Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

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Interpreting negative binomial regression with log transformed independent variables

My dependent variables were highly skewed, so to normalise the distribution they were log transformed. Also since there were zeros in the data, I've added + 1 to transform the variables. This is what ...
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6 views

Polynomial fitting for Shading Correction

I am reading the book Optimization for Computer Vision, and the first example of optimization is a regression for shading correction, in which the author proposes the following polynomial: $p_s(c,x) ...
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2answers
75 views

Skewed Distributions for Logistic Regression

I have been developing a logistic regression model based on retrospective data from a national trauma database of head injury in the UK. The key outcome is 30 day mortality (denoted as ...
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1answer
21 views

How do you conduct regression analysis using SPSS when there is more than one dependent variable?

How do you conduct a regression analysis in SPSS using 1 predictor variable (personality score) and 8 dependent variables (stigma scores to 8 different case studies)? I have tried to use this process ...
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20 views

Regression without intercept [duplicate]

I saw that here explain how to get the formula for getting a regression without intercept but I already know it (for example in R you get it outomatic with ...
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1answer
15 views

Normality requirements across tests

I would like to understand the reason why normality is required in many tests. T-tests: I read that what needs to be normal is the sampling distribution rather than the sample distribution. But since ...
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20 views

Model failed to converge

I'm doing a variable selection for the interaction gender:type2 now ...
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1answer
32 views

Curvilinear relationship with moderator

I am trying to moderate a curve and determine which equation I should follow. A simple curvilinear relationship has the following equation: (1) $Y = b_0 + b_1X + b_2X^2$ (i.e. a linear term and a ...
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34 views

Linear regression for classification

Suppose, I have a classification problem with 2 classes (0 and 1) and evaluation criteria is AUC. I used the following method: fit a linear regression and then pass its predictions through the ...
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1answer
17 views

What statistical test to use to examine attitude and which components have strong/weak influnces on attitude? 4-point Likert Scale survey

This is a quantitative study using survey to examine attitude towards online reading. In the survey, there are 5 main components which contribute to overall attitude. Each component has different ...
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17 views

Multiple regression with many observations per case

I need to analyse the results of a psychology experiment which had a repeated measures design, i.e. all subjects underwent both treatment A and treatment B. There are 27 subjects with many trials ...
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1answer
12 views

normalizing predictor by another predictor

I'm fitting a linear model with outcome $Y$. I have measurements for variables $X_1$ and $X_2$. I hypothesize that $X_1$ and $Y$ are linearly related. I want to know the slope and significance of ...
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8 views

Change in destribution af variable with time - doing cox regression

A hopefully a question that has a simple answer:) I'm doing cox regression on a large dataset. Event=death. Destribution of the main variable change a lot during the timespand (10years) - i.e type of ...
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21 views

Price elasticity of demand from regression equation [on hold]

How it's derived the formula to calculate price elasticity of demand from regression equation which is: E = the slope of the price * (Average price over the period/Average quantity over the period)
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1answer
33 views

How to reproduce a plot with fitted value graph and lowess smooth graph superimpose? [on hold]

I have a binary response variable (y) and continuous explanatory variable (age). I utilised logistic regression to analyse them. I have a plot of y against age. What I want to do now is superimpose ...
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10 views

Performing interaction with a very significant predictor drives down p val of other predictors. But does it makes sense?

I'm observing a phenomenon that I can't understand. I have a linear regression setting with categorical vars. A couple of these elicit an highly significant coefficient and low p values. When used ...
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49 views

proving regression with dummy variables gives same estimates as separate models

Let ($x_{i1}$, $x_{i2}$, ..., $x_{id}$, $y_i$), $i = 1,..., n$ be an i.i.d. multivariate sample and furthermore assume each observation belongs to one of possible $K$ categories. Assume for each ...
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8 views

What is the relationship between orthogonality expectation of the product of RVs

Is there such thing as a statistical concept of orthogonality? Does somebody could provide a formal explanation about the relationship between orthogonality and conditional expectation of a RV? Here ...
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22 views

Which kind of analysis could be made to associate a set of genes to clinical values?

I have a set of 5 genes that can be mutated or not, so therefore are intended as dichotomous yes/no vars. I want to identify the effect of the mutation of this genes on a continuous response var. The ...
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1answer
19 views

From a normal distribution concerning tv rating to its profit distribution

I'm following a Quantitive Methods Course, we have been asked to solve 12 questions from an harvard business case concerning tv ratings. Intro: The questions: I've already answered the first 10 but ...
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21 views

How to fit a single quadratic term to a regression

I have a high dimensional multivariate model and am fitting linear weights to each of the $N$ free variables using a classic stable SVD matrix solver. This works. I want to improve the fit by using a ...
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2answers
134 views

r-squared in quantile regression

I am using quantile regression to find predictors of 90th percentile of my data. I am doing this in R using the quantreg package. How can I determine $r^2$ for ...
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3 views

Calculating Model's Classification Accuracy in R [migrated]

I want to calculate the classification accuracy for a model I am fitting using glmer. Here is what I am doing: ...
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0answers
8 views

Reporting Cox & Snell and Nagelkerke in logistical regression [duplicate]

As part of an assignment, I've been asked to report Cox & Snell and Nagelkerke values for my (logistical) regression analyses. I have been asked to interpret them as well as discuss what they ...
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11 views

Creating a new (dependent) variable with several indicators

I'm studying political science and my aim is to test whether foreign aids have effect in improving governance. I want to measure governance by World Governance Indicators (WGI). But the problem is, ...
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1answer
32 views

Estimates and estimators strict definition

Let's look at a simple regression model: Y = $\hat{\beta0}$ + $\hat{\beta1}$Xi + $\hat{e}$ Estimator's definition is that it's a rule for arriving at an estimate, in this example it would be a ...
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22 views

find best fitting model from GLM output

after running biglm on the data I am a bit confused regarding the output. I normally use GLM and so the biglm output looks rather different. A summary of the object outputted by biglm is ...
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12 views

Confidence intervals for multinomial mixed models

I am using the ordinal package in R to create a multinomial mixed model using the clmm2 function. However, I cannot find a way to get confidence intervals for the coefficients; confint() does not work ...
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Quadratic model as linear decrease in proportions

Assume $Y_i <= X_i$ for all $i$. The conditional expectation of our data was found to satisfy $E[Y|X=x] = a1*x-a2*x^2$ to very good accuracy for a large range, with $0<a2<a1<1$. ...
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1answer
27 views

Practice problems for linear and non linear regression [on hold]

I am new to statistics and am developing an interest in learning regression analysis. To be more precise, can you point me to some online resources where i can find real world data sets and regression ...
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35 views

Multple linear regression, adding one predictor with almost perfect fit make others irrelevant

I found something interesting while playing with some data and linear regression. I built a regression with various predictors, more or less correlated with the outcome. Then I added one predictor ...
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12 views

Can I take advantage of the structure of the predictors to improve prediction?

Suppose we are trying to do regression on 10 predictors to predict a response (continuous or binary). Suppose further that 7 of the predictors seem to behave like a Gaussian, but the remaining 3 have ...
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1answer
105 views

Understanding the error term

I am trying to figure out the meaning of these different "hatted" terms in regression analysis. Here is my basic understanding: $Y$ the original of population/sample values $\hat{Y}$ regression ...
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1answer
46 views

Linear regression parameters that vary with periodic time

I am looking to predict the suitability of weather conditions (a rating out of 10) for an outdoor activity. My plan was to use linear regression with features such as maximum temperature, chance of ...
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16 views

dataset for multiple regression? [closed]

I'm looking for a dataset to perform a multiple regression and possibly a PCA. It should have a dependent variable and more than 10 independent variables and possibly interval variable. I have already ...
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90 views
+50

Modelling mortality rates using Poisson regression

I'm examining trends (between 1998 and 2011) in mortality rates among patients with Crohn's disease. Each patient (case) have been included during 1998 to 2011. At inclusion, each patient have been ...
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33 views

anova vs. multiple regression

I am interested in the effects of A, B, and their interaction (AXB) on Y. The effect of A is not significant while I am using ANOVA, but it is significant while I am using multiple regression. ...
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14 views

If DV and IV were already in percent even before log transformation, how will the log-log betas be interpreted?

I have a question about the interpretation of log-log form when both left and right side variables are already in percentage to begin with even before the log transformation. For example: Let's say ...
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2answers
78 views

Prior/Posterior predictive distributions

I have trouble understanding some notes I have from the lectures. We are in Bayesian linear regression and he explained how we can first introduce a prior probability distribution to the weights: ...
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1answer
25 views

SPSS logistic regression. categorical --> dummies

All our variables (question asked to students in our questionnaire) given by school are answered by: 1) very important 2) important 3) unimportant 4) vert unimportant we want to use these variables ...
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52 views

Ordinary least squares regression giving wrong prediction [migrated]

I am using statsmodels OLS to fit a series of points to a line: ...
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2answers
34 views

“Select All That Apply” — How To Generate a Predictive Model with this Type of Question

For a particular question in a survey, respondents have been asked to select all that apply (i.e. say from a list of books they have read). I'm wondering if anyone knows how I would be able to build ...
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2answers
53 views

estimating the value of a property (real estate) using the hedonic regression

I'm trying to estimate the value of a property depending on the property characteristics. I did some research and I found out, that it would be better to use the Hedonic Model/Regression instead of ...
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3answers
75 views

Econometrics - which independent variable has the greatest impact on dependent variable? [closed]

So I have a model predicting college GPA (dependent variable). I have around 10 independent variables, ranging from hours studied per week to alcoholic drinks consumed per week. That being said, how ...
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1answer
15 views

what R function fits a smoothing spline regression model with correlated errors?

I want to fit a smoothing spline regression model with correlated errors (it's a time series) using R. All I could find is function ssr, from library ...
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10 views

How to use the Glejser test?

Glejser tests for heteroskedasticity of a single independent variable within a multiple regression model. And, it tests it by conduction a basic regression: ABS(Residual) = intercept + slope(X). In ...
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34 views

Regression in SAS and R not matching [migrated]

I'm trying to re-write a current SAS program of mine in R, and I'm checking the output to make sure it matches. I'm starting with a very basic regression, and I can't even get that to match. I also ...
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18 views

Mixed logistic model with complete separation

I want am trying to produce a mixed logistic model but certain explanatory variables suffer from complete separation. I am aware that I need to either use exact logistic regression or a firth ...
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0answers
35 views

Logistic Regression Get 100% R-square but no predictors are significant? [duplicate]

How could that be possible? The model is significant too. The null model already predicted 70% correctly. After adding predictors, the model predicts 100%. But no single predictors are significant. I ...
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high variable significance and low r-sqaured [duplicate]

So I have built a linear model on the yelp business dataset. ...