This tag is a signal that the question focuses on a problem particular to multivariate analysis, such as multiple correlations or interactions.

learn more… | top users | synonyms (1)

0
votes
0answers
12 views

Not normally distributed dependent variable in moderator analysis

I have been running a multiple moderator analysis in a pretty simple model. X are Google search queries normalized to 0-100. Y are the new registrations of cars in one country and one moderator. ...
0
votes
0answers
11 views

Under-forecasting in Regression

I have to do forecasting of sales that is how much sales of a product is going to happen in a particular store. I have time series data for last two years and doing forecasting for 2014. The variables ...
0
votes
1answer
36 views

Importance of intercept term in regression equation

What is the actual importance of an intercept term in a regression equation? If regressors include variables like age, height, weight and gender(1= male, 0= female), should the equation have an ...
1
vote
2answers
39 views

Probability of event happening when data is aggregated with many independent events over the course of time

Let's say you have $X$ coins, each with a differing probability of landing heads (e.g. coin 1 has 10% chance of landing heads, coin 2 has 20% chance of landing heads, etc.). Now, let's say that you ...
0
votes
1answer
13 views

adjusting batch effect by multiple linear regression

I am analyzing rna-seq data in the format of counts. There is batch effect revealed by PCA. One method I tried called RUVseq, it estimated the variation basing on control genes, and then added it to ...
2
votes
0answers
14 views

How to use one variable in regression with many independent variables in lm() [migrated]

I need to reproduce this code using all of these variables. ...
0
votes
0answers
43 views

Determining Relative Weights

I am looking for some recommendations and more specifics about how to do the following: Objective: To determine the weights of a number of stock valuation metrics. I am looking at doing this across ...
0
votes
1answer
37 views

Why the Breusch-Pagan rejects H0 on apparently non-heteroskedastic data?

Breusch-Pagan rejects the H0 on this residuals: ...
0
votes
1answer
22 views

How to format multi-row time series data for LIBSVM regression

I would have expected this to be covered in detail by the LIBSVM tutorial but after hours of wasting time googling for answers I've had to throw in the towel. What I am trying to do is rather trivial ...
-4
votes
0answers
92 views

Exploratory regression analysis for data with missing values

Recently I have performed an exploratory regression analysis, using lavaan R package and observed the following output with some warning messages in it. I have the ...
0
votes
0answers
38 views

Statistical Analysis help for thesis - Correlation, Probit, Tobit and Moderation

Hello CrossValidated users! I am writing here cause I need some guidance on my statistical analysis which has turned out far too complex for my basically begineer stats skills and my self research. ...
2
votes
0answers
37 views

Degrees of freedom of J-Test distribution

This book states, on page page 256 ( the GMM section) that the J-test(for over-identifying restrictions) is of the following form $G_n'J_n^{-1}G_n \approx \chi^2(m-p) $, where $G_n=G_n(\theta)=1/n ...
0
votes
0answers
23 views

is there a difference between semipartial correlation and regression coefficient in multiple regression?

I am preparing a presentation about multiple regression. Most of my sources seem to equal unstandardized coefficients in multiple regression with the semipartial correlation of that IV with the DV. ...
0
votes
1answer
18 views

How to blend multiple time series models?

I have three different linear, multi-variate time series models with a best fit against the same observed value $Y$ at 1 minute, 3 minutes and 10 minutes horizons respectively. Each model is using ...
0
votes
1answer
70 views

How can a regression be significant yet both predictors be non-significant? [duplicate]

I know this question has been asked in a slightly different form here. But my question differs and because of the forum rules I can't post on that thread. I have 2 independent variables, n=32, highly ...
0
votes
1answer
39 views

How to run main effects and interactions in a stepwise regression?

I am using multiple regression with the backward elimination method. I have one control variable (social desirable responding) and four predictor variables (gender and three self-esteem constructs). ...
0
votes
0answers
24 views

How to interpret Dickey Fuller (DF) test results in R (for unit test)

I read 1) Intuitive explanation of unit root and 2) http://www.r-bloggers.com/unit-root-tests/ for doing unit root test. I have basic questions: 1)should I check for unit root on both 'x' and 'y' ...
0
votes
1answer
46 views

problems to build the linear multiple regression in R and to explain the output [closed]

I have a problem to build and to explain the linear multiple regression. I have a data set called Cars93 with 26 variables (numeric and not numeric) and 93 ...
4
votes
1answer
92 views

Regression where the dependent variable is the difference between two correlated variables — bias and other issues to consider

I am interested in estimating a regression that looks like this: $(x_{1,i} - y_{i} )_{i} = x’_{i}*\beta + \epsilon_{i}$ (1) However, I am not sure if doing this—in this form—is appropriate. ...
0
votes
0answers
21 views

Is there a way to model the error term in a linear regression with R?

I'm tryin to estimate a pretty basic regression. I have a dataset containing x and y and would like to esimate the following ...
0
votes
0answers
31 views

piloting a multinomial logistic regression model

I have 11 variables in my data set. farmers Group(1,2,3,4 this is my dependent variable) Independent variables Total holding ,Crop area , barn capacity.....and barn capacity extent match and YPH. ...
1
vote
1answer
41 views

Can I compare two regression coefficients

I am comparing treatment outcome to two therapeutic treatments. Specifically, I am looking at how attachment moderates the relationship between therapeutic alliance and outcome. I hypothesize that the ...
-2
votes
0answers
16 views

polynomial regression model [closed]

Please, could you help me in answer on my question which it is as follows: I have 7 factors with 3 levels and no. of experiments are 27 tests, Can I build polynomial regression model with these data ...
0
votes
0answers
10 views

Count variable as control variable in regression in SPSS

I'm doing a research on development of audit fees in 2005-2012. I'd like to see if there's a downward or upward trend in them. I have made a count variable of the years (2005=1 2012=8) and now should ...
1
vote
0answers
32 views

How to do multiple regression with within-subject independent variables using SPSS?

I have two groups of subjects, both undergoing two experimental conditions. During the experimental conditions, the subjects take questionnaires for evaluation of the mood states, and blood pressure ...
0
votes
1answer
27 views

Multlinear regression: analysis of residual of transformed response and predictor variables

In the first step of modeling a regression equation I came up with the following model: $T_c = 26.73 + 0.042{\rm Sc} + 0.247{\rm Lc} - 14.709{\rm Lf} + 1.41{\rm Lu} - 0.214{\rm Fc} + 0.041{\rm Ad} - ...
0
votes
0answers
10 views

Interaction between percentage and population size in a regression

Assume I have a regression type model. Are their any statistical and/or substantial reasons NOT to include a two-way interaction which consist of a percentage and the corresponding population size. ...
0
votes
1answer
43 views

Hypothesis Testing using dichotomous and 7-point Likert scale

I am working on testing the following hypothesis: H1: People who are worried about their health will consume yogurt H0: People who are worried about their health will not consume yogurt I have ...
3
votes
0answers
29 views

Partial regression plots vs scatter plots for checking linearity

In a multiple linear regression analysis, what is the most suitable plot for checking linearity? I have seen a number of examples that use scatterplots as a preliminary test to use a linear model. ...
0
votes
0answers
32 views

$R^2$ equal square of sample correlation [duplicate]

I?m having a hard time proving that $R^2$ is equal to the square of the sample correlation between $Y$ and $\hat{Y}$. Every book I search tells me that's very easy, like verbeek. They just state that ...
0
votes
0answers
38 views

F test to compare regression coefficients across different groups, using R

Imagine the following type of dataset: I have a dependent variable Y, two independent variables X and Z, and a variable that can separate the dataset in two smaller datasets. I estimated 2 ...
0
votes
1answer
30 views

correct use of Negative Binomial with a Geometric distribution in a mixed model (glmmPQL)

I am trying to fit a NB GLMM with a gemoetric distribution. I have come across very little information on this form of regression. And would like some pointers/reasurance. some literature is ...
0
votes
1answer
26 views

Interaction model significant in multiple linear regression

This question is not about interaction effect, but about interaction model in Hierarchical Linear Regression. I have 1 DV and 5 IVs. I want to see which of the IVs is significant predictor of DV. ...
0
votes
0answers
10 views

split file in weka

I want to run a logistic regression were the file is split on a certain value, namely key (major and minor) and I want to see if the other values are able to predict the ranking of a song. But I have ...
0
votes
1answer
54 views

How to determine which variable or combination of the variables are affecting to the predictor variable?

I have one dependent variable name as "win ration" of the deal contested and more than 30 independent variables, all are categorical variable name as role of the customer, geo, region, and 27 ...
1
vote
1answer
43 views

Interpreting orthogonality

In a multiple partial linear regression setting, the book I'm reading has this sentence: «As a consequence of the fact that residual are orthogonal to explanatory variables, the 'cleaned' variables ...
0
votes
1answer
19 views

model specification problem

I have the following model that I would like to rebuild: $Y_{i,t}=a+bx_{i,t}+cx_{i,t-1}+e_{i,t}$ I' am wondering now whether this is the same as the model above: $Y_{i,t}=a+ d\Delta ...
2
votes
0answers
27 views

What kind of analysis gives you the statement "If you DONT reach X amount by time T, then your chances go down by P percentage?

I am trying to model growth for data I have regarding downloads of applications. I would like to make a statement, if you "DONT reach X amount of downloads by time T, then your chances of reaching 15 ...
0
votes
1answer
25 views

Which predictors to include in multiple regression where predictors are more than number of samples?

I have 3000 predictors (biological measurements) and 200 samples (weight), and would like to build a linear multiple regression model. How do I select which predictors to include in the regression ...
0
votes
0answers
29 views

How can I use the sufficient statistics (variances, covariances, means) to estimate a linear regression model in R?

My question is simple: is there a function in R which estimates the linear regresion model in a similar fashion as lm, but only ...
0
votes
0answers
43 views

Multiple regression analysis

I have a data set based on 14 field sites, the dependent variable I am investigating is count data recorded at each site (on 3-5 visits). The independent variables (I have around 45) are fixed at ...
0
votes
0answers
26 views

Estimate linear regression using items randomly selected from an item pool

I am asking this question against the background of a linear regression with single predicted variable $Y$ and multiple predictors $X$. $X$ comes from a survey using an "item pool" which suggests that ...
1
vote
1answer
20 views

Assumptions of two way anova

Please tell me what the actual assumptions of a two way anova are. I read somewhere that this being similar to multiple regression, the only assumption is that the residuals are to be normally ...
0
votes
0answers
10 views

Mediation and moderation in labeling

So I'm looking at student stigma affecting self-esteem. The overall idea is that stigma affects student achievement through the mediator self-esteem, so I will do multiple regressions and test for ...
0
votes
0answers
36 views

Neural-Net style pattern recognition with an unknown/varying number of inputs?

Say for example I had a weighted graph such that each node had an associated value. The nodes' values are given by some function of the edge weights and the number of edges as well as the node's ...
0
votes
0answers
26 views

Multivariate multiple moderated regression

My Dissertation analysis method involves a Multivariate multiple moderated regression. Where I have 4 Dependent Variables (DVs) and 4 Independent Variables (IVs). My model 1 is testing main effects ...
6
votes
1answer
125 views

Follow-up question: When should you center your data & when should you standardize?

I have a follow up question to MånsT's reply to the "When should you center your data & when should you standardize"-question. ( I cannot leave a comment as I am below the magic "50 reputation".) ...
0
votes
0answers
29 views

How to use factors (from CFA) as independent variable in Regression Analysis

I calculated 4 factors as latent constructs in a Confirmatory Factor Analysis (I use AMOS). Now I am wondering if it is possible to extract some kind of a factor score like I know it from Exploratory ...
2
votes
1answer
38 views

Non normal residuals in multiple regression

I used height, weight, gender and age to regress on BMR (basal metabolic rate) and obtained the following qq plot of residuals I then regressed the above variables with log BMR and obtained the ...
2
votes
0answers
37 views

What is the best way to simulate data for a linear regression model?

I am concerned with simulating data for a linear regression model. I need to control the means, variances, and correlations (covariances) between the predictors and the criterion variable. In ...