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

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Feature selection using cross-validation and multiple linear regression

I would like to predict discrete (mean) values from discrete (mean & std) values of the extracted features. My question is how you should perform feature selection when you are applying K-fold ...
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10 views

Constructing a multiple linear regression model

I have some covariates to consider adding into my model and I have to decide which to choose and think of possible transformations. Would this be a decent method of variable selection: I construct ...
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1answer
15 views

Variables in the regression

Would it be correct if the outcome variable is at the household level, to use variables at both household and household member level as regression explanatory variables? E.g. if the outcome variable ...
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12 views

How to choose between data-driven pattern or intuition?

I am performing a multivariate logistic regression (This could very well be any other kind of regression method) to study the effect of some predictor variables on the probability of event. I have ...
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29 views

Looking for the best data analysis

I am currently in my dissertation phase and I am stumped on what data analysis I should be using. I am looking to evaluate resilience differences in genders. I'm using a non-experimental, causal ...
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30 views

Can we solve multiple linear regression using simple linear regression solver?

Suppose I have a blackbox function that solves simple linear regression. Can I use this function to solve "multiple" linear regression? The blackbox computes the slope and intercept in a simple ...
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1answer
16 views

Predictive Analytics, Rare Outcome, Multiple Regresson [on hold]

I'm looking at a data set with ~50,000 observations, ~800 outcomes of interest, with ~30 dichotomous (yes/no) variables for each observation. My goal is to create a predictive rule that will predict ...
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24 views

Why do t statistics decrease (standard error for cofficient increases) when multicollinearity exists?

Can anybody show how the t statistic decreases when multicollinearity exists? It is easy to prove using the F test, but I don't know how to prove it using the t test.
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7 views

Creating a meaningful metric from a multiple regression residual

I want to show a scatter plot and accompanying best fit line for a regression equation I am using for, say, number of cigarettes smoked (x-axis) predicting visible skin blotches (y-axis). A ...
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9 views

Partial Correlation - Quantifying the effect of removing confounding variables on the correlation

I'm conducting partial correlation in order to quantify the association between two variables, X and Y, after the effect of a set of confounding variables Z has been removed from both X and Y. In ...
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9 views

Mult regression: Dealing with assocation between a dummy IV and a continous IV

I'm running a multiple regression and I'd like to include two variables related to online reviews: one for review count and one for avg review score. The complication is that about half my sample ...
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1answer
21 views

Methodology: breaking multi-regression apart

I have to perform a multiple regression where my independent variable is store visits, while the dependents include hour of day, day of week, and others. I need to do this in Excel. Excel limits ...
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20 views

Critical point for 'hat values' in a normal linear model

I have a normal linear model $y=X \cdot \beta$, where $\beta$ is a $13$-dimensional vector. I want to see if there are any points (I have $287$ data points in total) I can throw out. I want to use hat ...
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833 views

Why does this regression NOT fail due to perfect multicollinearity, although one variable is a linear combination of others?

Today, I was playing around with a small dataset and performed a simple OLS regression which I expected to fail due to perfect multicollinearity. However, it didn't. This implies that my ...
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1answer
11 views

What kind of assumptions do I need to test when running a fixed effects panel model?

I am running a regression analysis on a panel data set. The Hausman test and the logical setup of the research question indicate that a fixed effects model would be best for running the regression. ...
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19 views
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20 views

multiple linear regression with factors

I am conducting a multiple regression analysis on a number of variables to determine the dependent variables. However, some of these variables are factors (have entries 1 or 0). If general ...
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3 views

Multinomial logit model and Principal component analysis

I am trying to estimate if a household owns a PC, has access to PC or none. I have very large and rich dataset on household data. In the data set I don't have income variable so I was thinking to use ...
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14 views

Geometric interpretation of MLR

$\vec{y}=\mathbf{X_{n\times p}}\vec{\beta}+\vec{\epsilon}$ We know that $\hat{\beta}^{\text{LSE}}=(X^TX)^{-1}X^Ty$ If all the dimensions are orthogonal, we can obtain that $\beta_j=\frac{\langle ...
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7 views

Choose between models FE vs RE vs Pooled OLS

I have quarterly data for 3 countries for a period of 10 years. Number of observations = 123. I have the following two questions: I would like to know what tests should I perform to choose between ...
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31 views

Best linear fit [duplicate]

Which one is the best fit according to these information? fit1 or fit2. I am NOT working with this in an academic context so explanations are not important to me.
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1answer
61 views

compare two linear models. Linear regression

I have made two linear regressions to estimate y and I get this results: One: ...
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2answers
26 views

Using a dichotomous categorical variable that has an underlying continuous dichotomy in a multiple regression?

FINAL EDIT I just found a good answer to this question in another thread in the forum here, therefore, I think this question could be closed. Thanks so much for your help and the clarification about ...
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9 views

which technique will be appropriate for my problem

suppose i have three independent variables x1 x2 x3 and one dependent variable y with following defination x1=test1 scores (20% of final score) x2=test2 scores (10% of final score x2=test3 scores (30% ...
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2answers
58 views

Does cross-validation on simple or multiple linear regression make sense?

Does it make sense to apply train-test split or k-fold cross-validation to a simple linear regression model or multiple linear regression model? I'm really confused about this because I saw this ...
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21 views

Vif and stepwise regression

I used the VIF to detect multicollinearity, I want to use forward selection and backward elimination procedures. My question is: Do I have to use all the variables in my dataset in the procedures or ...
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1answer
16 views

Regression with multiple dummy variables and dummy interactions

I have a model measuring Click through rates using 3 dummy variables. Placement location (PL1 vs. PL2) Ad type (Text vs. RM) Device type (Mob vs. Desk) Additionally I want to measure the ...
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Full conditionals for the parameters of a Bayesian regression with dependent components

Let $\mathbf{y}_i=\{y_{ij}\}_{j=1}^p$, $i=1,\dots ,n $ be a $p-variate$ vector and $$ y_{i,j} = \alpha_{j}+\beta_{j}x_{ij}+\epsilon_{ij}, $$ where $x_{ij}$s are known constants and ...
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38 views

LASSO regression when model is known

I am very new to regression as I have been reading "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" by Hastie et al. on Standford's website this weekend. My goal is to ...
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47 views

Constrained regression and lagged factors

I have a model that I need to estimate, where I've seen a similar example (Constrained Regression in R: coefficients positive, sum to 1 and non-zero intercept) but without the second part of ...
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20 views

What are the statistical properties of regression when a regressor is used as the dependent variable?

If you have a data generation process y=f(x)+ϵ and mistakenly regress x on y will your estimate be consistent for f inverse? Will your estimator be less efficient then if you had chosen to regress y ...
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11 views

how to calculate adjusted sum squares for each predictor in a multiple linear regression model?

I don't understand why the sum of adjusted sum squares of each predictor(0.0979+9.08723=9.1851) don't equal the total regression sum of square(11.7778)? And I know how to calculate sum of adjusted ...
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16 views

Linear model in R. Basics; how to read and understand the table [duplicate]

I am asked to fit a linear model for some data that my teacher has given. The first 5 lines of the R code is given by my teacher. X1 has 4 kind of outcome (1, 2, 3 and 4) and x2 has 6. I believe I ...
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1answer
48 views

Multiple regression in R results different to simple linear regression? [duplicate]

Hi I am having trouble acquiring the final results for presentation. The results from a multiple regression are different to my results in a simple linear regression. For example, the multiple ...
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11 views

what to do when proportional assumption is not met in stata

I have built a Cox multiple regression model. When I tested the proportional hazard assumption using estat phtest command, I realized the PH assumption is not met. In this case, what is the next step? ...
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5 views

Interpretation of diagonal detail for a 2D (Haar) Wavelet Transform

I am a statistics grad student, and I have just began exploring the topic of wavelet regression (specifically, Haar wavelets for discrete functions). I understand the generalization from a one ...
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5 views

Picking a particular model from regsubsets

I ran regsubsets in r from the 'leaps' library. I have gotten some 16 models in their order of which is best according to certain criterion. How do I select, say, model no.14 from this order and run ...
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7 views

Multiple regression with 5 continuous IVs and one continuous moderator in SPSS

I am trying to test a multiple regression model with 5 continuous independent variables and 1 continuous dependent variable. Also, I'm trying to test the moderating effect of 1 continuous variable on ...
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18 views

interpretation of CAPM test

the CAPM is the Capital Asset Pricing Model. If i test the null hyphotesis that all intercepts of a multiple regression (where the dipendent variable is a equity and the indipendent is the market ...
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15 views

Low variation in dependent variable

I want to study a setting where my dependent variable has quite low variation. Specifically the dependent variable in the regressions that I want to run, is a dummy variable which takes the value of 1 ...
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1answer
28 views

Test on SUR model

I have a SUR model with 22 equations, where each equation has the same 7 factors. I want to test if a coefficient (b3 in equation 1) is significantly different from another coefficient in another ...
3
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1answer
78 views

Data space, variable space, observation space, model space (e.g. in linear regression)

Suppose we have the data matrix $\mathbf{X}$, which is $n$-by-$p$, and the label vector $Y$, which is $n$-by-one. Here, each row of the matrix is an observation, and each column corresponds to a ...
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1answer
32 views

PROCESS macro in SPSS - Analysis for multiple IV?

I have a moderated mediation model with 4 IVs, 1 mediator, 1 moderator, and 1 DV. I can't find the right model in the templates provided by Andrew Hayes for his SPSS PROCESS macro. Can I carry out one ...
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1answer
31 views

p-values of the coefficients or AIC for model selection in multiple regression

I´ve got two models from a multiple linear regression (A and B, see below) and don´t know which to select. I want to predict a value called AW as good as possible, so I´d like to have the highest r². ...
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1answer
23 views

An independent variable in the logistic regression has 2.2%, 2.1% and 95.7% distribution [closed]

I have one independent variable in the logistic regression with a 2.2%, 2.1% and 95.7% distribution (three categories IV). My DV has good distribution (68% and 32%). How would this IV affect my ...
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Multiple regression - A question about dependent variable

I have a doubt about hierarchical regression analysis. My dependent variable is Presenteeism (the act of attending work while sick). Originally that variable has 4 levels (Has it happened to you ...
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20 views

Adding variables in multiple linear regression

Pretend that I had a MLR consisting of $Y=a+b$ and I wanted to add a new variable $c$ or $d$, but not both. So I would either end up with $Y=a+b+c$ or $Y=a+b+d$. I know I can run the regression on ...
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24 views

Dropping the non-significant parameters in multiple regression

Given the standard multiple linear regression model $$Y=X\beta+\epsilon\sim N(X\beta,\; \sigma^2I)$$ One derives the distribution for the estimated parameters $$\hat{\beta}\sim N(\beta,\; ...
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20 views

Smaller p-values on a selective multiple regression analysis [duplicate]

When I run a multiple regression analysis in Excel on 20 independent variables and 1 dependent variable (in two goes), I obtain in the summary a set of p-values. When I select the (six) ones that are ...
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13 views

Error measure in regression task by using a neural network

I am working on a regression task in which I which I want to predict vectors of about 30 values starting from textual documents using a Convolutional Neural Network. In particular, for each document ...