# Tagged Questions

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

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### How do I manually calculate linear multiple regression coefficients? [duplicate]

I am working on an assignment in which I need to manually calculate the coefficients in a multiple linear regression model with 6 predictor variables. I also need to demonstrate my working. I found ...
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### Out of ideas: transformation of continuous variables to obtain normality of residuals seemingly impossible

I've been browsing stackexchange for days to come up with decent solutions, but to no avail so far. Some threads seem to apply and offer solutions (e.g. How to transform negative data to be ...
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### Applying support vector regression to vector-valued functions?

So, just as a preface: this is my first time posting to this site, and I am also a machine learning beginner, so I apologize if my question is dumb or if I do something wrong, format-wise. Alright, ...
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### Logistic regression with only categorical predictors

So I started off with a model which included both continuous and categorical predictor variables. But now I wanted to drop the only continuous variable (distance to shore), because to my opinion it ...
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### how to find risk premium for APT multi factor model? [on hold]

I have daily return for company A, B, C and macroeconomic factor D, E how to find the risk premium - lambda for factor D and E using R? r = lambda0 + beta(i)*lambda(i) + e I used lm(A,B,C~D+E) to ...
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### Ridge Regression Plot by Direct Calculation [on hold]

I would like to emphasize that ridge regression coefficients is becoming close to zero as the penalty parameter $\lambda$ increases, but without using R package (glmnet, lm.ridge). My procedures are: ...
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### Can I use PCA (or should I use regression) for testing the effect of multiple variables on one dependent variable?

I have 2000 soil property measures and 14 different variables like rainfall, temperature, slope, etc. I want to check the effect of those 14 variables on soil property measures, including which ...
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### Using differences or ratios in regression

Is it always wrong to use ratios in linear regression? For example, If I am trying to fit a linear model and I have a predictor given by: average age of team A / average age of team B should i ...
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### drop1 LRT is zero in R

So for my current binomial model I am dropping some components and I found out that for one variable the results look a bit different. For 'hurseason' (class factor with two levels Y/N), the LRT is ...
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### Logistic regression model: remodelling significant vars only

I did a logistic regression on 8 vars (continuous & categorical) with stepwise selection, 4 vars came up significant. I then remodelled using only those 4 vars and 3/4 became insignificant. Why so?...
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### How to model turnover as a function of square meters?

After some study I came up with the following regression structure for a model with turnover as the dependant variable and square metres as the independant variable (and some others, but I don't think ...
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### Linear model for testing a ratio of ratios

Our experimental design is as follows: For each of two genotypes (wt and ko), we perform two different gene expression assays (Assay1 and Assay2), and do 4 replicates of each assay. We are interested ...
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### Is dependency created when adding one variable which is the difference of two existing variables to a regression model?

I have two variables $x_1$ and $x_2$ in linear regression. I would like to see if the distance between $x_1-x_2$ is significant. So I want to add one more variable $x_3$, which is equal to $x_1-x_2$. ...
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### Which method for variable selection for multivariate data?

I have a dataset with 299 observations, 35 independent and 141 dependent variables. This is a vegetation dataset, the IVs are environment variables, the DVs are coverage of 141 species (of course many ...
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### The significance of existing variables decreases after adding additional variables in regression [duplicate]

I build a simple linear regression, $y=w_0+w_1x_1$. I find the coefficient of $x_1$ is significant. Then I add $x_2$. It shows that the coefficients of both $x_1$ and $x_2$ are NOT significant any ...
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### How to know which statistical model to use for categorical data?

I'm new to statistical analysis. I'm trying to conduct an analysis of datapoints and possible correlations between them using Python's sci-kit learn library. My data is categorical. For example, a ...
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### Smaller residuals after transformation better?

This is a two part question concerning linear regression in R. Here is my code and what my residual plot looks like before transformation: ...
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### Interpreting lower order effects not contributing to the interaction terms, when the interaction is significant (C in a regression of A + B + C + A*B)

In a regression including 3 variables, and the interaction of 2 of those variables: Variable A Variable B Variable C Variable A * Variable B, where the interaction of Variable A * Variable B ...
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### Alternative Applications of Portfolio Optimization [closed]

What other statistical optimizations in the natural and social sciences require the maximization of the difference between the mean and the variance? In other words have an objective function (...
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### Too many variables and multicollinearity in OLS regression

After reading material related to my topic, I understood that multicollinearity among predictors would result in singular matrix $X'X$, and that leads to noninvertible matrix. Thus, the solution will ...
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### correction of covariates

In an multiple regression model I want to investigate the relation between my dependent variable (cognition score) and an explanatory variable of interest(size of some brain structure). There are ...
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### Violation of parallel lines assumption for gologit2 + implications for choice of model

I've just run gologit2 with the option auto lrforce to find out which of my variables violate the parallel lines assumption, (basically a generalized ordered logistic regression corrected to have ...
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### Prediction of multiple time series with classification

I have multiple time series of air passenger demand with specific classification data. Data looks like this (some rows may lack some data): ...
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### What can be said about about significant predictors in simple regression that become insignificant in multiple linear regression?

I have two predictor variables: An indicator variable A and a continuous variable B. My response variable is continuous (and also bounded, have not made it logit for reasons of simplicity). In simple ...
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### Functional regression with two predictors, one of which is an angle

I'm designing a machine, and I want to investigate the effect on performance of 2 predictors, a length $l$ and an angle $\phi$. Performance is defined by two response curves $y=f(x)$ and $z=g(x)$: ...
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### Interpretation of a residual scatter plot

Hi all, I need to make a basic statement about whether this residual scatterplot looks normal, homoscedastic and linear. I understand that there are probably too few data points to make any conclusive ...
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### How to use a vector of ranks to predict actual values?

I am interested in this problem of learning a machine learning model to take a vector of ranks as input and predict their numerical values. Let's say I have a matrix $Y$ with shape $m$ (instances) ...
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### Partial correlation controlling many variables, overfitting?

I would like to know if using partial correlation analysis when controlling for many variables (here 11 variables) in the same time can affect or bias the results. I have 1 set of data containing 200 ...
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### What statistical analysis for identifying sections of

I am a stats novice, and dont quite know the method to use in my problem. So I have a set of independent variables, and I want to find what sort of prediction strength exists with some dependants. ...
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### How to select the final/best model and the important predictors from LASSO outputs in R? [duplicate]

I am trying to learn about regularization techniques The R commands generates the following plots: I would like to know how to select the final/best model and the important predictors included ...
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### Crowdsourcing price computation for new task

I have historical data for crowdsourcing micro tasks(Task : jobtype (classification of jobs(String)), location of job , price of the job, completion time(start date - final date)). These tasks are ...
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How would you interpret the outcomes if your predictor significantly predict the outcome variable (p value = .013) only in the adjusted model and not the unadjusted one (p value = .542)? Could you ...
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### Constructing A Multiple Regression From Multiple Single-Variable Regressions

Let's say I'd like to fit a linear regression model to predict a student's college GPA. There exist three predictors/features: high school GPA, # of hours the student studies per day, and hours of ...
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### Linear-Regressing Y on X : Interpreting coefficient 1

I did a (multi)linear regression of Y on a collection $X_1, X_2,..,X_k$ of variables. Some of the coefficients of the $X_i$ were very close to 1. Can I conclude that Y and $X_i$are , in a sense, ...
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### Multi-level analysis in SPSS?

I am struggling to work out how best to analyse a large set of data in repeated measures design. I have 4 main conditions: a, b, c, d. Then within each condition, participants repeat a trial 2 times ...
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### Comprehensive list of dimensional reduction methods?

I was wondering if something like this existed on the site. I found this https://e-reports-ext.llnl.gov/pdf/240921.pdf which mentions PCA LA and ICA but I was wondering if there was more, for example ...
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### Appropriate to interpret logistic regression interaction when DV probability is the same for two cells?

I have two factors (F1, F2) and I want to determine if they interact. A possible problem, however, is that the probability of the outcome occurring is exactly the same for two of the 2x2 cells (b-a ...
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### Projection Pursuit Regression

I am trying to use Projection Pursuit Regression to fit a model to my data set, but I am running into some difficulties. I have a few questions: 1. Can PPR only be used when you have many predictors? ...
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### Is it good practice to adjust for multiple comparisons when performing different multivariable regression models?

I have five different outcomes and the same group of independent variables which have been used for the five different multivariable Cox regression models. The indipendent variables I have used in the ...
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### How to interpret this Residuals vs Fitted plot for multiple regression?

I'm quite new at linear regression. I get the following Residuals vs Fitted plot : I do not understand why there are two parallel lines. Is there a problem with my data? Thx !
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### Generate HTML from stats model summary [migrated]

I have the following code to model a regression and print the summary to a log file ...
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### Optimizing weighting coefficients between series

I am working with a set of predictor variables against a single response factor. This is repeated over several series, all with the same variables. For example, it can be imagined as the predictors ...
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### how does nls model work?

I am trying to understand how nls model works. Let's say I have data frame mtcars and like to do this: model<-nls(mpg~disp) but getting this error: ...
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### logistic regression multivariable fractional ploynomials stata vs. R

I a going through Hosmer, Lemenshow and Sturdivant's (HLS) Applied Logistic Regression (2013) and trying to interpret the difference between what STATA is doing and what R is doing. Concerning the fit ...
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### Nonlinear Multiple Regression

I have a dataset that has multiple x predictor values. To fit a model, I was going to use multiple regression but I looked at the scatter plots for each x value and the y dependent variable and they ...
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### Negative regression estimates with positive intercept, including dummy and numeric variables

I am running a multiple regression including dummy, count and numeric variables. The dependent count variable is the result of PCA. Multicollinearity tests do not show collinearity. However, most ...
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### Is there difference between “spectral decomposition” and “singular value decomposition”? [duplicate]

Am I right that "spectral decomposition" for symmetric matrix and "singular value decomposition" for non square matrix? Any clarification would be appreciated.
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### Regression With More Than $y$ On One Side

I have an equation in the form of, $$ax + by = cz + dr + es$$ The variables are $x,y,z,r,s$ and the rest are the coefficients. In a book I see the author does a fit and finds values for all these ...