response variable is on a Box_cox scale. How could I solve this problem ?
0
votes
0answers
15 views

How to forecast course completion percentage?

The goal of this task is to be able predict percentage of students who registered a specified term which in the future will pass the course. I did a logistic regression for binary response whether ...
1
vote
0answers
37 views

Why not just use log for regression if it improves r-squared?

theoretical question here: Say I have a model, $y = \beta_0 + \beta_1 x + u$ and it gives an $R^2$ of 0.02 Suppose, I re-estimate the model with $y = \beta_0 + \beta_1\log(x) + u$ which gives an ...
1
vote
0answers
6 views

Skewed response variable LM [duplicate]

I have a positive asymmetric response variable in a regression model. One of the assumptions about linear model is that the stochastic component of the model is normally distributed. If I have a ...
0
votes
0answers
14 views

Continuous low truncated response in regression

I can't find a clear answer on how to model a regression with a low bounded response. The tipical case is with response variables that can take only positive results. Poisson and negative binomial ...
0
votes
0answers
18 views

statistical modelling for imbalanced data

I am dealing with a binary response (good/ bad) type data set of size 2153, which reflects a dependent variable. Out of these, only 67 are in favor of "bad" and the remaining are of "good". Also, i ...
0
votes
0answers
35 views

Linear Probability Model Construction

I am dealing with a questionnaire consisting of 15 questions. I performed a factor analysis to all the questions and it has given three factors each consisting of 5, 7 and 3 numbers of questions ...
3
votes
1answer
46 views

Help constructing a simple regression model with a breakpoint

This is related to my questions here and here. I am still struggling with my model, so I am taking it back to basics. My assertion is simple, I believe that watershed runoff will have a different ...
2
votes
2answers
55 views

Combination Forecast - Which models to pick?

Combination Forecasting can be produced by simply averaging different forecasts or employing more complex techniques (see Makridakis, 1989; De Gooijer and Hyndman, 2006; Goodwin, 2009; Pesaran and ...
4
votes
1answer
113 views

Maximum number of alternatives in a discrete choice model

We are modeling a discrete choice scenario, with alternative-specific coefficients. We also break the assumption of independence of irrelevant alternatives. To model this, we are using an ...
0
votes
0answers
33 views

Project help [undergraduate] [R]

I have to do a project for my statistics course, which I have now finished but would very much appreciate some insight and help as I've never done anything like that before. I was given data on ...
0
votes
0answers
23 views

Optimal model/statistical test for my design?

I have a design with 1 between-subject factor and 2 within-subject factors as independent variables and 5 dependent variables (longitudinal accelartion, lateral acc., response time, first conscious ...
2
votes
0answers
13 views

A control variable that forms part of the definition of the dependent variable: Drop it or transform it?

My aim is to analyze, using OLS, how Y (firms' benefits) depend on some factors. To normalize Y, I divide it by firms' size (S). Therefefore, my dependent variable is Y/S. To know how size affect ...
1
vote
1answer
47 views

How to decide whether a variable belongs to a linear model?

I have a set of inputs $x$ and noisy outputs $y$. I think that either $$y = a_0 + a_1 x$$ or $$y = a_0 + a_1 x + a_2 x^2.$$ How can I determine which model was more likely to have generated the data? ...
0
votes
0answers
15 views

Library for using LASSO to tune parameters of arbitrary model?

I typically see LASSO applied to the question of finding coefficients of a linear model. I'm looking for a library/tool that performs LASSO with an aim to tune the parameters of an arbitrary ...
1
vote
1answer
32 views

Does AIC require the residuals of the model to be normally distributed?

Does AIC require the residuals of the models to be compared to be normally distributed?
0
votes
0answers
21 views

Determining if data follows a Sigmoid function given data that does

I am essentially trying to determine if some gene expression data I have follows as circadian pattern. Currently I have gene expression data for genes know to follow a circadian pattern. I want to ...
0
votes
0answers
16 views

hedonic model: estimating coefficients for variables not used in the regression

I'm trying to estimate the value of a real estate upon its characteristics. To do so, I'm using the Hedonic Model and I'm doing the regression using ...
0
votes
1answer
19 views

Nonlinear Gompertz model

Is it possible to use Gompertz to model a non time series data? i.e. change t (time) into x (independent variables). Data does not involve growth or survival.
4
votes
1answer
64 views

What distribution is appropriate for modeling internet forum posting?

Internet message boards, such as 4chan, consist of a series of "threads" to which people post replies. One observes that most threads receive zero or one reply, a few receive 2 or more, and only the ...
0
votes
0answers
29 views

choosing between non-linear/logistic regression models

We are fitting regression models to dose-reponse data obtained from drug assays. Where a drug has an effect on cell viability, there is often a sigmoidal relationship between the proportion of viable ...
0
votes
0answers
20 views

Fitting a model while adjusting for some variable

I plan to figure out the effect of variable X on variable Y. I have time series data for both X and Y and a simple regression model should do the job. Unfortunately the variable Y is also affected by ...
0
votes
1answer
17 views

Model for probability of N autocorrelated events

Say we have $N$ birds, $r$ is the probability that one bird sings. What is the probability $p$ that any of $N$ birds sings? If we assume independence, there is a simple model describing the ...
0
votes
0answers
61 views

Setting up a aov model in R for a variant of a 2-way nested ANOVA

I have a rather complex experiment i need to analyse The experiment was an infection trial, where the effect of two different infection methods ("inoculation" and "in-contact infection") were ...
1
vote
1answer
83 views

Creating a disease severity score/index

I am trying to design a numerical scale which would describe the severity of a certain disease (in this particular case anaphylaxis). I have a set of clinical symptoms and a database of patients who ...
0
votes
1answer
61 views

Model Selection and RFE using caret

I'm faced with a high dimensional (samples=148, features=20000), supervised binary classification problem. Which I would like to approach with an ensemble of classifiers, that will classify using a ...
1
vote
0answers
78 views

Statistical model Regression

A statistical model is often defined as a set of probability distributions on a random variable of interest, indexed by a parameter $\theta$; hence, for continuous random variables equivalently as a ...
0
votes
0answers
11 views

Heterogeneity test for risk factor association (hazard ratio/coefficient) in two or more different survival outcomes

Suppose I'm interested in comparing smoking category (current, ex, non-smoker) as a risk factor in two or more different but similar (considerably exclusive) outcomes, say lung cancer of different ...
0
votes
1answer
80 views

Optimizing parameter estimates by minimizing chi^2 in iterative procedure

I need to minimize my Chi^2 (bottom-left in figure 1) by adjusting parameter-values in a MLE-procedure (or something alike). The chi^2 (red) is a goodness-of-fit measure. It expresses how well the ...
0
votes
1answer
80 views

Mathematical Modeling and Statistical Modeling

What is the difference between mathematical modeling and statistical modeling? I only know that a mathematical model is deterministic while a statistical model is stochastic. Is that all to answer ...
0
votes
0answers
14 views

Binary outcome and correlated predictors

I have binary outcome variable (infection yes/no), two types of predictors, correlated (CRP 1, CRP 2 and CRP 3) which can be numeric or binary (it's pretty same to me) and uncorrelated predictors ...
0
votes
1answer
36 views

No ARIMA, No GARCH, which model?

I am trying to fit a model for a data set. The acf and pacf, after differentiating the data are: The acf shows that the returns appeared to be random. According to them seems like the ARIMA model ...
9
votes
1answer
167 views

Additive Error or Multiplicative Error?

I'm relatively new to statistics and would appreciate help understanding this better. In my field there is a commonly used model of the form: $$P_t = P_o(V_t)^\alpha$$ When people fit the model to ...
0
votes
0answers
24 views

What is Tau and Omega in the Black Litterman model?

I'm looking into the BL model when it comes to portfolio optimization, and I'm having a hard time trying to understand each one. I've read on several papers that Omega is the covariance matrix, but I ...