Stack Exchange Network

Stack Exchange network consists of 174 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange
Join us in building a kind, collaborative learning community via our updated Code of Conduct.

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

2
votes
0answers
12 views

divergence of beta estimates between OLS and regression with ARIMA error

I have physiological time-series data: ~60k observations per channel, ~100 Hz sampling. I will model individual channels with ~20 regressors. Under OLS, given temporal autocorrelation in the data, ...
0
votes
1answer
18 views

How to interpret and determine the reference levels of independent variables in a regression problem?

I encountered several regression problems in the field of engineering that are formulated similar to the following form (I will ommit the error terms): $$ y=a_0+a_1(x_1-c_{ref})+a_2\ln(x_2/d_{ref})\,,...
0
votes
1answer
18 views

Sample size calculation for elastic net regression

I am using elastic net regression to investigate the effect of preditors on the response variable while accounting for multicollinearity among the predictors. But I wish to perform a sample size ...
0
votes
0answers
14 views

Finding when an external effect appears in time series using regression analysis

I have the 'seen' data (post views, PV) of different social media channels over a period of time and I want to see whether the effect of an external factor (EF, for instance, internet accessibility) ...
0
votes
0answers
15 views

How to perform an exponential regression with multiple variables in r - follow-up [on hold]

I've found the question below and I thought it would answer my similar questions. Can you help explain the role of the highlighted part of the code? I think this is the cause of it not working. <...
0
votes
1answer
21 views

pca axes for multiple regression [on hold]

I have 15 environmental variables, and I would like to use them on a multiple regression analysis. I decided to use PCA to deal with multicollinearity, then I extracted the first three PC axes and ...
1
vote
0answers
22 views

What is the correct multiple regression method to answer my hypotheses?

I have a dataset in which I'm looking to explore the relationships between nine input variables (X1 - X9) and five outputs (Y1 - Y5). For each output, I have a hypothesis based on the previous ...
0
votes
0answers
9 views

How to interpret the output from an all-subsets regression?

I have a dataset in which I'm looking to explore the relationships between nine input variables (X1 - X9) and five outputs (Y1 - Y5). For some of the outputs I hypothesise that certain inputs will be ...
0
votes
0answers
13 views

multivariate multiple regression, testing if a variable leads y's at the same time

I have a what I understand to be a multivariate multiple predictive regression. The y's are different variables and I am attempting to see if these are lead by w at the same time. I use the standard ...
0
votes
0answers
9 views

ancova pre-post negative linear relationship with IV [closed]

I have pre and post treatment data along with a bunch of variables. There are two groups, control and treament. While checking for assumptions of ancova plotted one of the IV with the dependent ...
0
votes
0answers
9 views

Why do some attributes show null outcomes in multiple linear regression [duplicate]

I am trying to analyze this data by linear regression and found experimental outcomes given below: ...
2
votes
1answer
30 views

Interpretation of regression coefficients with different subsets of independent variables

I have a multiple regression problem. Let's say there is a physical system with a true model: $$ y = b_0x_0 + b_1x_1 + b_2x_2 \;\;\;\;\;\;\;\;\;\; (1) $$ Now, imagine I only have access to a ...
1
vote
0answers
9 views

Autocorrelation in a multiple regression time series model

I am developing a multiple regression model that uses inputs available at time t to predict a continuous outcome at time t + 1. One of the best predictors of the outcome at t + 1 is the value for the ...
0
votes
0answers
21 views

Trends analysis: use the year when the data generation was concluded or the average of years when the data generation process took place

In my research, I want to determine time trends in a variable y, controlling for confounders a and b. My time variable x is the "year when the event y took place". In some cases, I have data for a ...
3
votes
0answers
35 views

Why is it necessary to eliminate components in PCR in order to 'solve' multicollinearity?

Running some form of regression on an input dataset that exhibits strong multicollinearity can cause unstable regression coefficients, because the regression algorithm can somewhat arbitrarily ...
1
vote
1answer
17 views

Decomposing VIF (Variance Inflation Factor)

I am wondering if there is a method to decompose variance inflation factor (VIF) of variables, to determine the strength of correlation between one variable and other variables. Is there a method ...
0
votes
0answers
7 views

Forward Stagewise vs Coordinate descent

Both the Forward Stagewise and the Coordinate Descent can be used to solve LASSO. The Forward Stagewise updates the coefficients by a step size or learning rate $\epsilon$. The Coordinate Descent ...
3
votes
1answer
23 views

How does pooling work with crossed effects in multilevel models?

In Section 12.2 of Gelman and Hill, The authors mention that one of the main benefits of creating a multi-level model is that you can take advantage of "partial pooling". As an example, if you were ...
0
votes
1answer
26 views

The meaning of coefficients in Multiple Linear Regression

So I am learning about linear regression. The coefficient is the slope of the function, which means how much the dependent variable change due to change of the independent variable. So I make an ...
0
votes
0answers
22 views

Power Regression with multiple variables

I have conducted a parametric study and have obtained a dataset. $Y$ is the independent variable and $X_1$, $X_2$ and $X_3$ are the three independent variables. Now, looking at the problem, I felt ...
0
votes
0answers
24 views

EDA and cross-validation suggest no interaction but OLS suggests otherwise

Say that we have two variables, $S$ is a score that is used to predict your $I$, an income. We have two groups in our dataset, $A$ and $B$. Members of group $B$ are expected to have higher income. ...
0
votes
0answers
11 views

Alternatives to validate Multi Linear regression time series

I am using multi linear regression to do sales quantity forecasting in retail. Due to practical issues, I cannot use use ARIMA or Neural Networks. I split the historical data into train and ...
0
votes
0answers
11 views

How to fit a model that forces all points through zero while allowing for interaction effects

I'm trying to build a model to predict the percentage of a target audience reached as a function of the amount spent on several media channels (e.g. TV and radio) and the type of campaign. The fitted ...
2
votes
0answers
57 views

Multiple linear regression or simple linear regression on residuals

I have several small datasets of few (9 to 12) observations each, and I do have to treat these datasets separately. For each dataset, I want to test for the relative contribution of two continuous ...
0
votes
0answers
18 views

Mean square Residual

Why Sum of Square of Residuals(SSE) decreases when the number of explanatory variables increases? And why Mean Square Residual(MSE) is non decreasing with increasing explanatory variables???
0
votes
0answers
21 views

Multiple Regression Quadratic Assignment Procedure

Can you advice a paper or a book about ''Multiple Regression Quadratic Assignment Procedure'? I need information about the essentials and the assumptions of the procedure. Thanks
-1
votes
1answer
65 views

I have some few question regarding OLS.

Can I interpret my my coefficient's p-values even I violated the error normality assumptions? I have a large sample size.
0
votes
0answers
29 views

Sample size and significance

I conducted a series of multiple linear regression analysis to estimate the effect of a treatment. The sample size variates from 100 to 500, while about 10 covariates are included. Since I had no ...
3
votes
1answer
47 views

Regression coefficients have a different sign for highly correlated predictors [duplicate]

I'm working with some real world data and the regression models are yielding some counter-intuitive results.I know that in my data, the X1 and X2 predictors are highly correlated in the same direction ...
0
votes
2answers
20 views

Can I rule out moderation with stable regression coefficients?

I have a multiple linear regression, with a and b as independent and c and dependend variables. I want to rule out a moderating effect between a and b. To do that in the easiest way, my idea was to ...
0
votes
1answer
31 views

Regression for Independent and Dependent Variable

I am doing a quick study on availability of agricultural insurance and food security. I am using data for 3 years from 20 countries. I am using the Food Security Index (independent variable) to the ...
0
votes
0answers
14 views

Find contribution of various features/input variables to the variance of the dependent variable / Attribute variance of dependent variable to features

I am working on this problem where I have 20 odd features (input variables) and two dependent variables. The objective is to find the variance structure of one of the dependent variables. More ...
1
vote
1answer
33 views

Explained Sums of Squares in matrix notation

I am currently reading Appendix C from Gujarati Basic Econometrics 5e. It deals with the Matrix Approach to Linear Regression Model. I am unable to decipher how the author went from equation 7.4.19 ...
1
vote
1answer
26 views

Fluctuation in Impulse Responses

I try to set up a basic first differences VAR_Model: when I plot the IRF it looks like this: The fluctuation seems suspiciously wrong to me. Of course, the coefficients in my model also change the ...
0
votes
1answer
18 views

single variable regression vs. multi variable regression p values

If I have 2 variables which I performed single variable linear regression on and observed no significant p-values > 0.05 for either one, is it possible to get significant p-values in a multi-variable ...
3
votes
2answers
74 views

data imputation of missing values in non-normally distributed explanatory variables

I have been told that mean imputation of missing values is inappropriate when the variables underlying distribution is non-normal. my variable is contiunous (but bound at 100) and most observations ...
0
votes
1answer
15 views

Assumption tests to be run on each independent variable or on the entire model in a multiple linear regression?

Here are important assumptions that one has to check when performing a linear Assumption 1: Homoscedasticity of residuals or equal variance (with Breusch-Pagan test for example) Assumption 2: ...
1
vote
1answer
20 views

Is log-log model considered to be nonlinear?

I am currently revising a paper, in which I tested an empirical model in the following form: , where EP is indicator of environmental performance, FDI - foreign direct investment which is the main ...
1
vote
1answer
28 views

Residualizing dependent variable and two step linear regression

Assume we have a DGP of the form $y = \beta_0 + \beta_1 * x_1 + \beta_2 * x_2 + \beta_3 * x_3 + \epsilon$ where $\epsilon$ is a standard i.i.d. error term. Does residualizing $y$ using a linear ...
0
votes
1answer
22 views

imputation method to deal with missing data (in explanatory variables)

I have a large data set of 700 ebay auctions and want to examine seller reputation effects on auction revenue. some sellers have "detailed ratings" (about 45%), these ratings are out of 5 stars across ...
0
votes
0answers
18 views

when should log explanatory variables be used?

I'm modelling auction revenue on eBay against a set of continuous explanatory variables. when is it best to regress against the log of these variables? i understand that it allows you to discus ...
0
votes
1answer
29 views

Does it count as multicollinearity if an independent variable is perfectly correlated with the dependent variable?

Obviously i want to avoid multicollinarity but the textbook only mentions relationships between explanatory variables. my dependent variable is TOTALPRICE (price plus postage costs) and i wanted to ...
1
vote
1answer
22 views

What explanatory variables to exclude?

I am conducting research on cross-sectional data of ebay auctions and want to determine the effect of reputation on price. ebay offers several measures of reputation: a users "feedback score" (...
0
votes
1answer
42 views

variable selection and model selection

I have a disease dataset, for this dataset. disease_rate is the dependant variable, and rest independant's. ...
0
votes
0answers
14 views

Interpretation of a dif in dif approach

I am conducting a Difference in Difference Regressionalaysis of a Treatment effect over time. Leaving out the covariates, I have three variables over which I am conducting a multiple linear ...
1
vote
1answer
47 views

A question about the linear regression

I applied a linear regression (continuous response, two continuous predictors and one categorical variable). The plot of residuals and fitted values is something like two different clusters. Can I say ...
0
votes
0answers
20 views

Linear OLS regression with aggregates and components

A linear OLS regression is specified as Y = a + b*∑(O+R) + c*R + e, i.e. ∑(O+R) is an aggregate and one of the components, R, is added separately. Results for the regression show that both b and c are ...
0
votes
1answer
33 views

Interpreting Results of Multivariable Regression / how to transform variables to improve results

I am working on a project that predicts the Market Cap (value) of different crypto-currencies. My data is very small (51 observations) and I initially have 18 X-variables. I was hoping to get feedback ...
0
votes
0answers
10 views

which models' variable importance should I trust if their rank are different?

I have a question about vairable importance that is generated from different models, say random forest and logistical regression. For example, if I have two models that are trained on the same ...
0
votes
0answers
11 views

How to check categorical explanatory variables for multicollinearity in SPSS?

I've read on some forums (i.e ResearcGate) that it can be checked by activating "collinearity diagnostics" function as part of a multiple linear regression model. Then I just need to choose a random ...