Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

Filter by
Sorted by
Tagged with
0
votes
0answers
787 views

Standard deviation when estimating a Poisson regression using R

I'm interested in plotting the estimator of the standard deviation in a Poisson regression. The variance is $Var(y)=\phi⋅V(\mu)$ where $\phi=1$ and $V(\mu)=\mu$. So the variance should be $Var(y)=V(\...
1
vote
0answers
53 views

How to predict significant dominant regions of two sequence of numeric values by Hidden Markov Model?

I have few training on Hidden Markov Model. But, I intend to solve my problem by HMM. I would like to have your helps/directions to me. Here, I have two variables to define the 8 one-dimension space (...
22
votes
4answers
93k views

Maximum number of independent variables that can be entered into a multiple regression equation

What is the limit to the number of independent variables one may enter in a multiple regression equation? I have 10 predictors that I would like to examine in terms of their relative contribution to ...
6
votes
2answers
3k views

Estimating standard deviation in Poisson regression

I'm interested in an estimator of the standard deviation in a Poisson regression. So the variance is $$Var(y)=\phi\cdot V(\mu)$$ where $\phi=1$ and $V(\mu)=\mu$. So the variance should be $Var(y)=V(...
11
votes
2answers
7k views

How to test whether a regression coefficient is moderated by a grouping variable?

I have a regression done on two groups of the sample based on a moderating variable (say gender). I'm doing a simple test for the moderating effect by checking whether the significance of the ...
2
votes
1answer
115 views

Inferences about non-overlapping lines

So... let's say I have data that look something like this... (as I look at it now, in the actual data the red line is about 20% shorter than the black (at the high end... but you get the idea) I've ...
4
votes
1answer
3k views

Should quantitative predictors be transformed to be normally distributed?

I am always struggling with normality testing for quantitative predictors (no factors) and transforming them to normality. If I am running a GLMM and my predictors are really non-normal, should I ...
2
votes
1answer
123 views

How to combine 2 different observations to improve state estimates?

Context Let $\mathbf{x}_i \in \mathbb{R}^{100}$ and $\mathbf{z}_i \in \mathbb{R}^{20}$ be input vectors with the same corresponding target $\mathbf{y}_i \in \mathbb{R}^{25}$. Using ridge regression we ...
12
votes
2answers
12k views

Formula for weighted simple linear regression

This wiki page Simple linear regression has formulas to calculate $\alpha$ and $\beta$. Could anyone tell me how to derive the formulas in weighted case?
14
votes
2answers
2k views

Box-Jenkins model selection

The Box-Jenkins model selection procedure in time series analysis begins by looking at the autocorrelation and partial autocorrelation functions of the series. These plots can suggest the appropriate $...
32
votes
1answer
25k views

Equivalence between least squares and MLE in Gaussian model

I am new to Machine Learning, and am trying to learn it on my own. Recently I was reading through some lecture notes and had a basic question. Slide 13 says that "Least Square Estimate is same as ...
13
votes
3answers
7k views

Software package to solve L-infinity norm linear regression

Is there any software package to solve the linear regression with the objective of minimizing the L-infinity norm.
3
votes
1answer
1k views

Perform simple regression without raw data

I have a dataset that I can collect some quantities from, eg. sum,mean,variance... I want to perform a simple regression on column(x,y). According to Wikipedia, the closed form for $\alpha,\beta$ is \...
19
votes
1answer
17k views

Constrained linear regression through a specified point

I have a point (x,y) that I need a linear regressor to pass through given a data set (X,Y). How do I implement this in R?
3
votes
1answer
5k views

How to obtain the variance of my dependent variable in a linear regression with R? [closed]

How can I obtained the estimated variance of a linear model when using R, i.e. \begin{equation} \widehat{var(y)}. \end{equation}
0
votes
2answers
8k views

How to do prediction from a linear regression?

I am not very good in statistics (ok, I'm really bad), I guess this is a very simple question but I dont understand much of the literature. I have a dataset that is arranged in 2 columns (...
9
votes
2answers
10k views

Why is R plotting standardized residuals against theoretical quantiles in a Q-Q plot?

In R, why do the default settings of qqplot(linear model) use the standardized residuals on the y-axis? Why doesn't R use the "regular" residuals?
2
votes
0answers
2k views

Error using rfe in caret package in R

I am doing some exploratory data analysis in the Heritage Health Prize , and have come across a weird error using R's caret package. In the dataset, I've created a dataframe counting how many times a ...
9
votes
2answers
10k views

How to correctly use the GPML Matlab code for an actual (non-demo) problem?

I have downloaded the most recent GPML Matlab code GPML Matlab code and I have read the documentation and ran the regression demo without any problems. However, I am having difficulty understanding ...
4
votes
4answers
231 views

How do I determine which set of measurements is better?

I am measuring protein in humans using two different types of measurement techniques, X and Y (measured on different scales). I have two replications for type X and four for type Y. I average the ...
0
votes
0answers
66 views

Combining heterogeneous measurements to improve inference

I have two separate and heterogeneous measurements of the same object. I wish to make predictions about the object state using both sets of measurements. What ways can the measurements be combined ...
16
votes
2answers
31k views

What are average partial effects?

Does anybody know the meaning of average partial effects? What exactly is it and how can I calculate them? Here is a reference that might help.
126
votes
3answers
39k views

What if residuals are normally distributed, but y is not?

I've got a weird question. Assume that you have a small sample where the dependent variable that you're going to analyze with a simple linear model is highly left skewed. Thus you assume that $u$ is ...
1
vote
4answers
441 views

Viewing kernel regression in a Bayesian framework

If one wanted to use Kernel Regression in a Bayesian Framework, any ideas on how one would go about it? Kernel Regression
10
votes
1answer
36k views

Expected Value and Variance of Estimation of Slope Parameter $\beta_1$ in Simple Linear Regression

I am reading a text, "Probability and Statistics" by Devore. I am looking at 2 items on page 740: the expected value and variance of the estimation of $\beta_1$, which is the slope parameter in the ...
2
votes
4answers
1k views

Refining a linear regression model for condominium prices

I'm hoping someone here is able to help me refine a linear regression model I'm working on at work. I am in no way a statistician, but I guess I have the most experience (basic stats course and ...
8
votes
2answers
13k views

Linear regression terminology question — Beta (β)

I was a bit confused with the meaning of $\beta$, and thought its usage was rather loose. In fact, it seems that $\beta$ is used to express two distinct concepts: The generalisation of the sample "b ...
6
votes
1answer
1k views

Measuring predictive accuracy for multiple dependent variables

In machine learning and in statistics there exist plenty of measures which estimate the performance of a predictive model. For example, classification accuracy, area under ROC curve ... for ...
9
votes
1answer
2k views

Multivariate orthogonal polynomial regression?

As a means of motivating the question, consider a regresison problem where we seek to estimate $Y$ using observed variables $\{ a, b \}$ When doing multivariate polynomial regresison, I try to find ...
1
vote
2answers
6k views

Dissecting three-way interactions

I'm trying to interpret a significant three-way interaction. Basically, I've used hierarchical regression to analyse my data, and I have come up with a significant three-way interaction. My DV is ...
1
vote
2answers
2k views

Curve fitting and max slope calculation

I have a dataset: X variable is date (from April to October) Y variable is vegetation biomass data In my study area, growing season starts around April when vegetation biomass is low and peaks ...
5
votes
1answer
6k views

Propagation of polynomial coefficient errors in fit

I fit a cubic function (in mathematica) $$ y(x) = a + b x + c x^2 + d x^3 $$ to my data and obtained a function. I have the error in each coefficient ($\sigma_a$, $\sigma_b$, $\sigma_c$, $\...
0
votes
1answer
236 views

Need to refine results of logarithmic regression

Using a logarithmic regression tool found at xuru.org ( http://www.xuru.org/rt/LnR.asp#CopyPaste ) and the data from below, the curve of the graph for this data is roughly described by y = 31....
20
votes
3answers
15k views

How to model bounded target variable?

I have 5 variables and I'm trying to predict my target variable which must be within the range 0 to 70. How do I use this piece of information to model my target better?
1
vote
1answer
108 views

How to present a empirical study when using econometric models?

I've got a (probably easy) question in how to handle empirical studies, when there are a lot of effects involved. I have a whole bunch of variables and I'd like to analyze just a few of them. But the ...
6
votes
2answers
7k views

Mediation model with linear regression

In my master thesis I have drawn a few hypotheses. I have answered them all with linear regression. In these linear regressions, I took control variables into account. My question is: do I have to ...
1
vote
1answer
170 views

Interpreting a lots of effects

Does anybody know how to interpret a whole bunch of effects (main and interaction) in a clever way? Or does anybody have a good example where it's shown? To be more precisely: Assume that you have a ...
8
votes
1answer
5k views

Explanatory power of a variable

I have simple linear regression model. What I want to calculate is how "important" each of my input variables are i.e. to make a statement something like this: "60% of predictive power in this model ...
1
vote
0answers
2k views

Interpretation lin-log regression where the covariate is log(x1 + 1) transformed

I have a lin-log regression model like $$Y = b_0 + b_1 \log(x_1 + 1) + e.$$ The distribution of $x_1$ is very skewed, thus I use the natural logarithm to get a more Gaussian like distribution. ...
4
votes
1answer
391 views

How to optimize the k parameters in dynamic linear regression?

I am starting to use R's dynlm package. Currently I am just looking at the fit and eyeball which choice of lags might be the best. Is there a standard way or a strategy to determine the best k ...
16
votes
2answers
26k views

Persistence in time series

Could someone tell me what the term 'persistence' mean in time series analysis? It's regarding econometrics and applied regression.
5
votes
2answers
21k views

Covariates in regression models

Should covariates be included in regression analyses if they are correlated with the dependent variable or if they are correlated with the predictor variable/s. Alternatively, should they be included ...
7
votes
1answer
12k views

Fitting a beta-binomial model in the case of overdispersion in R

I'm estimating some count data. I have counts for say $m=100$ individuals. Unfortunately when using the Poisson regression overdispersion occurs. So I was thinking to fit a negbin model. But this is ...
16
votes
4answers
6k views

Updating linear regression efficiently when adding observations and/or predictors in R

I would be interested in finding ways in R for efficiently updating a linear model when an observation or a predictor is added. biglm has an updating capability when adding observations, but my data ...
5
votes
1answer
718 views

What count-data models to choose besides negative binomial model when overdispersion occurs?

Assume that you have a Poisson model with overdispersion. Besides negative binomial models, what are other appropriate count-data modeling regression techniques?
4
votes
2answers
6k views

Take the log of an independent variable in a Poisson regression

Is it possible to take the log of an independent variable in a Poisson regression? What to I have to be aware of, when doing so? (The results are getting better, when assuming that the independent ...
4
votes
2answers
2k views

Why does noisy data result in better prediction performance?

I have tested a regression framework's robustness to noise and I have noticed in some cases that adding noise improves the prediction performance and in other cases the performance degrades. What ...
6
votes
2answers
2k views

Interpret t-values when not assuming normal distribution of the error term

Assume that you have a regression with a whole set of variables and you know that the residuals are not normal distributed. So you just estimate a regression using OLS to find the best linear fit. For ...
11
votes
3answers
2k views

Are there any libraries available for CART-like methods using sparse predictors & responses?

I'm working with some large data sets using the gbm package in R. Both my predictor matrix and my response vector are pretty sparse (i.e. most entries are zero). I was hoping to build decision trees ...
4
votes
3answers
3k views

How should we convert sports results data to perform a valid logistical regression?

Say we want to perform a logistical regression analysis (although my question pertains to regressions in general) on sports results to determine the effects of various factors on who wins and who ...

1
472 473
474
475 476
482