Refers to any model where the a random variable is related to one or more random variables by a function that is linear in a finite number of parameters.

learn more… | top users | synonyms

5
votes
2answers
80 views

What is MA(q) model input in real world?

I understand the AR(p) model: its input is the time series being modelled. I'm completely stuck when reading about the MA(q) model: its input is innovation or random shock as it's often formulated. ...
2
votes
0answers
30 views

Interpreting regression coefficients of log(y+1) transformed responses

I have measurements $y_1$,...,$y_i$,...,$y_n$ taken from a set of replicates in a factorial designed experiment. In order to use a linear regression I define my response $z_i = log(y_i + 1)$. The ...
2
votes
1answer
33 views

What exactly does the 'boxcox' function in R do?

I am familiar with the power transform family and I know how to estimate the MLE for $\lambda$ for given samples of a random variable. I have been using the 'boxcox' function in R for a sample of a ...
1
vote
2answers
28 views

Trouble fitting a simple linear regression

I have been trying to do a simple linear regression of x3=Weeks Claimed against x4=Weeks compensated. I am having problems because my residual standard error is very high and also my residuals are ...
0
votes
0answers
14 views

Classification measures for linear classifier

Let $\mathcal{H}\colon\mathbf{w}\cdot\mathbf{x}+b=0$ be a separating hyperplane, which some binary linear classifier results in. Let $\mathbf{x}_t$ be an unseen, new sample that appears and needs to ...
1
vote
0answers
19 views

What criteria are used to compare feature-based classification techniques?

When comparing feature-based classification techniques, what characteristics about the different processes should be considered? I'm comparing different classification techniques to try to figure ...
0
votes
0answers
27 views

Inflation as an independent variable

Assume a model like this, basically explaining stock market returns with a bunch of stuff: ...
0
votes
0answers
22 views

Help interpreting R linear model fit [duplicate]

I have observed variables power$values. I am trying to model this process using a second set of observations, such that $P = M\cdot X + B$. $P$ is the function ...
1
vote
0answers
13 views

Transforming / Evaluating (Probably) Normal Data from Sample with Linear Probability Distribution

I have a sample of data for which the count of a given value increases roughly in proportion to the actual value. For example, the value 22 will occur around 22 times and the value 30 will occur ...
0
votes
0answers
4 views

Liblinear types of solver

There is many variants of type of solver in liblinear but I don't understand their differences.Which one I must choose? Also why data must be scaled? duo to some numerical issues? ...
0
votes
1answer
38 views

Multiple linear regression through orthogonal matrices

An example of linear regression could look like: $min \sum_{i=0}^{m}||x_i A - y_i||_2^{2}$, where ${x_i, y_i} \in \mathbb{R}^n$ and $A \in \mathbb{R}^{n\times n}$. I am interested in knowing how do ...
0
votes
1answer
52 views
1
vote
1answer
24 views

Dummies instead of the Chow test

I have found somewhere a mention to the possibility of using dummies variables instead of the Chow test to test whether the coefficients in two linear regressions on different data sets are equal. ...
0
votes
1answer
26 views

Interaction: ignore two-level if three-level is significant in every case?

I am using lmer in R to run LMM. My DV is continuous and my IVs are categorical. Many statistician said if the three-level parameter is significant, I cannot interpret the two-level parameter. Does it ...
0
votes
1answer
50 views

Interpreting the “coefficient” output of the lm function in R

I have created a linear model (which has multiple predictors) using the lm() function and I would like to interpret the "coefficients" that I get when I use the summary() function on the linear model. ...
4
votes
3answers
149 views

Question about the error term in a simple linear regression

Suppose we have a linear regression model $Y_{it} = \beta_0 + \beta_1 X_{it} + \epsilon_{it}$, many times in literature it is assumed that $\epsilon_{it} \sim N(0,\sigma^2).$ This assumptions makes ...
1
vote
1answer
34 views

Linear regression on grouped data

I'm new in stats..I hope to write something that makes sense. I have a sample composed by 200.000 projects, each project is defined according to its size (S) and the presence of active users (U). The ...
0
votes
0answers
34 views

Random model selection and validity of significance tests

Suppose we have some data, $\{y_i, x_{1i}, \dots, x_{ki}\}_{i=1}^n$ and we want to build a linear model of the form $y_i = \beta_0 + \beta_{1'i}x_{1'i} + \dots + \beta_{k'i}x_{k'i} + \epsilon_i$, ...
0
votes
0answers
15 views

Interpretation of three-way interaction in an output of lmer

I am using lme4 to run a three-way interaction model. I have three independent variables: animal (rat, lion, dog), color (red, green, blue) and sex (male, female). The baselines are as follows: ...
0
votes
0answers
14 views

Do I need a planned contrast or post-hoc analysis when doing LMM in R?

I am doing a LMM in R and would like to know if I need to do a planned contrast or a post-hoc analysis. From my understanding, the LMM in R already provideds me a planned contrasts and if I have ...
0
votes
0answers
15 views

Including squared predictors in model matrix [migrated]

I have the following code x <- c(1, 2, 3) y <- c(2, 3, 4) z <- c(3, 4, 5) df <- data.frame(x, y, z) model.matrix(x ~ .^4, df) This gives me a model ...
1
vote
0answers
34 views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
0
votes
0answers
9 views

Goodness of Fit tests in a linear model (lin-log)

I am using a lin-log model and am currently doing tests for goodness of fit of the regression. I already used the R-squared test, Q-Q plot and Shapiro test. Are there any other tests i could use in R ...
2
votes
0answers
34 views

Effect of each parameter on a Monte Carlo Simulation

I was wondering what is the best way to determine the effect of each random parameter on the result obtained from a Monte Carlo Simulation. I realise I have asked a similar question here, but this ...
2
votes
1answer
64 views

Interaction term in a linear log model

I am using a linear-log model to test whether overseas development assistance and remittances positively affect FDI in cases of good governance and financial market development. Let's say I want to ...
1
vote
1answer
30 views

Explanatory variables in a Lin-log model

I have a data set and I want to fit a Lin-log model. Is it possible to apply the log transformation only to some of the explanatory variables or should the log ...
0
votes
0answers
22 views

When are multiple linear regressions independent?

I'm doing many pairwise linear regressions on a set of variables. For example x as the dependent variable vs y, ...
4
votes
1answer
67 views

Statistical significance for comparison of linear regression models

I have two linear regression models (with the same predictors) that try to estimate two different (although related) features of the same population. I am analyzing the hypothesis that these ...
2
votes
1answer
20 views

When no model comparison, should I use REML vs ML?

I'm running LMM, and I will make no comparison of models. Could I ask which one should I use between REML and ML?
0
votes
0answers
74 views

Difficult interpreting linear mixed model result - R lme function

I'm fitting an harmonic regression model on data from different plants separately as follows: ...
0
votes
0answers
20 views

Power analysis in a linear model - pooled vs individual samples

I'm interested in how many samples are required to see a relationship between allele frequencies (proportions of alleles) and environmental gradients, such as temperatures and water quality. ...
4
votes
1answer
53 views

Residual Vs. Fitted Plot with Outliers

I have a model relating fuel consumption to other vehicle parameters, which produces the following Residuals Vs. Fitted plot. My Question: Is the skew to the right simply an indication of outliers ...
1
vote
1answer
53 views

R: multiple linear regression model and prediction model

Starting from a linear model1 = lm(temp~alt+sdist) i need to develop a prediction model, where new data will come in hand and predictions about ...
0
votes
0answers
12 views

So the interpretation of 2-way interaction cannot be interpreted if it is significant in 3-way interaction?

I use LMM to analyse my data and my variables are as follows: DV: continuous IV: 1) color(red, blue, green), height(low, tall, medium), and sex (male, female) Then for color, "red" is baseline, and ...
0
votes
1answer
34 views

R: Explanation of a multiple linear regression summary [duplicate]

I am quite new with R and while i am able to perform the basics i am not yet able to understand the output results. For example: summary(lmodel) generates the ...
2
votes
1answer
148 views

The main effect will be non-significant if the interaction is significant? [duplicate]

I am using linear mixed models to identify important factors, and it turns out that: A: significant B: not significant ...
2
votes
1answer
89 views

How to define weights when testing an exponential trend?

In a linear model including ANOVA one can test a trend (e.g., linearity, quadratic effect, etc.) among the ordered effects (regression coefficients or factor levels) through assigning proper weights. ...
1
vote
1answer
77 views

Is it necessary to exclude all nonsignificant parameters to choose the best model?

I'm running LMM models and could I ask if I can just report the model after comparing random intercept with random intercept and slope model without excluding nonsignificant factor?
1
vote
2answers
70 views

Should the outcome variable be measured at least twice for a longitudinal study?

I am trying to find the association between BMI and onset age of a condition with linear regression model. I have multiple records of BMI measurement. But the outcome variable, onset age of condition, ...
0
votes
0answers
31 views

Best Regression with Binary Features

I'm seeking to do a linear regression for an evaluation function in a board game. My features are all (signed) binary 1 0 0 1 -1 1 0 0 0. Mostly zeros. Around 200 to an observation. I have 10 million ...
2
votes
1answer
93 views

What type of multivariate linear regression is this?

I'm trying to reproduce a result from a book (see bottom) and it doesn't work. I would like to do some further readings about this method but he doesn't specifically give the method other than a ...
4
votes
1answer
39 views

Linear regression: Evaluate probability of $Y>y| X=x$

Given a linear regression model with all the assumptions checked and validated, I would like to obtain the probability that $Y>y|X=x$. For example for the iris dataset, I would do the following to ...
2
votes
0answers
40 views

creating contrast matrix (limma) for two factorial in R

I am attempting to construct a contrast matrix that I can run in R, using the limma bioconductor package, but I am not sure that I have coded the contrast matrix correctly. A previous post and the ...
2
votes
1answer
43 views

Positive linear regression coefficient

I am trying to use R to find the optimal solution for my problem with positive coefficients. Here are my data: ...
3
votes
1answer
152 views

Analysis of the Residuals vs Fitted

I have a model for which I gathered 10 observations from each person, a total of 25 people, then 250 observations. Well, this is part of my summary of the model, ...
7
votes
1answer
106 views

Linearized exponential regression by lm() vs. non-linear nls() regression

Disclaimer I am new to this site, relatively new to R (two weeks of learning), have just a really basic knowledge in statistics so sorry if I'm doing a dumb mistake there or asking bad question or ...
0
votes
0answers
22 views

Three-way interaction in LMM: Can I write interpretation in this way?

I have a couple of questions regarding to the interpretation of LMM: Question 1: If I have three independent factors and two of them have three categories, I will have to run at least four models to ...
3
votes
1answer
59 views

What happens in linear regression when observations are not independent in time

Let's consider the example of whether a person's weight is correlated with their height (simple linear regression). What is the difference between running this correlation on the following two data ...
-1
votes
1answer
52 views

Code for conditional linear regression [closed]

I've just run a linear regression on an entire data set, but now I need to run the regression with data just from females within the data. Females are denoted under the ...
1
vote
1answer
47 views

Can univariate linear regression be used to identify useful variables for a subsequent multiple logistic regression?

Does the $R^2$ (or some other statistic) from a univariate linear regression tell me anything about how it would work in a logistic model? What if I normalized the data to mean zero? I'm doing ...