Regression that includes two or more non-constant independent variables.

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How can the relative importance of a categorical variable in a linear regression model be determined?

A simple example can be seen here:http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm Gender is a dummy coded variable. I completely understand how to interpret this variable. I cannot use the ...
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1answer
61 views

How to deal with non-normally distributed residuals?

I'm fitting a multiple linear regression model. I've read that the residuals of my regression need to be normally distributed in order for the p and t values to be accurate. Now my residuals (see ...
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1answer
10 views

Can anyone give me some clarity on Q2 where PLSR is concerned?

Can someone describe to me in laymen terms what Q2 values mean with regards to PLSR I understand that it is a cross validation number, but i have no idea what it is saying. only that the model is ...
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0answers
17 views

finding relationships between variables in dataset [on hold]

I have a large project data set, which includes numeric values like dollar amounts, and non numeric quantities like country codes, purpose codes etc. I want to find relationships between the ...
2
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1answer
36 views

Multiple regression with correlated variables

I am trying to do multiple regression analysis of a continuous health variable (yval) keeping age, gender, height (cm), weight (kg) and waist (cm) as predictor variables for a database of about 7000 ...
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2answers
67 views

What criteria tell us that the prediction of a model is reliable

What criteria can be used to tell whether the prediction of a model will be more reliable than other specifications. Background: We have data with $N$ computers. However, prices available only ...
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26 views

Find distribution of Bus arrival time

I am currently working on a problem in my research which can be modeled into the following question: Let's say I have a rich dataset with values for the variable $A$ which is equal to ...
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0answers
29 views

Estimate Probability of being in a time interval

​You arrive at a bus stop in an unfamiliar part of town. Assume that buses arrive at the stop with an unknown (to you) distribution and wait in the bus stop for a few ​minutes. The wait time ...
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37 views

Prediction based on multiple time series - Python

I have 3 predictors and 1 variable that represents ground truth. They all are linked time series. My purpose is with the 3 predictors to try to forecast the ground truth data. For example : ...
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9 views

Adding a new factor to an existing factor model for stock returns

Let's assume I have a three factor model of stock returns (Cross-sectional model): $R_i = \alpha + \beta_{1,i} X_1 + \beta_{2,i} X_2 + \beta_{3,i} X_3 + \epsilon_i$ for $i =1,..,N.$ where ...
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26 views

Assesing the explanatory power of predictors, interactions and combination of terms

I have a model with 5 basic predictors and all interactions between the predictors themselves. Something like (I'm simplifying here, in reality I have many more variables): ...
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28 views

How to handle autocorrelated residuals (and independent variables) in multiple regression? [closed]

I am trying to carry out multiple regression for an air pollutant (dependent variable), and weather parameters- wind direction, wind strength, rain, air temperature, relative humidity (independent ...
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1answer
17 views

Test equality of coefficients in separate regressions when populations are not independent

I have three regressions with the same IVs. Equation 1: Y1 = X Equation 2: Y1 = X Equation 3: Y2 = X X is a vector of IVs with B1-B8 coefficients. Equations 2 and 3 are tested on the same ...
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1answer
48 views

Accounting for unknown error in multiple regression?

I have a very naive question about multiple regression and errors...one that isn't addressed here already (Choosing a robust estimator to account for measurement error in dependent variable) I would ...
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1answer
40 views

Selecting variables in multiple linear regression in R

Consider that we have a problem with 4 variables (y, x1, x2 and x3) and we want to do a multiple linear regression model. As we need to know which variables are the most important in the problem, we ...
2
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1answer
37 views

Do different units of explanatory variables affect p-values?

In multiple linear regression, does including variables with different metering units and thus values very different in dimension/size affect statistical significance/ p-values? In my model, a ...
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2answers
71 views

Should I remove non-significant variables from my regression model

I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up ...
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17 views

Migration Flows and Multiple Regression Quadratic Assignment Procedures

I'm a forth year economics student (undergraduate) and last semester I wrote a research paper, with a classmate, in which we analyzed the determinants of interprovincial migration flows in Canada. It ...
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23 views

Searching for the non-zero coefficients in lasso regression using glmnet.

I have to analyze genomic data set: ~ 22 000 of gene expressions for the two groups each of 40 subjects. I have tried different methods to find genes, which are significantly different among two ...
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15 views

Does multiple regression applied to panel data yield the same estimates as pooled OLS?

I'm trying to analyse a large unbalanced panel dataset. First, I simply used multiple linear regression and obtained feasible results (parameter estimates). Later, I was advised to use pooled OLS ...
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1answer
10 views

Multiple Regression study design - questionnaires

If an existing questionnaire does not exist to tap into a construct, is it better to change the instructions of a preexisting measure that isn't quite tapping into the variable of interest to have ...
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0answers
22 views

How does turning categorical variables into dummy variables affect ANOVA results?

I'm running a multiple linear regression with (amongst others) several categorical explanatory variables. My categorical variables are factors with several factor levels. For example, variable $x_1$ ...
2
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1answer
21 views

Does robust regression affect contributions to explained variance by the different variables?

I've learned that in multiple linear regresion, parameter estimates as well as R$^2$ are not affected by using robust standard errors, i.e. are the same as resulting from non-robust regression. I now ...
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4answers
226 views

Linear regression - is a model “useless” if $R^2$ is very small?

Given a complex output which depends on many underlying factors, I am given 3 explanatory variables and about 10K data points and the task to assess their impact on the output. The OLS model is very ...
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1answer
48 views

how to test the significance of a multivariate, multiple regression model as a whole

I have a dataset with seven dependent variables and three independent variables. Now I want to test the significance of a multivariate regression model as a whole. My model looks like this (using ...
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1answer
37 views

Is testing predictors separately theoretically sound?

I am running a regression analysis to understand the effect of several IVs on the transport mode choice of questionnaire respondents. My sample of respondents is of 100, and I have more than 10 ...
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1answer
27 views

Should you t-test variables before putting them in a multiple regression?

We are doing a research project looking at attachment's relationship to trust. We have measured possible confounding factors: age, relationship status and sex. We did t tests (categorical variables: ...
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33 views

Is there a difference between modeling Path Dependence and using a Lagged Dependent Variable?

I am modeling the choice of transport mode of a series of questionnaire respondents at year y. So far, I've resorted on modeling the choice as a function of a ...
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1answer
41 views

Can site be used as a factor in a multiple regression model?

I am studying two sites with different fertility and soil texture. In the model I used the data from both sites. I would like to know if could I could use the site as a factor (variable) in the model? ...
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40 views

Interpretation of output generated by PROCESS macro in SPSS for model with two moderators

I used the PROCESS macro for SPSS from hayes to regress a model where det_mean is the indepedent variable and y_tot the depending variable. I'm testing if this relation is moderated by two variables ...
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1answer
63 views

Time-series regression

Suppose that a typical firm determines its level of stocks $H_t$, in accordance with the following rule: $H_t - H_{t-1} = \lambda (H^*_t - H_{t-1}) + \epsilon _t$ where $\epsilon _t$ is a serially ...
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0answers
11 views

mat.regress/set.cor [migrated]

I am having a problem with the function mat.regress in the psych package that I was hoping someone would be able to give guidance on. I have a correlation matrix that is [76,76] with the first ...
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1answer
37 views

Do you think I should apply a transformation to my independent variables?

I have done a simple linear regression on my two standardized independent variables and standardized dpendent variable. In the residual plot there is a distinct quadratic pattern left after the two ...
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11 views

Largest value in a set with a nearly equal distribution between value +/- 20%?

I am working through a data analysis task in a contract, and trying to build a generalized spreadsheet that can be used for this and similar analysis. I'm tripped up by a requested procedure, and ...
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2answers
146 views

How to get ANOVA table with robust standard errors?

I am running a pooled OLS regression using the plm package in R. Though, my question is more about basic statistics, so I try posting it here first ;) Since my regression results yield ...
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14 views

Bagging for sparse survey data regression?

A colleague came to me with an idea for training a regression model from a very large but sparse survey data set. In this data set, many variables are available but most respondents only answered a ...
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13 views

high correlation between IVs, but VIF and tolerance normal?

In a multiple regression analysis: If two variables are correlated by more than .8, but the VIF for each of them are 1.005 and 1.006 and the tolerance numbers .994 and .995, what exactly does this ...
3
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1answer
54 views

What if a transformed variable yields more normal and less heteroskedastic residuals but lower $R^2$?

I am trying to decide whether to use a square root transformed dependent variable in multiple linear regression. Transforming $y$ leads to more normally distributed residuals and also to less ...
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2answers
165 views

Which residuals to analyse when dependent variable is transformed?

I am running a multiple linear regression where the dependent variable is sqrt-transformed. As far as I understand, the residuals from the regression are different from the residuals calculated as ...
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0answers
28 views

Regression sum of squares (SSR) when $\beta_0 = 0$ in multiple linear regression?

I hope this is not a duplicate but I cannot find the answer to this question. In a linear model $$Y_i = \beta_1 X_{i,1} + \dots + \beta_{p-1} X_{i,p-1} + \varepsilon_i, \qquad i = 1, \ldots, n$$ with ...
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0answers
9 views

Raw values vs SDS for linear regression?

I have data on age, gender, height, weight and a health variable (call it yvar) which is a continuous variable in the range of 50-150. The age is in the range of 5-15 years (a study of children). I ...
4
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1answer
31 views

Basic - Modelling Two Series, one is an index

I'm trying to model two time series. One is a seasonally adjusted # of new jobs number against and index of business development. Its been a long time since I took econometrics, so I'm hoping ...
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3answers
138 views

Transforming a variable when original variable does not have explantory power

Sometimes in multivariate linear regression, there will be one explanatory variable that does not contribute much in way of explanatory power. Then, we will perform a tranform on that variable, i.e ...
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29 views

How to calculate 3 predictors with 1 criterion by hand?

so we got this homework with 3 numeric predictors and 1 criterion. We have to calculate the 3 predictors (regression coefficients) B1, B2 and B3 by hand. It's easy to calculate a regression model with ...
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32 views

Multiple Versus Multivariate Regression

I have 4 IVs and 4 DVs and not sure if I have to use linear regression (one IV and one DV at a time) or multiple regression (the 4 IVs and one DV at a time)? Is there any application where I can put ...
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2answers
99 views

Predictor variables sum up to 1 but not necessarily correlated - is it a problem? [closed]

I am trying to fit hierarchical mixture model (using ML and MCMC, but this shouldn't matter) where the linear predictor part contains 17 independent variables. These are habitat variables: for each ...
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1answer
47 views

random forest modelling with high dimensional data

I am puzzling on developing random forest regression of high dimensional data. My predicted variable is plant cultivar or Class (say 1, 2, 3) and regresser are 82 variable in separate column (40 X 83) ...
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25 views

Robust Standard Errors(SE) estimators vs SE estimators assuming Conditionally Homoskedasticity [duplicate]

If both the asymptotic Variance-Covariance matrix estimators (robust and non-robust) are consistent to the same matrix, i.e., both will have the same efficiency (True?), then what is the advantage of ...
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36 views

To use Interactions or to split the sample?

I have panel data for 16 years and 134 countries. I am using fixed effects model and using following regression models: Model 1: $y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \epsilon $ Model 2: $y ...
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33 views

Can I use linear regression analysis for likert scale responses?

My dependent variable is measured on a 5 point likert scale and independent variable is measured on a 5 point likert scale. Is it appropriate to run linear regression analysis on such data? What if ...