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

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4
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2answers
100 views

Is Multiple Linear Regression in 3 dimensions a plane of best fit or a line of best fit?

Our prof is not getting into the math or even geometric representation of multiple linear regression and this has me slightly confused. On the one hand it's still called multiple linear regression, ...
0
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0answers
13 views

What statistic is used when showing odds ratios?

This relates to running a multiple regression. In similar studies to mine a descriptive stats table shows odds ratios for each categorical predictor to display the odds of being in that category. To ...
1
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0answers
12 views

Regression analyis based on questionaire surveys

I want to do an regression analysis based on a questionnaire survey regarding taking risks. I have 80 respondents from the questionnaire. My dependent variable is a gamble question: where options are ...
5
votes
1answer
42 views

Does a continuous censored predictor have to be treated as ordinal?

This relates to the use of a continuous variable as a predictor in a multiple regression. If a continuous variable (e.g. age) was measured in a questionnaire but the datafile has placed 'cutoffs' on ...
1
vote
1answer
25 views

Panel data basic question on right (best?) approach

I have panel data that is structured like the example below only with more variables. I am using R and my goal is pretty straight forward - estimate the effect of the independent variables on my ...
1
vote
1answer
21 views

Computational error running regression model

This problem has held me up for three days now, so I really hope somebody here has a solution for the problem. I have a model with an excessive number of zeros, so I use a zero-inflated poisson ...
0
votes
1answer
20 views

Linear relationship assumption with dummy variable

This question I think was already asked here but I can't fully understand the answer. I have a number of ordinal predictors that I'm transforming into dummy variables and I'm wondering whether the ...
1
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2answers
43 views

Interpretation of LASSO regression coefficients

I'm currently working on building a predictive model for a binary outcome on a dataset with ~300 variables and 800 observations. I've read much on this site about the problems associated with stepwise ...
3
votes
1answer
60 views

Multilinear regression with multicollinearity: residual regression

I am trying to build a multilinear regression with predictor variables that likely are correlated. I understand that this is a problem, due to overlapping explanations of data. I think I have a method ...
1
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0answers
9 views

Lift Charts in Multiple Linear Regression

When lift charts are generated in a Multiple Linear Regression model, for example, in predicting a continuous variable such as price of a car, how can they be explained in evaluating the performance ...
4
votes
3answers
164 views

What does this plot tell me about my linear model?

I have fit the following linear model, I tested the response by looking at a qq plot and it is almost perfectly linear. When i fit the model though, and study the predicted vs observed plot, It looks ...
0
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0answers
9 views

Zero-inflated model: non-finite value supplied by optim

So I have the following model predicting the presence of an animal on a certain spot. As a time unit quarter is initially used, but for one of the species of animals there is some (little) interesting ...
1
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0answers
21 views

Can I work out the regression coefficients from the betas and the t-values?

I have t and beta (the estimate of the standardized coefficient), but I didn't write down the coefficients or their standard errors. How can I work out the unstandardized coefficients?
0
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0answers
6 views

Determining Optimal Markdown price for Items

Suppose you have a set of items. Some items are old and some items are new. Naturally, the demand for old items will diminish while the demand for new items will increase. As a result, old items will ...
1
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0answers
13 views

Transform my data or use the original data in my multiple regression analysis?

I have three independent variables and one dependent variable (two separate questions which I want to analyse seperately against the tree independent variables). I want to look at the relationship ...
0
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0answers
12 views

Dominance analysis in multiple regression

I'm trying to compare the importance of two groups of three predictor variables. The first group of predictors all relate to a person (to be more exact interpersonal homophily, tie strength and source ...
0
votes
0answers
9 views

Combine two regression models when variables are highly correlated

I investigated the relation between an angle $\alpha$ and a sensor value $x$. So I have $\alpha = f(x)$ which can be modeled here as a simple polynom. When I want to use this relationship in a real ...
0
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0answers
60 views

What skills are required to interpret the result of large scale statistical analyses? [on hold]

Suppose I have all the necessary skills to analyze a large data and I perform an analysis to I obtain results following the answer on a related question. Now I am overwhelmed by the task of ...
0
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0answers
20 views

Zero inflated GLM and singularities

So I am using a zero-inflated model to (1) model the presence/absence of an animal over certain habitat characteristics using a binomial distribution (2) model the count data over the same ...
1
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0answers
17 views

Dealing with correlated predictors when using LASSO

I am developing a prognostic index using the LASSO technique and wondering how to deal with the highly correlated predictor variables. Should I choose the ones I want to include in the LASSO a priori ...
0
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0answers
15 views

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 ...
1
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0answers
44 views

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 ...
0
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0answers
12 views

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, ...
0
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2answers
36 views

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 ...
0
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0answers
11 views

how to find risk premium for APT multi factor model? [closed]

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 ...
-1
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0answers
47 views

Ridge Regression Plot by Direct Calculation [closed]

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: ...
1
vote
2answers
78 views

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 ...
0
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0answers
13 views

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 ...
0
votes
1answer
18 views

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 ...
0
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0answers
16 views

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?...
0
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0answers
23 views

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 ...
1
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0answers
13 views

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 ...
1
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1answer
12 views

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 $x_1-x_2$. ...
1
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0answers
21 views

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 ...
0
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0answers
24 views

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 ...
2
votes
0answers
40 views

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 ...
0
votes
0answers
29 views

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: ...
0
votes
1answer
17 views

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 ...
1
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1answer
14 views

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 (...
1
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1answer
85 views

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 ...
0
votes
0answers
11 views

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 ...
0
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0answers
5 views

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 ...
0
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0answers
13 views

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): ...
2
votes
1answer
36 views

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 ...
0
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0answers
19 views

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 $z=g(x)$: ...
0
votes
1answer
22 views

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 ...
0
votes
0answers
10 views

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) ...
0
votes
0answers
18 views

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 ...
0
votes
0answers
30 views

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. ...
-3
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0answers
42 views

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 ...