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

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Longitudinal data analysis where meaning and metric of response variable varies over time

Determining what factors predict change over time is a topic of investigation in many fields and there are a variety of readily implemented methods for analysing repeated measures in the same metric. ...
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14 views

Multiple Regression with Predictors that Restrict other Predictors

I'm not even sure if the title of my question makes sense at first sight, so let me try to explain it. I'd like to fit a parametric multiple regression model to data. But depending on the value chosen ...
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Modeling a proportion using longitudinal data

To illustrate my question I'll make a (very) fictional example. I have a set of 17 year old people that every year report how many cigarettes they smoked and how many miles they ran. Very few of ...
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Multiple regression summary for different predictor classes

One question on regression: Following model: M1 <- lm(y ~ x1 + x2 + x3) x1 and x2 are in ratio scale, x3 is a nominal variable, they have values as x1= 1.4, 1.3, 1.2,... x2= 2.1, 2.2,2.3,.... ...
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26 views

Choosing weights for regression

I want to weight observations for a regression. I'm worink in R and I'm using the method lmrob from the package ...
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31 views

Hypothesis testing on multiple regression coefficients

In the simple linear regression setting, in order to determine whether there is a relationship between the response and the predictor we can simply check whether $\beta_1=0$. In multiple regression, ...
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Which type of Regression model do I need and what do I need to do to my variable to allow it to work?

thanks for taking the time to read this! A little background, I'm pretty enw to doing most statistical analysis; I've only ever done linear regressions and I tried doing research online but was ...
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29 views

Regression with dependent variable which ranges from -1 to 1

I performed a series of Pearson correlations which give me as expected values between -1 and 1 (actually very few below zero). I'd like now to see if some factors are linked to these correlation ...
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1answer
19 views

Evaluating goodness-of-fit in sequential multiregression [on hold]

I am doing a sequential multiregression analysis where I look at how four independent variables (porosity, permeability, sandstone thickness and sandstone depth) labeled $x_1, x_2, x_3, x_4$ influence ...
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Hierarchical Regression Moderator Analysis

I ran a 2-block hierarchical regression to test for moderation. In the first block I entered the centered IV and centered moderator variable (MV) and in the second block I entered my centered ...
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Calculation of coefficients in multiple regression [duplicate]

I am trying to calculate the coefficients of a linear regression using 2 or more independent variables. Case for 1 independent variable: Using one independent variable X and one dependent variable ...
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1answer
38 views

Creating an interaction term with 2 continuous variables: What to do?

I want to create an interaction term in SPSS on two continuous variables (ticket price and household income) in order to use this interaction term in a multiple regression model and test whether my ...
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heteroscedasticity in residuals in regression

How to deal with heteroscedasticity in residuals after running regression? if log transformation or any other transformation is applied to the model then it should be applied on actual values of ...
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1answer
38 views

How to calculate the prediction interval for an OLS multiple regression?

What is the algebraic notation to calculate the prediction interval for multiple regression? It sounds silly, but I am having trouble finding a clear algebraic notation of this.
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1answer
30 views

Definition of 'Model Diagnostics'

Can anyone help me out with explaining what the term 'model diagnostics' refers to when applied to multiple regression please? In particular, what tests are necessary to check whether your estimated ...
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11 views

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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2answers
33 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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2answers
37 views

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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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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19 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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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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1answer
178 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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2answers
85 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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1answer
51 views

Multiple Linear Regression coefficents

I'm doing a linear regression, in R. The values are like this - ...
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2answers
54 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
20 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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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
20 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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49 views

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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34 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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35 views

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
90 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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2answers
98 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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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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33 views

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
25 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
54 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
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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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29 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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2answers
177 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 ...