Questions tagged [multiple-regression]

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

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Ordinal regression, multinomial regression or linear regression with dummy variables?

I am conducting a study looking at the quality of clinical trials. I am trying to find out what factors are predictors of the methodological quality of the trials. My dependent variable is a score ...
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Binary logistic regression: p-value of predictor containing all cases of response=1

I'm analyzing a dataset with a set of binary predictors and a binary response variable using logistic regression. The response variable equals 1 only if some variable $x=1$, so there is a clear link ...
Janda Kunegunda's user avatar
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Geometric understanding of linear regression

I am reading up on linear regression from mit 16.850 Here is how the lecture goes: Given: $Y_{n,1}$ (targets), $X_{n, p}$ (data), $t_{p, 1}$ (the parameters I'm optimizing over), True model: $Y = \...
figs_and_nuts's user avatar
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How do I choose a spatial unit for fixed effects model

I have a panel dataset of 40,000 observations, that is 8,000 parishes and 5 years. When I run a fixed effects regression, using parish as a spatial unit, it shows negative adjusted R2 and rather ...
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Thesis - asserting good variable selection [closed]

Requesting some input for my thesis; We want to do a quantitive analysis on how consumer satisfaction (Dependant variable) is with Youtube ads. Then run a regression on independent variables like ...
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In prediction modelling, is it bad practice to combine differing sampling frequencies of covariates into the same model?

An example of this type of prediction task is modelling economic time-series data. Depending on the type of data being reported, the sampling frequency varies: GDP is reported quarterly, employment ...
ron burgundy's user avatar
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Predicted R squared - when is it good enough?

In order to access whether I am overfitting a multilinear model, I have calculated the predicted $R^2$, based on the info found here. My question is, when is a predicted $R^2$ "good enough", ...
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Remove non-significant independent variabels and re-run multiple regression

We have done a multiple regression with the 3 Theory of planned behaviour variables (subjective norm, perceived behavioural control and attitude) plus 2 added variables (because previous research ...
Carl Berglund's user avatar
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Multiple regression model and correlation between predictors

I am reading the Introduction to Statistical Learning in Python (ISLP) book. I am reading the below paragraph: Now suppose that the multiple regression is correct and newspaper advertising is not ...
amineh's user avatar
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In the enclosed context, can a linear mixed effects model be used in place of a general linear model when repeated measures are present?

I work within a field that has some fairly challenging datasets. Without going into too much detail, the following sequence of analyses are canonically performed: Individual subjects have time series ...
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ANOVA comparison different subsets of same data frame

I am trying to compare two model which are based either on the male or female gender in my data. There is the same number of people in every gender group. Why is the anova function not giving a p-...
Han's user avatar
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Linear Regression with Only Categorical Features: Evaluating the Model

Big Idea: This might seem a bit rambly, but there is a unified theme: how good is my model, and can I trust the predictions it's giving me? Background: I am performing a linear regression (not ...
Adrian Keister's user avatar
3 votes
3 answers
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About regression analysis with categorical variables

Suppose my dependent variable is a continuous variable and is normally distributed. And I have three IVs: one is a continuous variable, and the other two independent variables are categorical. What ...
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Missing Coefficients in Linear Regression with Multiple Categorical Variables in R

I have an odd scenario where I am trying to regress a numerical variable on several categorical variables, with no other numerical variables. I have roughly 23k rows of data in my real-world example. ...
Adrian Keister's user avatar
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Chi2/dof going to a different value after doing a Monte Carlo?

I am writing to you because I am completely lost in my work today. I have been working on a somewhat complex multilinear regression for some time, and I am facing a problem that my brain cannot seem ...
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How to interpret the coefficient of a residualized variable in a linear model?

I was fitting a linear model and there was strong multicollinearity present in the data. So, I decided to residualize one regressor variable to reduce the multicollinearity and fitted the model again. ...
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Autoregession meets multiple regression? - Help with verbiage and approach

Needing some help with verbiage and opinions on how I am approaching this model. I have counts of people over the past 24 months. month count 1 100 2 105 ... ... 24 200 First, I reverse the ...
Tyler Brown's user avatar
1 vote
1 answer
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multiple regression using rank?

I like how Spearman's correlation compares rank between two variables, since it puts less weight on my outliers but doesn't completely exclude them. Is there an equivalent procedure using ranks that ...
Trevor D's user avatar
1 vote
1 answer
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Predictive capacities of Generalized Linear Models and significance

I have the following data ...
GiorgioS's user avatar
1 vote
1 answer
41 views

Mixed effect models where one fixed effect leads to very different outcomes [closed]

I am running a pilot experiment that is testing whether a modified form of music notation results in fewer errors in performance than conventional music notation. Our experiment involves participants ...
David's user avatar
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How to prove the square of the t-statistic is the F-statistic in a linear regression without Lagrange multipliers?

My question is essentially identical to this In linear regression, how to prove the equivalence of F-test and t-test? - it was (I believe humbly) erroneously marked as a duplicate of this Prove F test ...
NovicePatience's user avatar
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1 answer
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Analyzing compositional data (sum of proportions = 1) using mixed models with explanatory variables for each proportion

I aim to investigate how the relative abundance of species across communities is associated with the functional traits of each species. For each location ($>250$), I have compositional data that ...
Ruben's user avatar
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1 answer
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Multicollinearity and control variables dilemma

I had some superficial understanding of multicollinearity, that two highly correlated variables in the regression model are not what we want, as the estimated coefficient would be biased. Control ...
LJNG's user avatar
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Analyzing Intervention Effects in a Natural Experiment with Uneven Measurement Points

I am currently working on an observational study aimed at understanding the impact of a certain intervention within a natural setting. Our dataset includes two distinct groups: a treatment group that ...
yelena's user avatar
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3 votes
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Handling non-significant beta coefficients

In our thesis, we have asked participants (consumers) about how several independent factors/variables (like price) affect the willingness to buy (the dependent factor). We have used the beta ...
Carl Berglund's user avatar
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1 answer
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What is the difference between linear regression on two time periods with a time dummy variable and two separate regressions for each time?

I have panel data with two time periods on individuals aged 50+. I am interested to see if if the coefficients on dependent variables $X_{it}$ (e.g, age, gender, health) change from one time period to ...
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Regression model with 3 outputs and different dataset to train each of them

In general I want to predict 3 parameters ISO, aperture and exposure from photo. I was thinking about using cnn with regression. Despite of fact that this is hard task, my minor problem is with ...
swaxkidrauh's user avatar
4 votes
1 answer
370 views

Regression model not significant, but the predictor significant

I'm testing the hypothesis that variable $x$ predicts the variable $y$ AND that it predicts it when adjusted for other variables that have been shown to predict $y$ in the literature ($z_1$ to $z_5$). ...
user216960's user avatar
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Crude multivariate binomial regression OR vs univariate binomial regression OR?

I've performed an univariate binomial regression with OR (95% CI) and I obtain this result: ...
ArTu's user avatar
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2 votes
1 answer
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Calculate SE of regression coefficients using p-value (for meta-analysis)

I would like to do a meta-analysis of the regression coefficients from a group of 9 studies, but they authors had not reported the SEs. For all of the studies, the tables included in the reports only ...
plainsong's user avatar
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least squares in regression with covariate-dependent model [duplicate]

Classical least squares results in regression in statistics state that if $(Y, X)$ follow a model where $$\mathbb{E}[Y\mid X=x] = \alpha + \beta x,$$ we can estimate $\beta$ from a random sample ...
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After creating a weighted dataset using IPTW, can we use the same covariates again during regression in Survival analysis? [duplicate]

I'm conducting an observational study between two non-randomised treatment groups. I plan to balance the two using IPTW (inverse probability of treatment weighting). I'll be using some covariates (...
Uro_Star's user avatar
1 vote
1 answer
83 views

How to interpret the output of 'Linear mixed model fit by REML' in R?

I made a mixed model to investigate the effect of 2 interventions (strength or endurance) on physical activity. Here are descriptions of my variables: PA = Physical Activity (measured in minutes of ...
Nathan Vermaerke's user avatar
1 vote
2 answers
54 views

How to proceed with my multiple logistic regression?

I am conducting a univariate logistic regression to determine factors that predict the success of a surgery. The significant variables were then added to a multiple-regression model. However, the ...
yusefsoliman's user avatar
1 vote
1 answer
30 views

What kind of regression to use for multiple independent variables and multiple treatments

I am brand new to stats and trying to run my first regression based on an existing dataset I've been given at work, and I need some help being pointed in the right direction. I'm self taught, so ...
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Why is the noise included in the posterior predictive distribution in bayesian regression?

Assume the following model: $y = b_0 + b_1 * x$ where we set some priors to $b_0, b_1$. Let $I$ denote our historical data and $x^*$ denote future inputs. Let $p(b_0, b_1|I)$ denote our posteriors. We ...
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Best method for building models using cumulative autocorrelated data

context: As an example, say I am trying to find the causes of maintenance on a vehicle using its use. With an ultimate aim of understanding the relationship between the two. If I only have variables ...
Pooman19's user avatar
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38 views

Terminology: multivariable when multiple levels of categorical variable?

Oftentimes, one sees people use terms such as univariate and multivariate logistic regression, where they clearly refer to number of predictors rather than number of response variables. I know it ...
NeuroPanda's user avatar
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43 views

How to compare multiple linear regression statistically? [duplicate]

I am looking for a way to compare the results from two lmRob() functions statistically. Here is an explanation what I am trying to do: My professor wants me to compare the results of two ancovas (one ...
Emil's user avatar
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1 vote
0 answers
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Deriving the Exact Percentage Change Formula in Logarithmic Models

I have been studying the relationship between logarithmic changes and percentage changes in the context of regression analysis. I understand that when working with small changes, the change in the ...
Nyashiro's user avatar
2 votes
1 answer
37 views

Linear regression on 3D position measurements

I have 3D position measurements $x_i,y_i,z_i$ and their corresponding timestamps $t_i$ in a buffer. The time intervals are not equal between all timestamps. I would like to carry out linear regression,...
user120112's user avatar
3 votes
2 answers
98 views

Applying logistic regression to data recorded on a 0-10 scale

I used 0-10 scale to record disease data on each plant. 0 = no disease 1 = 1–10% of plant leaves infected 2 = 11–20% of plant use infected . . . 10 = 91–100% of ...
Ahsk's user avatar
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2 votes
2 answers
65 views

How can I get the probability of a predicted outcome conditional on the posterior in bayesian regression?

Assume that I am running the following regression: $\hat{y_t} = \beta_0 + \beta_1 \cdot x_t$ where as $\hat{y_t}$ is a continuous variable. Lets assume a gaussian likelihood and nonconjugate priors ...
karl henriksson's user avatar
2 votes
0 answers
63 views

Which dependent variable is mostly impacted by predictor?

Usually one wants to identify the most important predictors (x1, x2, x3..., xn) in a regression model. My question is reversed: I have a data set that contains a risk factor risk and several outcomes ...
a.henrietty's user avatar
3 votes
0 answers
48 views

Checking the linearity assumption in multiple regression

If I wanted to check the mentioned assumption, it is quite easy for only 1 predictor, but what if I have several? I read online that I can plot the residuals vs predicted and see there is no pattern, ...
WalaWizon's user avatar
1 vote
1 answer
25 views

Is It okay to ignore Breusch Pagan Test?

My data han N=90 observation, I plot my data and I don't see any peculiar plot indicating heteroscedaticity, so I run BP and other test like Goldfeld-Quandt and Harrison-McCabe, but BP return ...
tolak's user avatar
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3 votes
1 answer
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Testing binary outcomes with linear regression - R^2 false positives?

There are many reasons not to test binary outcomes with a linear regression (as opposed to a logistic regression). If one did though, is there any scenario in which the F-test for the coefficient of ...
David B's user avatar
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5 votes
1 answer
58 views

Constants in Frisch-Waugh-Lovell / Partialling Out

If in general one wants to apply the Frisch-Waugh-Lovell "Partialling Out"-approach, should we include constants and in which of the following regressions? (1) In the first stage where we ...
Marlon Brando's user avatar
2 votes
2 answers
61 views

Interpreting main effects with dummy coded and continuous predictors in regression

I have a logistic regression predicting probability of a 'yes' response given 'condition' (A,B,C,D; dummy coded, with 'A' as the reference level). This will produce estimates for the following: ...
SilvaC's user avatar
  • 462
2 votes
1 answer
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Choosing Predictors in Multiple Regression

I am planning regression analyses and present this (hypothetical) scenario to communicate my query. I am interested in the effect of 2 different measures ('IQ' and 'SPQ') on dependent variable '...
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