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

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1answer
36 views

Redundant variables in linear regression

If I have some number of independent variables, and one dependent variable, and some of those independent variables are strongly correlated with each other, does that make one of them redundant?
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0answers
16 views

Multilinear Lasso regression with weighted coefficients

I wasn't sure whether to put this question on StackOverflow or here because I probably need help with both theory and code... Anyway, here goes: I would like to perform a multiple linear regression ...
0
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1answer
20 views

The F-statistic with all predictors vs. with predictors excluded

The F-statistic formula is: $$ F=\frac{(TSS-RSS)/p}{RSS/(n-p-1)} $$ Where $TSS$ is total sum of squares and is equal to $\sum_{i=1}^n(y_i-\bar{y})^2$ and $RSS$ is ...
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0answers
28 views

Interpretation of X'X and its inverse

I know how to calculate $X'X$ and $(X'X)^{-1}$, and also how to use them in proving ols and a number of other things. What is unclear to me is the interpretation of this matrix. We use it so much and ...
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0answers
22 views

How to model and estimate interference and subsequent shocks on panel data?

I have the following setting: In my factory, we have mutliple assembly lines(>10). Each line produces an amout of itmes every day, with some weekly and mothly production peeks. Thus its basically a ...
2
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1answer
57 views

Relationship between dependent and independent variables

I tried to develop an empirical equation by using multiple regression analysis. In my case I use aerosol as dependent variables and relative humidity and winds components ($U$ and $V$) as independent ...
0
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1answer
36 views

Relative importance of multiple correlated independent variables in logistic regression

I have a multiple logistic regression with 11 independent variables (x1 to x11). I have another 4 continuous IVs that are highly correlated with each other and correlated with x11. The 4 IVs along ...
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0answers
12 views

Centering the design matrix in multiple linear regression

What happens to the ordinary least squares (OLS) (multiple) regression estimates when one centers the explanatory variables in either of the following cases: Including an intercept: assume that the ...
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0answers
4 views

Reverse CARET proProcess()

I did the following steps in my modeling using R: 1)applied proProcess() function in CARET package and then encoded the data. 2)Used SMOTE to balance the data 3)Applied Mutlti linear regression for ...
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0answers
24 views

Best fit model in R [on hold]

My predictor variable(x) and response variable (y) are as the following. I tried fitting using multi linear regression, polynomial regression etc. I tried removing the influential points found by cook'...
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0answers
13 views

Hierarchical multiple regression and importance of individual predictors across multiple analyses

I'm currently reading an empirical study using regression and want to determine whether to consider it a trustworthy, well-analysed piece of literature. I'm more familiar with ANOVA models, so I was ...
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0answers
17 views

Models under Regression Analysis list [on hold]

I am compiling a list of models under Regression analysis(Whatever I think is useful for Machine learning) which is divided into two models i.e Parametric and Non-parametric Regression. Got most of ...
1
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1answer
46 views

Using Regression in R - Prediction doesn't match actual

I am using R to build a model and using last year's billing data to predict collection amounts. Comparing the predicted values vs. actuals, the numbers are way off. I'm expecting collections of ...
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0answers
10 views

Confidence Interval in a linear model with restrictions

I am reading the paper by Hannes Leeb and Benedikt Potscher "Model Selection: Facts and Fictions",(available as a working paper here https://www.wiwi.uni-frankfurt.de/profs/klump/D/koll/...
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0answers
26 views

How to calculate the variance of y in in the OLS model? [on hold]

When calculating the variance of the coefficients we get something like this: β̂ =(X′X)^−1X′y.(X is the design matrix and β̂ is the coefficient vector) and thus Var(β̂)=(X′X)^−1X′σ^2IX(X′X)^−1=σ^2(...
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0answers
20 views

regression approach for missing data (left censoring?)

I have a regression problem where I want to predict actuals (dependent variable) of some process where I only have values for a small number of independent variables at the beginning of the process ...
0
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0answers
14 views

censored regression problem if dependent variable only above threshold?

I have to predict some continuous dependent variable of samples where the value of this continuous dependent variable is only above a certain threshold (i.e. predict large values). Does this ...
0
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0answers
17 views

Non-orthogonal experimental design and model selection

I am working on designing some chemical experiments, with the goal (for now) to optimize reaction yield. I intend to use principal component scores in order to investigate solvents, Lewis acids etc. ...
0
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1answer
24 views

Accounting for “surgeon preference” when modeling a binary surgical outcome

I'm trying to model the risk of a binary event following surgery (event=admitted for observation, y/n), and my main predictor is a three-level 'treatment' administered during the procedure (Tx_A, Tx_B,...
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0answers
15 views

How to explain variance in SEM?

I am writing a paper discussion based on SEM findings to a non-statistical audience. For example, how do we explain the concept of R-square (variance) in plain language -- 'The model rationalizes a ...
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0answers
34 views

Interpretation and practical significance based on multiple linear regression coefficients

I am having difficulty articulating the practical significance of positive and negative tone in job application cover letters for male and female job candidates. I obtained the following linear ...
0
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0answers
13 views

low regression coefficient [closed]

I was replicating a study. All the variables have similar descriptive statistics(mean, median, std, q1, q3). However, when I ran regressions using these variables, the regression coefficients are much ...
0
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0answers
14 views

Regression for Interval and Continuous Predictors - Continuous Target Variable

I have in my dataset a continuous target variable sales and two predictors- one continuous and one interval. The continuous variable is ...
0
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1answer
22 views

How to incorporate AR(1) term in a multiple linear regression model

I was trying to model fish catch (CPUE) using a combination of some categorical and numrical predictors. I have the data for 10 years. The data has been collected only in the period from June to ...
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0answers
14 views

What is the relation between the effective sample size $n$ and the model dimension (the effective size of parameters) $p$ in Bayesian model selection?

What is the relation between the effective sample size $n$ and the model dimension (the effective size of parameters) $p$ in Bayesian model selection? Or is there any articles talking about this? I ...
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0answers
27 views

How to run a time series regression in R? [closed]

How to use tslm function in r ti run a time series regression? Do we want to use stationary time series when using tslm function? If I tell in detail, I have y variable and another 16 explanatory ...
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0answers
16 views

Extrapolating a 2D vector field in four dimensions

I'm not really sure how to explain this, but I've got several 2D vectors located at points in 4-space and I want to generate a continuous approximation(?) of this. My data looks like this: ...
3
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1answer
82 views

Ways of Testing Linearity Assumption in Multiple Regression apart from Residual Plots

I was going through the assumptions of linear regression and of course one of them was linearity between the dependent and the independent variables - to be precise I should say that the assumption is ...
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0answers
14 views

How to determine the standard error of the prediction from an MLR?

I have a multiple linear regression model that I have built in SAS that's fairly simple: it predicts someone's blood pressure based on four variables: age, race, gender, and level of depression. ...
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0answers
9 views

Workflow Design: Random Forest Regression with intent to see which independent variables are more important

I am very new in machine learning (and programming in general) so I apologise in advance if my question sounds too simple. I want to build a very simple random forest for regression (not ...
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0answers
22 views

Diagonal straight lines in residual vs predicted values: can it be fixed with bootstrap resampling?

I am studying a health-related-quality-of life scale and I run a multiple linear model for each of its subscales. For a few of these subscales I came across the pattern of several diagonal straight ...
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0answers
18 views

Categorical variable interaction compared to individual regressions

We are examining our understanding of interaction terms in categorical variables. We have a three category variable, and the model is: $y=\alpha+\beta x+\beta_0x_0+\beta_1x_1+\beta_2xx_0+\beta_3xx_1$ ...
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0answers
13 views

Residual sum of squares in multiple outputs problem

On ESL page 56, "...suppose $\mathrm{Cov}(\epsilon)=\Sigma$, then the multivariate weighted criterion $\mathrm{RSS}(B;\Sigma)=\sum_{i=1}^{N}(y_i-f(x_i))^T\Sigma^{-1}(y_i-f(x_i))=\mathrm{tr}((Y-XB)\...
0
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1answer
21 views

can you trust the statistical control

I'm reading a paper that compares two vaccine types. THe people who got vaccine 1 are of lower SES and have more chronic conditions compared to those who got vaccine 2. Multivariate logistic reg was ...
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2answers
38 views

Multiple regression - singularity issues [duplicate]

I am trying to fit multivariate regression models to my data; however, I get singularity warnings. Please find a part of my data below: ...
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2answers
166 views

Extract linear equations from R's lm

Assume I have data with a dependency y(t) and parameters p1, p2 and ...
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0answers
22 views

Interpretation of a residuals vs fitted values plot

My objective is to predict the taxi demand depending on location and time. I transformed the data to be more or less normally distributed and centered & scaled it. Then, I ran a linear regression ...
1
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1answer
17 views

Two-way interaction term and simple slope

In my study, I am looking at gender interaction with an independent variable. In the fitted linear regression, if the gender*var1...
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0answers
26 views

How to get specific terms of a polynomial function in a regression?

I want to simplify data from a complex modell like: fit <- lm(z ~ poly(a,4)*poly(b,5)*poly(c,6), data = somewhat) As I don't know which terms of the complete ...
0
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1answer
53 views

logistic regression vs. linear regression

in the following table (association between cognitive score and Folate intake), the beta coefficient represents difference in slope between different groups with the standard group. the interpretation ...
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1answer
37 views

Bootstrap in models with dummy variables

I have applied the bootstrap technique in a multiple regression model where some dummy variables are included, is there anything special about the treatment of dummy variables?
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2answers
114 views

Significance vs. goodness-of-fit in regression

Assume that I am interested in analyzing the following linear regression model: $$ Y = \beta_0 +\beta_1 x_1 +\beta_2 x_2+e $$ Please explain the difference between testing the p-value for each ...
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0answers
7 views

Minimal sample size for Granger causality testing with monthly time series macro data

I would like to conduct a Granger causality for bitcoin and remittance and FDI. However, the data for bitcoin only starts since 2008 so the maximum number of data is around 10 years. Also, within that ...
1
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1answer
35 views

Multivariate Binary Logistic Regression with Multiple Binary Independent Variables

I am trying to predict a binary response based on multiple binary independent variables, some categorical data, and some continuous data. My data set looks similar to: ...
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0answers
3 views

Predict setting parameters for machine based on 2 quality values

I'm getting started with Machine Learning. I'm not sure which approach would be the best for the following scenario: I want to build a model that predict optimiced setting values for a machine, based ...
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2answers
40 views

Calculate coefficients in multiple linear regression with covariance matrix

I wonder how I can calculate the coeffecients of a multiple linear regression, given just the mean and covariance matrix. For example with this values: Model: $Y = \beta_0 + \beta_1 \cdot X_1 + \...
0
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1answer
15 views

Confirmatory Factor Analysis - Variance Parameters - Error Variance Estimates

I am referring to a confirmatory factor analysis output. I am curious to know how do we interpret the "Variance Parameters" Output containing the "error variance estimates" if the predictors are ...
0
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0answers
13 views

Sobol Sensitivity Analysis for regression but with a difference

I have a linear regression model and I want to use R's sensitivity package to assess the global sensitivity of its factors. My regression equation is: $\hat{y}=\sum_{i=1}^{3} \hat{\beta_i}X_i$ ...
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0answers
11 views

Bug Prediction for next release

I have a requirement to predict the number of defect for next sprint release for each module. for example the data looks below ...
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0answers
29 views

Is Wald statistic sufficient to show no association?

When reporting the results of multivariable regression analyses, I would normally provide either $\beta$ with 95% confidence interval, or the estimated effect size (e.g. between Q1 and Q3). However, ...