This tag is a signal that the question focuses on a problem particular to multivariate analysis, such as multiple correlations or interactions.
26
votes
6answers
7k views
How can a regression be significant yet all predictors be non-significant?
My multiple regression analysis model has a statistically significant F value however all beta values are statistically non-significant.
All the regression assumptions are met. No multicollinearity ...
5
votes
1answer
1k views
How to choose between ANOVA and ANCOVA in a designed experiment?
I am conducting an experiment which has the following:
DV: Slice consumption (continuous or could be categorical)
IV: Healthy message, unhealthy message, no message (control) (3 groups in which ...
8
votes
3answers
8k views
Significance of coefficients in linear regression: significant t-test vs non-significant F-statistic [duplicate]
I'm fitting a multiple linear regression model between 4 categorical variables (with 4 levels each) and a numerical output. My dataset has 43 observations.
R gives me the following p-values from the ...
4
votes
2answers
1k views
How can adding a 2nd IV make the 1st IV significant?
I have what is probably a simple question, but it is baffling me right now, so I am hoping you can help me out.
I have a least squares regression model, with one independent variable and one ...
26
votes
5answers
8k views
When should you center your data & when should you standardize?
In some literature, I have read that a regression with multiple explanatory variables, if in different units, needed to be standardized. (Standardizing consists in subtracting the mean and dividing ...
0
votes
1answer
1k views
Not-significant F but a significant coefficient in multiple linear regression
I have a regression with two continuous predictors and one dichotomous predictor in Model 1 and two interactions of each of the continuous predictors with the dichotomous predictor in Model 2. The ...
0
votes
2answers
748 views
How to interpret inconsistent Beta values in different steps of hierarchical regression analysis?
I did hierarchical regression analysis on my data due to having moderation effects in my research model.
R2 increased from .695 in model1 (main effect only) to .734 in model2 (main &interaction ...
15
votes
2answers
478 views
In what order should you do linear regression diagnostics?
In linear regression analysis, we analyze outliers, investigate multicollinearity, test heteroscedasticty.
The question is: Is there any order to apply these? I mean, do we have to analyze outliers ...
2
votes
4answers
364 views
Why ANOVA/Regression results change when controlling for another variable
This question might be very basic, but somehow I don't understand this point.
Suppose initially I used a univariate regression equation such as
GDP=a+b*Income
...
18
votes
6answers
5k views
Is adjusting p-values in a multiple regression for multiple comparisons a good idea?
Lets assume you are a social science researcher/econometrician trying to find relevant predictors of demand for a service. You have 2 outcome/dependent variables describing the demand (using the ...
7
votes
2answers
694 views
How can I use the value of $R^2$ to test the linearity assumption in multiple regression analysis?
The below graphs are residual scatter plots of a regression test for which "normality", "homoscedasticity" and "independence" assumptions have already been met for sure! For testing the "linearity" ...
9
votes
2answers
2k views
How to deal with collinearity issue when performing variable selection?
I've got a dataset with 9 continuous independent variables that I'm trying to select between to fit a model to a single percentage (dependent) variable, Score.
...
8
votes
2answers
4k views
VIF, condition Index and eigenvalues
I am currently assessing multicollinearity in my datasets.
What threshold values of VIF and condition index below/above suggest a problem?
VIF:
I have heard that VIF $\geq 10$ is a problem.
After ...
6
votes
1answer
1k views
Why are rlm() regression coefficient estimates different than lm() in R?
I am using rlm in the R MASS package to regress a multivariate linear model. It works well for a number of samples but I am getting quasi-null coefficients for a particular model:
...
0
votes
2answers
1k views
How can you have a non-significant multiple regression model w/ significant predictors? [duplicate]
Possible Duplicate:
Not-significant F but a significant coefficient in multiple linear regression
How can a regression be significant yet all predictors be non-significant?
Significance of ...
13
votes
1answer
4k views
Multivariate multiple regression in R
I have 2 dependent variables (DVs) each of whose score may be influenced by the set of 7 independent variables (IVs). DVs are continuous, while the set of IVs consists of a mix of continuous and ...
6
votes
1answer
378 views
Should I run separate regressions for every community, or can community simply be a controlling variable in an aggregated model?
I am running an OLS model with a continuous asset index variable as the DV. My data is aggregated from three similar communities in close geographic proximity to one another. Despite this, I thought ...
6
votes
3answers
2k views
Adding both quadratic and interaction terms to the model affects significance
As part of an assignment, I had to fit a model with two predictor variables. I then had to draw a plot of the models' residuals against one of the included predictors and make changes based on that. ...
5
votes
1answer
701 views
Parallel lines on residual vs fitted plot
I have a multiple regression problem, which I tried to solve using simple multiple regression:
model1 <- lm(Y ~ X1 + X2 + X3 + X4 + X5, data=data)
This seems ...
3
votes
2answers
249 views
Improvement of regression model
I am just learning R. I have developed a regression model with six predictor variables. While developing it, I found the relationships are not very linear. So, maybe because of this the predictions of ...
4
votes
2answers
6k views
What is the correct way to test for significant differences between coefficients?
I'm hoping someone can help straighten out a point of confusion for me. Say I want to test whether 2 sets of regression coefficients are significantly different from each other, with the following set ...
1
vote
2answers
1k views
Factor analysis and regression
I have a question about how to interpret a regression analysis I did following a factor analysis.
I did prinicipal axis factoring (direct oblimin)
I got a 3 factor solution. The three factors were ...
0
votes
1answer
608 views
Where is the shared variance between all IVs in a linear multiple regression equation?
In a linear multiple regression equation, if the beta weights reflect the contribution of each individual independent variable over and above the contribution of all the other IVs, where in the ...
6
votes
3answers
292 views
How to model this odd-shaped distribution (almost a reverse-J)
My dependent variable shown below doesn't fit any stock distribution that I know of. Linear regression produces somewhat non-normal, right-skewed residuals that relate to predicted Y in an odd way ...
6
votes
1answer
3k views
How to present results of a Lasso using glmnet?
I would like to find predictors for a continuous dependent variable out of a set of 30 independent variables. I am using Lasso regression as implemented in the glmnet package in R. Here is some dummy ...
8
votes
3answers
239 views
Possible range of $R^2$
Suppose are three time series, $X_1$, $X_2$ and $Y$
Running ordinary linear regression on $Y$ ~ $X_1$ ($Y = b X_1 + b_0 + \epsilon$ ), we get $R^2 = U$. The ordinary linear regression $Y$ ~ $X_2$ get ...
8
votes
5answers
8k views
Acceptable r-square value for multiple linear regression model
I'm currently working on my thesis, more specifically I'm analyzing some data collected from researchers about the project's they're working on.
In the end, I'm have done a multiple linear regression ...
6
votes
2answers
392 views
Multiple imputation and model fitting
Multiple imputation is fairly straightforward when you have an a priori linear model that you want to estimate. However, things seem to be a bit trickier when you actually want to do some model ...
3
votes
2answers
4k views
Minimum number of observations for multiple linear regression
I am doing multiple linear regression. I have 21 observations and 5 variables. My aim is just finding the relation between variables
Is my data set enough to do multiple regression?
The t-test ...
2
votes
2answers
473 views
Multiple linear regression for hypothesis testing
I am familiar with using multiple linear regressions to create models of various variables. However, I was curious if regression tests are ever used to do any sort of basic hypothesis testing. If so, ...
1
vote
1answer
727 views
Independent variables prep in multiple linear regression
I have 3 independent variables A, B, C and want to run a multiple linear regression to predict Y. After studying the correlations between:
1) A, Y
2) B, Y
3) A/B, Y
4) (A-B)/(A+B), Y
It turns out ...
7
votes
1answer
105 views
Techniques for analyzing ratios
I am looking for advice and comments that deal with the analysis of ratios and rates. In the field in which I work analysis of ratios in particular is widespread but I have read a few papers that ...
5
votes
2answers
1k views
How to do a generalized linear model with multiple dependent variables in R?
I have six dependent variables (count data) and several independent variables, I see that in a MMR the script goes like this:
...
4
votes
1answer
263 views
Dealing with a categorical variable that can take multiple levels simultaneously
I recently posted a question with many parts and I'd like to focus in on just one issue that I didn't emphasize in the original post.
My data is a list of records, each one representing an ...
4
votes
2answers
186 views
Methods to predict multiple dependent variables
I have a situation in which I have $n$ observations, each with $p$ independent variables and $q$ dependent variables. I would like to build a model or series of models to obtain predictions of the $q$ ...
3
votes
1answer
248 views
AR1 and Law Of Iterated Expectations : No serial correlation
In the AR(1) model $y_{t}=\beta_{0}+\beta_{1}y_{t-1}+u_{t}$, assuming $E(u_{t-1}|y_{t-1},y_{t-2}...)=0$, how does the law of iterated expectations ensure that the errors must be uncorrelated: ...
3
votes
1answer
121 views
Am I interpreting this coefficient correctly?
I'm trying to get caught up in some notes, and the topic is pooled cross sectional data. If we have the regression: $$lwage_i=\beta_0+\beta_1married_i+\delta_0yr10_i+\delta_1married_i\times ...
3
votes
2answers
530 views
Multiple regression with binary predictors. Component value analysis
I have the data about process duration (in minutes) and components (procedures) done during it like this (CSV):
...
2
votes
1answer
101 views
Can ANCOVA disagree with multiple regression?
I have 3 categorical variables (CVa, CVb, CVc) all 0 or 1. Two continuous variables (IV1, IV2) are confounding my observational study. The multiple regression
...
2
votes
2answers
583 views
What happens when I include a squared variable in my regression?
I start with my OLS regression:
$$
y = \beta _0 + \beta_1x_1+\beta_2 D + \varepsilon
$$
where D is a dummy variable, the estimates become different from zero with a low p-value.
I then preform a ...
2
votes
3answers
333 views
Does adding more variables into a multivariable regression change coefficients of existing variables?
Say I have a multivariable (several independent variables) regression that consists of 3 variables. Each of those variables has a given coefficient. If I decide to introduce a 4th variable and rerun ...
2
votes
4answers
942 views
Univariate non-significant results becoming significant on multivariate analysis
I'm having a look at x-ray waiting times in an emergency department and how these relate to the day of the week and the time of day that requests are ordered. (want to know if patients have to wait ...
1
vote
1answer
601 views
Interpreting multiple regression coefficients with 2 continuous variables interacting and 2 categorical variables interacting
I find it challenging to interpet interaction effects in OLS multiple regression where the interactions are between two categorical variables and between two continuous variables.
Say for instance ...
1
vote
1answer
858 views
Factor scores vs. construct mean scores in regression analysis
I have 48 items in my questionnaire that represent 8 constructs. After conducting the exploratoty factor analysis (EFA) with PC method extraction and Varimax rotation method, 8 sets of factor scores ...
0
votes
3answers
171 views
Is it possible to have a variable significant in multiple regression but not significant in stepwise regression?
I have run a stepwise regression and found that some of the selected variables are not significant yet in a multiple regression with all variables included in the model those variables were ...
0
votes
2answers
58 views
Interpreting a weak model proceeding from there
I originally posted this question and since then I've posted another more specific branch-off question.
I've followed the advice I received in response to my questions, and I've done some statistical ...
-10
votes
1answer
461 views
Finding the best two predictor variables used conjointly, and levels of each
Twenty possible predictor variables in data set. One outcome variable.
Some of the predictor variables are not linear. So a standard linear multiple regression approach probably won't do. (And I ...
