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

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Predicted vs actual values of the dependent variable

My question is in what ways the predicted values of a dependent variable are more accurate than the actual values. The issue is that I want to investigate the effect of X on Y. In addition to X the ...
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9 views

Alternative to multinomial logistic regression for model with multiple related outcomes

I have been using multinomial logistic regression to answer a question analagous to: "which candidate will a person vote for, given particular demographic characteristics?". I am now looking to move ...
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17 views

Performing Canonical Correlation Analysis before conducting multiple regression

I have one dependent and five independent variables each having daily 10 years of continuous observations. I intend to develop a robust regression model using this data-set. Conducting multiple ...
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13 views

Mediation Analysis with many exposure variables

Does it make statistical/modelling sense if I would like to have a mediation analysis with many exposure variables (e.g. X1 and X2) but one mediator (M), where my main interest is about the causal ...
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19 views

What is the best way to extract time-series shared by two variables?

I have one dependent and several independent variables. I want to extract the time-series of the independent variable that is shared with the dependent variable. In other words, I want to extract only ...
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1answer
44 views

Assumtions behind simple linear regression model

If we are taking about simple linear regression model, that is, $y = X\beta + r$ where $y$ is a vector of size n x 1, $X$ a matrix of size n x p, $\beta$ the regression coefficient vector of size p x ...
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2answers
60 views

Computing R-squared change, F-, and p-values for the interaction / moderation term [on hold]

I would like to compute R-squared change for the interaction/moderation term in a multiple regression model, along with the corresponding F- and p-values. Previously, I have worked with the modprobe ...
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52 views

Marketing Data with many zeros

I am working on a marketing data which is a time series data with marketing spend done through different channels and revenue generated. The data looks like this : My data contains too many zeros ...
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54 views
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Difficulties obtaining valid predictions when using interactions

I examine long term trends (2003 to 2014) for a continuous dependent variable. I want to predict the mean each year in relation to income category. Income is arranged in quintiles, from 1 (poorest) to ...
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36 views

Weight shrinking in linear regression by L2 regularization

Quoting Prof. Bengio from his Deep Learning text (http://www.iro.umontreal.ca/~bengioy/dlbook/regularization.html), $ w = (X^{T}X + \alpha I)^{-1}X^{T}y $ We can see L2 regularization causes ...
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0answers
13 views

Eviews and Forecasting Linear Regression with AR(1) Error Term

This question is geared towards those who are familiar with Eviews and forecasting with linear regression in the case of AR(1) error terms. Consider the classical linear regression model where the ...
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7 views

Variables with low coefficients/high p-values in multiple linear regression highly correlated to significant predictors

This is possibly a very naive question: I am watching Hastie and Tibshiranie's class ("Introduction to Statistical Learning"), and at the end of this lecture, Tibshirani gives example of a multiple ...
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15 views

Self-designed objective for multiple linear regression

A multiple linear regression is to use several predictor variables to predict the outcome of a response variable, like the following relationship: ...
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1answer
43 views

How to use linear regression for heavily skewed purchase data?

I am trying to use multiple linear regression to predict the amount that a particular user will spend in a particular time frame on a particular site. Part of the problem is that there are very few ...
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1answer
23 views

Estimating error for parameters from multiple regression with linear constraints

I am working on a multiple linear regression problem where I would like to constrain only some of the parameters to non-negative values. There have been discussions of how to solve for the parameters ...
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0answers
37 views

Running Multiple Linear Regression in R with 3 Factors and 1 Continuous Variable

I am running a Multiple Linear Regression model using R. I am looking at travel behavior and most of the variables are factors with YES or NO as responses. However, I am concerned about using 3 ...
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1answer
21 views

Joint Hypothesis Testing in Multivariate Regression

I am fitting the following model to my data $y = \beta_0 + \beta_1 x_1 + \beta_2 x_2$ and the claim I want to be able to make is that $\beta_1 = 0$ and $\beta_2 \neq 0$. I am going to use ...
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0answers
31 views

Multiple regression with an unbalanced grouping factor

I have a question regarding multiple regression with an unbalanced grouping factor. Essentially what I am doing is an ANCOVA, but the interaction term ends up significant (which is interesting!) so ...
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1answer
60 views

What is this called?

We have several time series: $Y, X_1, X_2, X_3, ..., X_n$ The steps taken are: Regress $X_2, X_3, ..., X_n$ on $X_1$ to get residuals of each $X_{(>1)}$ Regress $Y$ on $X_1, r_{X_2}, r_{X_3}, ...
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2answers
106 views

How should strongly correlated covariates for logistic regression be treated?

I have to build a multiple logistic regression model with two strongly correlated covariates (predictor variables). How should they be treated? Am I to exclude one of them from the regression? There ...
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0answers
8 views

Missing at source data and predictive model

I have multiple sources of data, and each comes with its own set of observable cahracteristics. Most are common between all sources, but some sources have extra information that is useful, but not ...
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0answers
16 views

two stage? step? regression - passing covariance matrix

I received the following email from one of my colleagues/superiors this morning. I was hoping someone might be able to help me interpret the question and provide a response. I just started this job ...
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1answer
31 views

How to calculate the effect size of differences in groups using dummies in multiple regression?

I am running a multiple regression analyses on a sample of 1800 respondents. The dependent variable is the mean of a 5-point likert scale and I have 6 predictors (antecedents) also using mean of ...
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36 views

Sampling Distribution (Variance) of Weight Estimates

I am currently facing an issue regarding the sampling distribution of weight estimates. Problem Statement Given an estimate of a $n \times n$ covariance matrix $\hat{\Sigma}$ of $n$ random variables ...
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9 views

Fit multiple regression model with pairwise deletion (or on a correlation/covariance matrix) in R

I'm trying to fit a multiple regression model with pairwise deletion in the context of missing data. lm() uses listwise deletion, which I'd prefer not to use in my ...
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30 views

Gini Coefficient - Variable Importance Measure

There is a whitepaper for selecting important variables in a linear regression model. The URL of the whitepaper is http://support.sas.com/resources/papers/proceedings15/3242-2015.pdf . It explains ...
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12 views

Use SUR to measure unobservable effects?

My title is a littel bit ambiguous. I'm working on a project on measure the degree of sorting in housing market,put it simple, sorting refers to people with similar characteristics tend to live in ...
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21 views

Imposing restriction over some regressors in a linear model?

I have the following problem: Starting with a multivariate linear model $Y=\beta_1x_1+\beta_2x_2+\beta_3x_3+\beta_4x_4+\alpha_1x_5+\alpha_2x_6+\alpha_3x_7+\alpha_4x_8+\epsilon$ and $$X=\begin{pmatrix} ...
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19 views

Power analysis for regression with multiple mediators using bootstrapping (PROCESS)

I am currently trying to help a friend of mine with some statistical analyses for her master project, and she has come up with this question, which might be of interest to others (wel'll see) and to ...
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0answers
18 views

Application of Permutation Tests

I don't know how to post the question more formally. Therefore, let me introduce an example. Suppose you want to estimate the following regression: $n_i = f(x_i)+\beta \cdot 1[x_i = j]$, $n_i$ is ...
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12 views

Interpretation of Log-Lin models - % Contribution? [duplicate]

My question is as folows: I know that for log-lin models the coef express % ΔY = 100*(b*ΔX) The question is, could it also be interpreted as the % Contribution of X gave to Y ? For example, if y = ...
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1answer
27 views

Interpreting multiple polynomial regression coefficients

I read a couple post on interpreting polynomial coefficients here in cross validate however none of them touch on how to interpret multiple polynomial regression coefficients. Perhaps its the same but ...
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27 views

Interpretation of Generalized Inverse Gaussian regression with GAMLSS

Background on my project: I am comparing proteins (nodes) between a network representing protein interactions in metastatic patients v/s another network representing protein interactions in ...
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28 views

Can I use a “reach” variable as independent in a multiple linear regression?

I'm trying to model sales and I want to use publicity reach as an independent variable. The thing is, the reach from period A cannot be added to the reach of period B, $Reach(A\cup B) \ne Reach(A) + ...
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1answer
27 views

Why don't the following contrasts and linear models behave as I expected?

I am trying to understand how contrasts work, so I ran a small simulation using the following R code: ...
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1answer
30 views

Is it important to model heteroscedasticity during multiple regression?

Given a multiple linear regression (eg. using a GLS procedure) between a response variable and several predictive variables with different, heteroscedastic relationships with the response variable and ...
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0answers
18 views

Multiple Regression - Calculate Elasticity of demand

If I assume that all of my regression coefficients are statistically significant then how to calculate the point own price elasticity of demand for given price=8, Advertise=10, Income = 50? ...
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Choosing a method to solve a many-to-one mapping problem

Problem description To predict a list of values associated with a set of variables. Trainset Trainset has a set of variables X1, X2, X3, ... Xn. In the simplest form, each variable is of type ...
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27 views

Finding “partialling out” of regression

I have the following model $y= \beta_0 + \beta_1x_1 + \beta_2x_2+ u$ How can I find the "partialling out" interpolation of the regression estimator $\widehat{\beta_1}$, using the following formula: ...
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1answer
41 views

Using a Dummy Variable to Control for Great Recession data

I am looking to produce a forecast with a quarterly dataset of sales. I only have so many year's worth of data post-recession and I want to investigate including more datapoints, which would mean ...
4
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1answer
97 views

What is a partial F-statistic?

What is a partial F-statistic? Is that the same as partial F-test? When would you calculate a partial F-statistic? I'm assuming that this has something to do with comparing regression models, but I'm ...
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1answer
40 views

Estimating Price Elasticity

I'm having trouble coming up with a way to estimate price elasticity for my ticket sales data. Tickets go on sale 15 days before the event, and demand increases as the event comes closer. Prices stay ...
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1answer
35 views

Combining Forecasts

I am in the process of creating one well-rounded forecast, and in my research I found a few mentionings approving the use of several forecasts combined into one. I really like this idea but I have ...
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13 views

Attributing effect of independent variables on time series data

Consider a response variable, say, sales of a company. This variable is time series data (confirmed by using ACF and PACF plots). The sales depend on other variables such as price of the product, ...
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13 views

Interpreting scaled betas for quadratic terms in a negative binomial regression

I created a negative binomial model where the final model included 5 quadratic predictors (each with a corresponding linear term). I am considering two ways to interpret the beta coefficients for each ...
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0answers
13 views

Is it appropriate to compare two different estimation approaches using AIC?

validated folks, I've got a panel data regression model I am estimating using both random effects and a spatial maximum likelihood approach. Same regressors and DV, same data -- but different ...
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16 views

Penalized quasi-likelihood

Can anybody explain me why the function glmmPQL(.) in R behaves in different ways, depending on the number of ...
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0answers
16 views

Which statistical test to use with a continuous dependent variable and categorical and continuous independent variables?

I have a dependent variable (which is continuous) and it depends on a number of independent variables (both continuous and categorical). In order to find out which independent variables significantly ...
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25 views

Is it OK to divide the dependent variable and independent variable by the same scalar?

Specifically, if the dependent variable is analyst forecast revision, and the independent variable is earnings news, both divided by stock price, will this create spurious result? The reason to scale ...
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1answer
72 views

Regression performed using Principal Component Analysis

I have a dataset consisting of 10 correlated variables. I need to explain a response variable using these 10 variables, so I am using PCA to reduce dimensionality. Say, I use the first 3 components ...