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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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OLS Characteristics - Proof

I am trying to figure out the mathematical proof of one of the OLS characteristics in simple linear regression. One stage, or more accurately, one term is not working out. The term coloured in red is ...
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Testing beta is 1 with Minitab or SPSS

I would like to run a regression to test a hypothesis that alpha = 0 and Beta = 1. I understand minitab or SPSS regression will test whether alpha or beta is 0. How do I test whether beta is equal ...
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How to evaluate o model that predicts probabilities for a game between two players

I have a theoretical question. Let's say we have a game that is based on time (duration T) (something like football or basketball), and two players play each ...
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8 views

How to separate subjects into a handful of clusters depending on multidimensional correlations?

I am doing a research project on tobacco toxicity and have measured several biologic and physiologic parameters in the same patients (>15 parameters). Subjects have been exposed to 3 types of ...
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test for moderation in a quadratic regression model

I am trying to test for moderation in a quadratic regression model. my results show significant evidence of a quadratic relationship on one of the independent variables (x1) and also significant ...
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relation between OLS regressions using different data transformations

I have a $(n \times d)$ panel $y$ of $n$ different variables , and a $(n \times d)$ panel $x$ of their forecasts. $d=$ time length of data $n=$ cross section width/ no. of variables I run a pooled (...
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Regression $y = \beta_1x_1 + \beta_2x_2$ with constraints $\beta_1 + \beta_2 = 1, \beta_1, \beta_2 > 0$ [duplicate]

I would like to estimate the optimal weights where weights are all positive and add up to $1$. The most basic problem of this is as follows: Regression $y = \beta_1x_1 + \beta_2x_2$ with ...
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Estimate $\lambda$ in the fitting line $x^\lambda$, where $x \in [0, 1]$

Problem I would like to estimate $\lambda$ in the fitted line $x^\lambda$, where $x \in [0, 1]$. Note that the following R code generates "concave" growth as x ...
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2answers
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Validity of pruning algorithm in regression trees

I am reading the book "The elements of Statistical Learning"(pdf available online for free) and in particular I'm trying to better understand the validity of the algorithm presented in section 9.2.2, ...
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Which test to run for effect of nominal independent variable on multiple ordinal dependent variables

Im new to stats so dont shoot me. I am trying to work out what is the correct test to run in the following scenario Does the independent nominal variable of colour, with four categories (Blue, Brown, ...
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1answer
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How to understand pvalue with controls/covariates

Suppose I have a study with a response variable $y$ and two explanatory variables $x_1, x_2$. I do a regression such as lm(y~x1) and get a p-value of $p_1'$ for $...
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1answer
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glm.fit: fitted probabilities numerically 0 or 1 occurred however culprit feature is numeric

I've been receiving the warning message in the title and have reviewed posts such as e.g. this one. I would like to understand how this feature has perfect separation with the target variable, since ...
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r - Why does rlm (MASS package) return a model without intercept? [migrated]

Test code: rlm(x=runif(100, 100, 200), y=runif(100, 10000, 10002)) lm(runif(100, 10000, 10002)~runif(100, 100, 200)) Result of lm() is sensible: ...
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Regression Non-Normal distribution

I'm trying to make regression models for this sample data. And distribution is this: The net hourly electrical energy output (EP) is the response variable, ...
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1answer
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inconsistency in AIC and AICc - null and an alternative model

I have a dataset containing one response variable, and 3 independent variables. There are 6 number of observations. I want to see, in AICc framework, which of these independent variables best explain ...
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1answer
40 views

Can AdaBoost be used for regression?

I know that AdaBoost can be used for classification, but how about regression? With classification, it is clear how to assign the "amount of say" (or weight) to the predictions of each model (stump) ...
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1answer
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Proving an identity involving $E(e_i^2)$ in simple OLS

Once expressed the simple OLS residual $e_i$ as a weighted sum of the noise terms: \begin{equation}e_{i}=\sum_{j}\left(\delta_{i j}-\frac{1}{n}-\left(x_{i}-\overline{x}\right) \frac{x_{j}-\overline{x}...
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Regression for curve fitting

For a curve generated from dataset points, split the curve into parts and obtain the best-fit degree of polynomial,coeffcients and the interval/range of the split through implementation in python.I am ...
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Effect size in Negative Binomial Mixed Model

Is there a way to calculate Cohen's d (effect size) equivalent for a coefficient in level 1 (i.e., fixed effect) based on the output from a negative binomial mixed model (...
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Multiple regression including uncorrelated independent variables

I have two independent variables, one (variable-1) has a significant correlation with the dependent variable and the other (variable-2) does not. I want to first do multi-regression analysis (spss ...
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Logistic regression using Longitudinal Data

I want to know the factors affecting stunting status among children. My data was taken from year 2003 data and the same children in year 2011. So my data was a longitudinal data. I want to know which ...
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Grouped data as independent variable for continuous outcome

I have an independent variable grouped into irregularly sized classes (0, 1-5, 6-10, 11-20, >20), a continuous dependent variable, and several control variables. So far I've been trying to stay close ...
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Dropout in Linear Regression

I've been reading the original paper on dropout, (https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) and in the linear regression section, it is stated that: $\mathbb{E}_{R\sim Bernoulli(p)}\...
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Dummy variable + interaction term or stratified analysis with survey data

I have data where each observation is an individual. Each individual has a value for country, a factor variable. There is a sampling weight variable to make the sample from each country representative ...
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1answer
17 views

Linear Regression, Formula to Calculate AIC based on Residual Sum of Squares + Number of Predictors

In linear regression, suppose I have Residual Sum of Squares, how to calculate AIC from it? ...
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1answer
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Modelling binomial outcome in repeated measures design using glmer

I have a complex dataset for a repeated measures design. Each participant (N=53) saw a total of 72 images that varied according to three different properties (2 categorical and 1 ordinal independent ...
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Comparing regression slopes obtained in the same group at different times

I have a group of participants. I obtain two biological measures at baseline and then take the same two measures again after a period of time has elapse. I wish to determine if the relationship ...
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How to determine the bandwidth parameter? Newey-West

How to determine the bandwidth parameter? Following from the below paragraph is it easy to understand how Newey and West determine the bandwidth? "The heteroskedasticity consistent estimator (HCE) ...
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1answer
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multivariate truncated survival analysis

I have many short time series (1-5 data points) that document the development of morphological traits (length and pigmentation) of some lab critters in response to different dietary supplement. I ...
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1answer
28 views

Simulate data based on negative binomial regression coefficient

I'm trying to simulate a dataframe with columns x and y based on a real-world dataset. Fitting a negative binomial regression ...
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1answer
26 views

difference between interaction with anova and contrast functions

I'm trying to understand the difference between the interaction with the anova function and the interaction with the contrast ...
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20 views

What type of regression to use?

My dependent variable (values are in range 0-16) was created by summing 4 Likert scales (0-4, strongly disagree to strongly agree), all measuring attitudes. Cronbach alpha is very strong. Did I create ...
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How to do a sensitivity analysis on a non-linear equation?

In the company, it is very difficult to actually do quotations for our customers properly because we do not have perfect information regarding the factors that affect the cost and profit. So I created ...
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1answer
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FIND KNOTS IN REGRESSION

To find the knots automatically in piecewise polynomial regression, which concept is BEST, cubic splines or k fold cross-validation in python
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R-squared from Backward elimination doesn't match that from linear model

I am trying to pick features using Backward Elimination on the Housing Prices dataset in Kaggle using the following function. ...
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Logistic regression with lagged independent or explanatory variables without lagged dependent variable

I want to perform regression with a binary dependent variable (no lag) and independent variables with 3 lags. I am new to this field and so far the models that I saw included terms corresponding to ...
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Loss function for regression for bin prediction?

Say I want to predict the weight of somebody. I know that the weight of person A is something between 85 - 90 kg, but there is no exact value. One way to treat this problem is indeed just ...
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Cox Regression Specifics: Standardizing, Log Transforming

In the statistical analysis of this paper I have some questions regarding their approach. https://academic.oup.com/ndt/article/33/6/1001/3978817 “Variables with non-normal distributions were either ...
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R - Aggregating weights from multiple categorical variables

I used the lm function to fit a linear model on my data. Some of my data are multi-levels categorical variables (say season : summer, autumn, winter, spring). The lm function automatically splitted ...
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p value in backward elimination regression

I need some help with the backward elimination output from Minitab below. Can p values A, B, C, D be equal to 0.745? Or the p value should be smaller than 0.745?
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Error in lm.circlular.cl

I have a dataframe where I have information on the departure bearing when individuals are leaving a certain site, the departure date, and the distance they travelled. I'm interested in modelling the ...
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Compare if two regression model behaves similarly

this is my first asking question here... forgive me if my question is not clear enough. I have two datasets; one is ground truth dataset and another is perturbed version of the same dataset. I want ...
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24 views

Does independent variables relationship distribution estimation help in fitting Logistic regression

I had for some time now a question in mind which is quiet difficult to formulate, so I'll try my best : Say I have two variables (age and number of purchases) to fit with my LR, and I know this ...
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1answer
31 views

Decomposing Historical Data - Arimax versus linear regression

In the creation of a "marketing mix model", past sales data, is regressed against various marketing spend (TV, radio, billboards etc) along with other aspects influencing a companies sales such as ...
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Interpreting interaction term on highly correlated variables

Somebody at a meeting today made the following comment about a Marketing Mix Model (Linear Regression) we run every year. We should account for the high collinearity of the two Marketing Variables (...
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Implement an Intercept T-Test in NumPy

Quick statistical question from an university econ student. In Stata, when you run a linear regression, they perform a t-test of the intercept coefficient to see if it is statistically different from ...
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1answer
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Transfering the approach of GLM/GLM.NB to FEGLM in R to find the best dispersion parameter

I would like to analyze my dataset with around 1 million observations and 10 thousand fixed effects with a negative binomial regression model. Due to the high number of fixed effects I cannot apply '...
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6answers
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Linear regression when Y is bounded and discrete

The question is straightforward: Is it appropriate to use linear regression when Y is bounded and discrete (e.g. the test score 1~100, some pre-defined ranking 1~17)? In this case, is it "not good" to ...
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2answers
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Hypothetical experimental design - help needed for statistical analysis

I am proposing a hypothetical lab experiment where participants are randomly assigned into T(exposure to social norms messaging) and C (neutral messaging). Each group is then subsequently asked their ...