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

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Interpreting results of multiple regression with two categorical predictors

I ran a multiple regression model with 2 categorical predictors: X1 and X2, each getting values 0 or 1 (two factors with two levels each). Both variables were statistically significant. The ...
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25 views

Linear regression scaling independent variables

I am trying to do a linear regression. My $y$ variable is typical pretty small approx 0 to 0.3 I have some $x$ variables (regressing them individually on $y$ to start with) though that are very ...
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Regression analysis or Structural Equation Modelling

I have a 26 items questionnaire refined by running Common Factor Analysis. As a result, I got 4 factors (f1, f2, f3, f4). Each factor is measured by 6-7 items in the questionnaire. These four ...
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1answer
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How to apply diagnostics to regression model from FactoMineR

Many diagnostics to assess regression models are listed on this page: http://www.statmethods.net/stats/rdiagnostics.html ...
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13 views

Standard multiple regression and multiple dependent variables. and bonferroni correction?

I have a question regarding running multiple regression equations with multiple dependent variables. Three independent variables and 3 dependent variables. A colleague advised me to conduct three ...
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11 views

Comparing models using coefficient of determination

What guidelines are there for when we want to compare different models using $R^2$?
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162 views

Why not robust regression everytime?

Examples of this page show that simple regression is markedly affected by outliers and this can be overcome by techniques of robust regression: ...
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1answer
28 views

Logistic regression model: interpretation of average marginal effect

This is more a beginner question but I am having trouble finding helpful information. Could someone explain to me how to interpret the "average marginal effects" of independent variables from a ...
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77 views

Interpretation of continuous variable in dummy-continuous interaction

Similar questions have been asked before, but all of them focus on the dummy or interaction term. Say run an OLS regression on the model: $\ln( housePrice )= \beta_1 \times pollutionLevel + \beta_2 ...
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49 views

Multiple Linear Regression coefficents

I'm doing a linear regression, in R. The values are like this - ...
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50 views

multiple regression advice on results

I am testing to see if there is a relationship between my dependent variable Y and any of 4 explanitory variables x1, x2, x3 or x4 First I started by doing simple linear regression and got these ...
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1answer
18 views

ANOVA Table for Model In R

I'm trying to figure out how to produce an ANOVA Table in R for a multiple regression model. So far I can only produce it for each regressor, and the Mean Square is calculating as the same as Sum Of ...
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Do you have any information about regression model and logistic regression notes [closed]

Please share the link or any source for statistics notes to refer for in-depth study.PDF is also welcome.I want specially Regression Model,Machine Learning & Logistic Regression notes.
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15 views

Standardized Coefficients and Partial r

Ive looked over a few of the posts here on regression coefficients and partial regression coefficients but haven't seen an answer to this question, which maybe easily inferred from the other answers ...
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1answer
18 views

Why is not overlap in covariate distribution a part of model diagnostics?

I often compare groups that appear not to be comparable when viewing their descriptive data. For example, I frequently compare mortality risk (with Cox, logistic and Poisson regression) among ...
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If $aX_1+bX_2+cX_3=0$ then find the partial correlation coefficients

Suppose there are observations $X_{1i},X_{2i},X_{3i}$ with $i=1,2,...,n$. Suppose for every $i$, $aX_{1i}+bX_{2i}+cX_{3i}=0$ where $a,b,c$ are fixed constants of the same sign. Find the values of ...
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31 views

Choosing predictor variables (IVs) for inclusion in multiple regression

I am stuck on something and think I may have made a big error. My DV is ticket sales and I have 5 potential IVs: ticket price, income, review score, travel distance, and performance costs. I am ...
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Methods to tell which type of regression to be used

Given the total correlation coefficients between $X_1,X_2,X_3$, how can one determine whether to use Linear Regression or Multiple Regression when one is trying to predict $X_1$ from the others? If ...
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12 views

Changed signs from correlation to unique effect [duplicate]

I am running a fixed effects regression and I found that one of the significant independent variables has a substantial positive correlation with the dependent variable but a negative unique effect in ...
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1answer
33 views

How to find the significant variables in a multiple regression given the t-statistic and coefficient?

I know that significance can be identified using the p value. but if the degrees of freedom, t statistic and coefficients are given, how to identify significant variables? should the p value be ...
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1answer
86 views

I understand P-value but I am not sure about this question

What probability indicates the p-value? (more than one answer possible) a: The probability of a type I error. b: The probability of a type II error. c: The probability of rejecting the alternative ...
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93 views

difference between qt and qnorm

Can you tell me what is the difference between qt and qnorm? From my understanding, qt is used for small sample, and qnorm is use for large sample. Am I correct? If yes, how do I know whether my ...
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12 views

What statistics test can I use to make results more persuasive rather than just reporting M and SD?

I was recently given the task of reading and article and sort of critiquing it with the help of questions. I am from a psychology background but this journal is to do with communication and ...
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Impute missing data for mixed effects models?

Although I will not provide a reference, because I cannot recall where I did read it, I have several times read or heard that missing data is accommodated automatically in mixed models. Can anyone ...
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Establishing an empirical relationship among environmental properties using PCA and Multiple Regression

So, this post is a follow-up to a previous question of mine asked recently (Percentage of contribution of multiple factors to a single dependent variable), with more details on what I am trying to ...
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negative AIC or positive AIC? [duplicate]

I have calculated AIC by using R-studio to compare models. However, I got the following both negative and positive AIC AIC 8.52 0.41 -7.70 -5.84 -3.84 -2.10 Should I select the negative AIC or ...
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1answer
24 views

How to use factor analysis / PCA / regression for data having serial IV and DV?

I have data regarding effect of a food chemical on blood and urine levels as well as effect on blood sugar and cholesterol. So I have following variables: ...
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Multicollinearity and differences between independent variables

I am trying to estimate a model of attrition that takes into account characteristics of an individual's current job and characteristics of an individual's residency. Of particular interest is how the ...
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Dummy Variable Coding in Multiplicative Model in R

I saw this question online: http://www.actuarialoutpost.com/actuarial_discussion_forum/showthread.php?t=24196 "I am fitting a multiplicative model for insurance using R Cost = [X ^ B1] * [Y ^ B2] * ...
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sklearn.linear_model.RandomizedLogisticRegression : Handle Categorical Value [migrated]

I want to use RandomizedLogisticRegression for selecting variable for my data set. But the problem is that, One of the feature in my data set is Gender. So it's ...
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Coefficient of determination in the presence of a certain measurement error

In page 138 of Green's Econometric Analysis, we consider a simplified type of measurement error that allows the usual OLS estimator to be consistent. In the picture below that model is described. ...
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1answer
53 views

Percentage of contribution of multiple factors to a single dependent variable

I have a set of data (underway observation along cruise track) which includes one dependent variable A and three independent variables B, C, and D. It's known that A is related with each of the three ...
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1answer
14 views

MLP with 2 outputs vs 2 MLPs with single outputs for nonlinear regression

Assume i want to apply nonlinear regression to two output variables with multilayer perceptrons. Is there difference between using a MLP for each regression with single output and using a single MLP ...
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26 views

Multicollinearity and two variable with the same level of significance

I have a high value of correlation between 2 of my explanatory variables (0.79), and they are both significant at the same level exactly. Besides that they are both important to the model. The ...
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174 views

Consequence of Multicollinearity

In case of perfect multicollinearity the predictor matrix is singular and therefore cannot be inverted . Under these circumstances, the ordinary least-squares estimator $\hat\beta=(\Bbb X'\Bbb ...
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Transformation of regression model to estimate sum of coefficients

How can the model $y=\beta_0+\beta_1x_1+\beta_2x_2+e$ be transformed so that it estimates the sum of $\beta_1$ and $\beta_2$, that is, $\beta_1+\beta_2$ is a coefficient in the new model?
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drawing confidence interval graphs [migrated]

I've made regression model with 4 variables. And I have gotten the following regression equation $$ Y= 0.0761 - 0687X_1 - 3.46X_2 - 1.937 X_3$$ I calculated Confidence intervals for these four beta ...
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1answer
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Closed form solution for t-stats and p-values in multiple regression

I am trying to build a spreadsheet that will perform multiple linear regressions on a number of data series using the closed-form solution. It was fairly straightforward to write the solution for the ...
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53 views

Is it ok to have a unit root within an independent variable?

The Dickey-Fuller and ADF tests testing for a unit root in variables are very sensitive. Some econometricians have personally indicated to me that in some cases it may be acceptable to model a ...
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1answer
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VIFs and condition indexes give different answers about multicollinearity

For a multiple regression model, all the variables have p-values below 0.05. The p value for the whole model is below 0.05 as well. When I checked for multicollinearity, I got VIFs below 5 for all the ...
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When adjusting for X1, have we adjusted for X2, to the extent that X2 is related to X1?

I've just read Elizabeth Stuart's paper on matching methods (http://biostat.jhsph.edu/~estuart/Stuart10.StatSci.pdf), which I find very informative. She discusses propensity score methods and the ...
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41 views

Removing additive effect in Multiple Regression in R

I have this data set that I will used for my model ...
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1answer
40 views

Regression coefficient as weights

I am not good enough in Statistics. I have a data in which there are four variables and one response variable. Is there any way so I can use my regression coefficients as weights? I have one ...
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Separating the Intercept in many Dummy Variables in Multiple Regression in R

I did a multiple regression on a dummy variable using R about how much people will pay on a certain product. Given this variables and levels: ...
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Regression Model Data - Changing exponential data into linear data

I have some 20 year monthly economic data that for the first couple of years is growing at a linear rate then grows at a slight exponential rate then in the last few years takes on a linear shape ...
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1answer
51 views

Interpretation of interaction between a PC and a continuous predictor on a logical response

I'm using lme4 in R to test the effect of various continuous explanatory variables, some of which I've corrected for their collinearity using PCA, on a logical response variable. My optimal model ...
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2answers
35 views

Doing multiple regression without intercept in R (without changing data dimensions)

I am trying to calculate multiple regression in R without intercept. My data is as follow: ...
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40 views

Independent variables in multiple linear regression

I have a set of experimental parameters and my task it to find reasonable descriptors to describe them (chemistry). Since I've got descriptors, I checked Pearson correlation for each of experimental ...