Questions tagged [regression]

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

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12
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3answers
6k 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
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1answer
984 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
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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
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1answer
4k 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}
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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
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2answers
9k 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
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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
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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
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4answers
230 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
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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 ...
14
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2answers
30k 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.
122
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3answers
38k 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
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4answers
411 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
9
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1answer
35k 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
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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
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2answers
12k 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
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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
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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
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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
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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
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1answer
5k 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
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1answer
234 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....
19
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3answers
14k 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
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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
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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
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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
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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
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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
387 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
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2answers
25k 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
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2answers
20k 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 ...
6
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1answer
11k 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
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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
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1answer
698 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
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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
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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
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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
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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 ...
2
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2answers
2k views

Distance to a regression line, and degrees of freedom

How do you estimate degrees of freedoms for derived measurements? I want to assess the significance of the distance of an independent data point to a regression line. I can easily calculate the (...
1
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2answers
2k views

Disadvantages of negbin regression

Does anybody know if there're common known disadvantages of a negbin regression? In my opinion it seems to fit every problem pretty good (measured with the estimated dispersionparameter). So why not ...
9
votes
4answers
27k views

Minimum number of observations for logistic regression?

I'm running a binary logistic regressions with 3 numerical variables. I'm suppressing the intercept in my models as the probability should be zero if all input variables are zero. What's minimal ...
1
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2answers
1k views

Determining correlation in certain subsets of a dataset in R

I have a large dataset that has many variables. I'm trying to determine which variables correlate strongly with one specific variable. When you look at the entire dataset as a whole, the correlation ...
3
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3answers
6k views

How to estimate and interpret an offset correctly in a Poisson regression?

Assume the following easy example of a glm regression with an offset: ...
1
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3answers
5k views

How do you apply a linear regression built in SPSS to new data and generate prediction intervals

I am trying to use SPSS to build a linear regression on historical data (dependent and independent variables) and then apply this to new data (independent variables only) to generate predicted values ...
5
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2answers
1k views

Which measure of model fit to report when performing likelihood based regression: AIC, BIC, Pseudo R-square?

I'd like to hear your opinions on the following: What parameters would you report when estimating different likelihood based regression? AIC, BIC, Pseudo $R^2$? What is the standard to report? It ...
28
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4answers
59k views

Pseudo R squared formula for GLMs

I found a formula for pseudo $R^2$ in the book Extending the Linear Model with R, Julian J. Faraway (p. 59). $$1-\frac{\text{ResidualDeviance}}{\text{NullDeviance}}$$. Is this a common formula for ...
11
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1answer
8k views

What kind of residuals and Cook's distance are used for GLM?

Does anybody know what the formula for Cook's distance is? The original Cook's distance formula uses studentized residuals, but why is R using std. Pearson residuals when computing the Cook's distance ...
9
votes
1answer
368 views

Do zero counts need to be adjusted for a likelihood ratio test of poisson/loglinear models?

If there are 0's in the contingency table and we're fitting nested poisson/loglinear models (using R's glm function) for a likelihood ratio test, do we need to ...
9
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2answers
9k views

Whether to use an offset in a Poisson regression when predicting total career goals scored by hockey players

I've got a question concerning wheter or not to use an offset. Assume a very easy model, where you want to describe the (overall)number of goals in hockey. So you have goals, number of games played ...

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