Regression that includes two or more non-constant independent variables.
3
votes
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?
0
votes
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
votes
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 ...
0
votes
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 ...
1
vote
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
votes
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
votes
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 ...
0
votes
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 ...
0
votes
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 ...
0
votes
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'...
0
votes
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 ...
0
votes
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
vote
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 ...
0
votes
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/...
0
votes
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(...
1
vote
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
votes
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
votes
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
votes
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,...
0
votes
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 ...
0
votes
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
votes
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
votes
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
votes
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 ...
0
votes
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 ...
1
vote
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 ...
1
vote
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
votes
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 ...
1
vote
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.
...
0
votes
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 ...
0
votes
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 ...
0
votes
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:
$y=\alpha+\beta x+\beta_0x_0+\beta_1x_1+\beta_2xx_0+\beta_3xx_1$
...
1
vote
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
votes
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 ...
1
vote
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:
...
2
votes
2answers
166 views
Extract linear equations from R's lm
Assume I have data with a dependency y(t) and parameters p1, p2 and ...
0
votes
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
vote
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...
1
vote
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
votes
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 ...
1
vote
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?
6
votes
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 ...
0
votes
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
vote
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:
...
0
votes
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 ...
1
vote
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
votes
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
votes
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:
$\hat{y}=\sum_{i=1}^{3} \hat{\beta_i}X_i$
...
0
votes
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
...
0
votes
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, ...
