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

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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
15 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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6 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
19 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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830 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
10 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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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
25 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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15 views

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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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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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 ...
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9 views

Combine insights from linear regressions on multiple datasets with different outcome ranges

I have a number of datasets with the same (categorical) features/predictors and the same numerical outcome variable. I am studying the effect of each of these features on the outcome. The difference ...
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10 views

Imputation of predictors missing data for logistic modelling

I never used imputation of missing data and I would like to understand the effect of imputation in a specific scenario. Lets suppose that I have a dataset whit some predictors variable and one binary ...
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1answer
16 views

Hypothesis Testing Multiple regression with dummys and interaction

I have a model $$Y_i = \beta_{0} + \beta_{1} *P_i + \beta_{2}*B_i + \beta_{3}*E_i + \beta_{4}*B_i*E_i+u_i$$ where Y is a Rating, P is the price, E is the educationional level ( 0 no higher education; ...
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How to determine if a (binary) variable has a statistically significant effect on a response variable?

To be more specific, assume the following scenario: A customer advertising for a job on your site wants to know whether buying an extra ad-product or not will increase the number of applicants. You ...
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Why lower order term is necessary when higher order term is compulsory [duplicate]

I came across a result today stating that if the coefficient of the highest order of independent variable in the regression equation comes out to be necessary (via hypothesis testing), then the lower ...