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Questions tagged [regression]

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

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Logit Regression: change order, change results

I'm currently doing my thesis, and i'm having quite some trouble in running the logit regression in stata. If you can help me telling me whether what i'm doing makes sense would be appreciated. ...
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28 views

Testing interaction terms individually and simultaneously

I am currently testing two interaction terms individually in OLS regressions. Both interaction terms are significant (p < .5) when tested individually (i.e., two regression equations, one for each ...
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51 views

Regression and Standard Deviation

When I plot my Regression Line and the corresponding lines for 2 standard deviations, sometimes it happens, that no values lie outside the 2 standard deviations. Is that possible/correct or must there ...
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Time period for difference-in-differences analysis

Please, help me with the following issue. I want to analyze effect of firm location on its stock preformance (measure monthly) during the crisis. For this I construct the following dummies: Crisis = 1 ...
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R : Can i use discriminant analysis with 2 groups in the context of predictig the success of bank telemarketing?

I have a school project having us working on the dataset 'data-driven approach to predict the success of bank telemarketing' provided by Sérgio Moro. A bit of background about the dataset: it's ...
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20 views

What is the regression equation for this model I have built on Stata?

I know this is an easy question but I'm trying to get the format for the regression equation for a country and year fixed effect model I have built on Stata. My code is as follows: ...
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34 views

Do control variables in a regression analysis cause collinearity?

This is something that bothers me for quite some time, but I didn't find yet a satisfactory answer. I hope that the wisdom of the people hear will help me to clarify this: In a multivariate ...
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18 views

Assumption if independence between X variable and error term in multiple linear regression

Im doing a multiple linear regression model and i have to check if the assumptions are satisfied. I am not sure how i can check if the assumptions of independency between the X variables and the ...
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19 views

Holdout loss much worse than training & testing data

I am creating a simple MLP which is to predict a single output based and 9 inputs. The data is scaled between 0 and 1, and the data is shuffled before training. The resulting model leads to very low ...
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Coefficients of correlation and determination, linear regression

The decision-maker has two advisers arguing with each other. The problem is the following. There is a disease the cause of which is unknown. Some external factors are believed to promote its ...
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22 views

Self-calibrate sensor measurements with linear regression

I have measurements of 3 sensors (A, B, C) over variable W. Reproducible code. The sensors may be wrongly calibrated so before analysing the data I correct the values for each sensor. The correction ...
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1answer
32 views

Why does LASSO ignore a predictor that has predicting power and NOT correlated with other predictors?

I have a linear regression problem for my car fleet data, where $y$ is the change in rental price and $X$ is a design matrix with around 30 columns (predictors). Most of the predictors are continuous ...
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conditional and interventional expectation

Conditional expectation $E[Y|X]$ and interventional expectation $E[Y|do(X)]$ are related but conceptually very different things. I know that if $X$ is a randomly assigned by an experiment, we have ...
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61 views

Why normality assumption on linear model implies equivalence between least square estimation and maximum likelihood estimation?

Consider the following excerpt from the Alan Agresti's book on generalized linear models: "Having formed a model matrix $\textbf{X}$ and observed $\textbf{y}$, how do we obtain parameter estimates $\...
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45 views

Ramsey's RESET test vs Rainbow test for omitted variable bias tests

I am trying to provide some statistical proof about the omitted variable bias in my regression model. I have used the following two omitted variable bias tests for this purpose: (1) Ramsey's RESET ...
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Dissertation Results. X and Y non-correlation, but then X significantly predictive in regression model. Why? [duplicate]

I just finished my dissertation results and trying to interpret them and very confused. Basically, X and Y are not correlated but then when I put X in my regression model with a few other variables X ...
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12 views

3rd interactions using LASSO

This may be an easy/wrong question: I am trying to find third order interactions and which data to keep in my model: I am using LASSO and the glmnet package in R. I have multiple variables, E1,E2,E3,...
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289 views

In linear regression why does regularisation penalise the parameter values as well?

Currently learning ridge regression and I was a little confused about the penalisation of more complex models (or the definition of a more complex model). From what I understand, model complexity ...
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Logistic Regression For Classification

The origin of logistic regression is actually logistic curve which varies from the value 0 to the value 1. It looks like the letter S, and it specifies the growth of species. If our data distribution ...
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Linear model for paired data [closed]

We have 100 subjects of varying and known age and sex with two strongly related dependent variables (X, Z) measured with two methods (A and B). Method B is known and expected to show more reduced ...
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1answer
57 views

Logistic regression produces well calibrated models. Is that true for neural nets trained in batches?

This is an earlier discussion about LR producing well calibrated models: Some people equate neural net based prediction models (even deep NN or deep+sparse NN) to be equivalent to logistic ...
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1answer
37 views

High Correlation Between Residuals and Dependent Variable

I am working with a data set of roughly 1,500 obs. The model I built is a double-log GLM model to estimate price elasticities. During testing, I discovered the residuals and the dependent variable are ...
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32 views

Regression model form

I have the following exercise: US.pop dataset from car package contains information about USA population from 1790 to 1990. Find regression model in form of $y = a / (1 + \exp((b-x)/c) )$ for ...
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Is a linear mixed model suitable for this experiment design?

I would like to see if 1 year of musical training significantly changes the covariance between two independent variables (forward and backward digit span) and my dependent variables consisting of 4 ...
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46 views

Including future values in a regression

If I have a variable that depends on its expected value in the future among other things (for example inflation), would it be possible to regress it on future values of the dependent variables (in a ...
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31 views

When/why not to use studentized residuals for regression diagnostics?

Consider a linear regression $$ y=X\beta+\varepsilon. $$ Residuals $e:=y-X\hat\beta$ are often used as substitutes for the unobserved model errors $\varepsilon$ for validating assumptions such as ...
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Why does the R2 value of regression improve on retraining neural network

I have two sets of data samples. Set 1 has 1900 samples and Set 2 has 1000 samples (none of which overlap with Set 1). I am using Set 1 to train my neural network and then testing it in Set 2. On ...
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1answer
33 views

How exactly to evaluate Treatment effect after Matching?

In Elizabeth's Stuart's 2010 paper "Matching methods for causal inference: A review and a look forward", she states the following: "Section 5: Analysis of the Outcome: ... After the matching has ...
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Panel data dynamic model: transitory and permanent effect

I want to ask about panel data dynamic model. For my thesis I have Gini as the dependent variable and trade as the independent variable and some other control variables such as education and ...
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1answer
26 views

Mapping one feature space to another for prediction purposes?

I have a historical data set which comes with two sets of features, (X,Y,Z) and (A,B,C). The task is to see how similar a new data point is to the points in my historical data set in the space (X,Y,...
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1answer
27 views

Logistic Regression bootstrapping gives 0 bias and standard error

I'm an R newbie and I'm trying to use logistic regression to predict Admission granted using 4 dependent variables - GPA, Gender, International student or not and SOP grade. Since I have only 113 data ...
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1answer
63 views

R- up to third order interactions

I have multiple variables, E1,E2,E3,E4,E5,E6 are non binary variables and G1-G26 are indicator variables (0 or 1) . How would I be able to find up to 3rd order interactions for a linear regression ...
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1answer
27 views

The Impact of Multiplying the Dependent and Independent Variables by a Constant

I have a regression model in which the intercept is 0 and the slope is 3. Now I multiply the dependent variable by 10 and independent by 2. I wish to find out what happens to the intercept and slope. ...
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goodness-of-fit for logistic regression with a ratio dependent variable

My dependent variable is number of days in a week a certain activity occurs, so I figured I would express it as a percentage out of 7 (days) and model it using logistic regression. I would like to ...
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1answer
49 views

Verifying Identification Results for Univariate Regression

So I have this linear regression model shown below and I'm supposed to be showing that equation 3 is equal to equation 4. There's a hint that says a 2x2 inverse matrix appears in the proof, but the ...
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1answer
53 views

Beginner - Iteratively adding terms to regression model? [duplicate]

I'm learning about regression models via Andrew Ng's Coursera course. I have a question regarding automatically finding a good model. Does it make sense (my guess is no) to iteratively add terms, or ...
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1answer
17 views

Interpret coefficient for dummy variable in multiple linear regression

I have the a multiple linear regression looking like this: $$y = \alpha + \beta_1 female + \beta_2 x_2 + \beta_3 x_3$$ "female" is a dummy variable (0=male, 1 = female) My overall hypothesis is ...
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Is this residual plot a problem (multiple linear regression)

I have attached a residual plot (and QQplot), and i would like to know if you think its too big of a problem? It is not fully equally spread around 0 and there is a fairly big outlier. all the best
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1answer
133 views

Analysis of count data with percentages

for my master thesis I count and identify sediment grains. In total I have 82 samples from 3 different gravity cores. I divided the sediment components in 11 groups (Quarz, Mica, Opaque, Aggregate, ...
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10 views

How to properly represent a sum of interactions in ANOVA?

I would like to include an ANOVA-style linear model in my work that shows the main effects but has interactions lumped together in one term for brevity. When I do the analysis I will include the ...
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Multilevel logistic regression for 3 by 3 factorial design? Sparse matrix problem

For the sake of example, let’s say this is a costumer research study. I have a binary outcome (x) that is either making a purchase or not making it. I have three independent variables: store ...
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Confused about multilevel analysis and non independence of observations

I'm still struggling with my understanding of multilevel analysis, wondering if it applies or not to my problem. I'v read here the following (where author gives an example of a multilevel model with ...
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2answers
45 views

Formal Bayesian justification of conditional modelling

I'm having some trouble following the logic of this passage from Chapter 14 in Bayesian Data Analysis, A. Gelman: The numerical 'data' in a regression problem includes both $X$ and $y$. Thus, a ...
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46 views

Weighted least square - intervals of prediction

Lets assume that we have the following data set with problems of heteroscedasticity: ...
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1answer
31 views

OLS loss function 3-d surface plot

I was trying to plot the OLS loss function as a function of coefficients $\beta_0$, $\beta_1$. As far as I know it should be a convex function with one local minimum which is also a global minimum. I'...
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21 views

Bound of the bias of a Gaussian Process by its standard deviation in Gaussian Process Regression

In Gaussian Process Regression (GPR), intuitively, the bias of the conditioned Gaussian Process (posterior) at a location $x^*$ gets smaller if the variance at $x^*$ is getting smaller, for example in ...
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11 views

Direction of bias when changing from OLS to IV

If when using an instrumental variable it increases the size of the coefficient and changes the direction of the relationship compared to OLS --> what direction of bias does it suggest in the OLS ...
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2answers
33 views

what's the use of lag? when using it do I have to lag my dependent variable or is it only the independent variable?

I am confused about the usage of lags. I have a problem on multicollinearity and someone propose that I should use lag or conduct a first difference on my model. ***My method of estimation is a ...
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2answers
27 views

accuracy of a regression prediction model

I developed two prediction models using non-linear regression analysis to predict a set of values using sigmoidal and power functions. I was wondering how I can evaluate the accuracy of these ...
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What statistical test is suitable for testing the goodness of fit for an exponential regression model?

I want to do a study about the risk factors of heart failure disease that will affect the survival time of a patient. My independent variables: age, smoking status, ejection fraction etc.., and my ...