The tag has no wiki summary.

learn more… | top users | synonyms

0
votes
0answers
79 views

How to use residual analysis to remove the effect of confounding variables in a model in R

I want to find which soil variables better explain plant productivity, using a database that contains information for about 100 forests plots across Europe. These plots have only one species per plot, ...
0
votes
0answers
15 views

Degrees of freedom with duplicate data points

The degrees of freedom of the residual in an OLS model is $n - p - 1$, where $n$ is the number of samples, and $p$ is the number of independent variables. I.e., the data matrix $X$ is $n\times p$. If ...
0
votes
0answers
33 views

Arima Models Diagnostics

I'm doing a forecasting using seasonal ARIMA method. I'm using astsa package in r and I'm testing two models that I can't decide which one is better to use than the other The ACf and PACF for the ...
1
vote
0answers
23 views

Heavy-tailed errors in mixed-effects model

I'm relatively new to statistical modelling and `R', so please let me know If I should provide any further information/plots. I did originally post this question here, but unfortunately have not ...
2
votes
1answer
58 views

Interpreting case influence statistics (leverage, studentized residuals, and Cook's distance)

I just wanted to clarify some things about leverage, studentized residuals, and Cook's distance: Does a large (in absolute value) studentized residual mean that a case is an outlier? Does a large ...
0
votes
0answers
27 views

How to create an index to compare regression lines

Suppose I have the actual and fitted values of two regression lines. Each regression line is modeling the sales of some good. The fitted and actual values of one of the regression lines is much ...
3
votes
1answer
63 views

What if a transformed variable yields more normal and less heteroskedastic residuals but lower $R^2$?

I am trying to decide whether to use a square root transformed dependent variable in multiple linear regression. Transforming $y$ leads to more normally distributed residuals and also to less ...
1
vote
1answer
38 views

Analysing the residuals themselves

As far as I know, it is possible to fit a linear regression model and then fit a second model to predict the residuals from the first model by using some other variables. By this you can understand ...
1
vote
1answer
108 views

Not sure about the interpretation of this residual plot

I'm analyzing a residual plot of the residuals vs the fitted values. I'm not quite sure how to interpret this plot since there looks like there is a pattern and the average is not actually zero. ...
0
votes
0answers
50 views

Looking for a better post-hoc analysis

I'm a PhD student in Neuroscience, with a master degree in Psychology and I use R for my statistical analyses. As you know, in NHST usually post-hoc analysis in continuous data are essentially ...
1
vote
1answer
53 views

What is the benefit of knowing the F statistic in multiple linear regression?

One of the basic figures you get when running multiple linear regression using almost any off-the-shelf software is the F statistics. However, I cannot recall one situation, where the F value was low ...
2
votes
1answer
68 views

Multiple Regression - Residual Analysis

I am doing a multiple regression analysis regressing GPA against several 0/1 indicator variables (representing course completions). My fitted vs. residual plot is biased and looks awful. See below. Is ...
4
votes
2answers
600 views

How to perform residual analysis for binary/dichotomous independent predictors in linear regression?

I am performing the multiple linear regression below in R to predict returns on fund managed. reg <- lm(formula=RET~GRI+SAT+MBA+AGE+TEN, data=rawdata) Here ...
3
votes
1answer
47 views

How to perform residual analysis for weighted linear regression?

How do we perform residual analysis (verifying homoskedasticity, normality and independence of errors) for weighted linear regression? By weighted, I mean each row in the dataset has weight assigned ...
2
votes
1answer
67 views

Exponential regression residual check

If you have an exponential regression of the form log(y) = b0 + b1x with predicted equation ลท = 10^(b0 + b1x) and you need to ...
2
votes
3answers
438 views

What kind of residual plot does this variable have?

I am doing a multiple regression analysis and my focus is finding the best set of independent variables for prediction. I am starting to know my dataset and the behavior of each variable. I am doing a ...
0
votes
0answers
71 views

F-adjusted Mean Residual Test in R

I'm running diagnostics on a logit model in R produced with glm(formula = formula, data = data, family = "binomial"). I'm ...
1
vote
3answers
150 views

How does the inclusion of an intercept change the variability of the residual?

I want to use the variability of the residual as a measure M and then test whether M is higher or lower after some event. However, I estimate separate regression before and after the event to obtain ...
0
votes
1answer
80 views

Multlinear regression: analysis of residual of transformed response and predictor variables

In the first step of modeling a regression equation I came up with the following model: $T_c = 26.73 + 0.042{\rm Sc} + 0.247{\rm Lc} - 14.709{\rm Lf} + 1.41{\rm Lu} - 0.214{\rm Fc} + 0.041{\rm Ad} - ...
1
vote
1answer
1k views

R - Confused on Residual Terminology

Root mean square error residual sum of squares residual standard error mean squared error test error I thought I used to understand these terms but the more I do statistic problems the more I have ...
4
votes
1answer
427 views

Residual plot for nonlinear regression

I have a couple of questions regarding performance of nonlinear regression models. Are the residuals from a nonlinear regression model supposed to be randomly distributed too (as in linear ...
3
votes
1answer
42 views

Non-constant standard deviation in residuals

I am fitting a model in the frequency domain, and my fit looks as follows: As you can see, the model function does not fit the data perfectly, especially in the higher frequencies. So, I examined ...
1
vote
0answers
95 views

Goodness of fit for orthogonal least square regression

We have a set of XY coordinates which we have fitted with an orthogonal least square regression model. We have a vector of residuals for each point and the fitted line. How do we assess goodness of ...
3
votes
1answer
128 views

validity of regression assumptions on residual plot

I am learning regression course.For a homework given the residual plot I have to analyze it.This is how I interpreted it.I want to know if there are any wrong interpretations. 1)Since the variability ...
1
vote
0answers
63 views

Minimizing the sum of squares of autocorrelation function of residuals instead of sum of squares of residuals

I am trying to fit my multi-exponential model to some experimental data and I am using a simulated annealing algorithm. My objective function has so far been the sum of squares of the residuals: ...
1
vote
1answer
602 views

Outlier detection in ARIMA model with R

After fitting my time series with an ARIMA model, I want to test outliers in the residuals' series. Are there any functions in R that could do this test and furtherly test whether the outlier is ...
2
votes
1answer
694 views

Non-normality of residuals in linear regression of very large sample in SPSS

I have a dataset of ~17,000 cases in SPSS 21 with which I am trying to run multiple linear regression. I have plotted the Studentised residuals against the unstandardised predicted values and also ...
1
vote
1answer
83 views

How to describe the failure of this linear modelling?

I have a time series $X_t$, which is shown in the first plot. In the second plot, I am doing a linear regression on $X_t\sim X_{t-1}$. The regression line is very close to $y=x$. But this is tricky ...
4
votes
1answer
113 views

Why are residual plots constructed using the residuals vs the predicted values?

I am interested to know why residual plots are plotted with residuals against predicted variable of y and not against y?
0
votes
0answers
153 views

Robust Residual standard error (in R)

I have a question regarding to the concept of robust standard errors. What I found about that topic is, that one can estimate the robust standard error for regression coefficients to eliminate ...
2
votes
1answer
111 views

Analyzing residual plot vs independent variables plot

Why do we analyze residual plot in regression analysis and NOT between two individual variables? For example when checking for normality, heteroscedasticity etc. we don't analyze two individual ...
2
votes
1answer
569 views

Multiple testing correction for chi squared residuals

I am running cluster analysis (using mclust in R) and then looking to see whether various known data groupings (based on ...
4
votes
1answer
871 views

Interpreting a residuals vs fitted plot and extracting points

I'm doing a multivariate linear regression with R, and i find myself with the following residuals vs fitted plot: As you can see there is a very regular line of points that seems to follow a ...
2
votes
1answer
889 views

Reasons for autocorrelation in time-series residuals

Why are residuals usually autocorrelated in time-series data? Could it stem from the autocorrelation of the response variable? Is the reason that in some cases the differencing (i.e., the differences ...
8
votes
1answer
2k views

Sandwich estimator intuition

Wikipedia and the R sandwich package vignette give good information about the assumptions supporting OLS coefficient standard errors and the mathematical background of the sandwich estimators. I'm ...
1
vote
2answers
1k views

In R, how can I transform to normalize residuals when I have a U-shaped Q-Q plot?

I am running a two-way ANOVA with one random variable. My histogram of the residuals is showing considerable (negative?) skew: And my Q-Q plot of the residuals shows a corresponding U-shaped ...
0
votes
0answers
81 views

Hypothesis testing using only RSS

I have been presented with an interesting regression question: Suppose I have a "black box" that will calculate the residual sum of squares: $RSS=(Y-X\hat{\beta})'(Y-X\hat{\beta})$ for any standard ...
15
votes
3answers
24k views

Regression when the OLS residuals are not normally distributed

There are several threads on this site discussing how to determine if the OLS residuals are asymptotically normally distributed. Another way to evaluate the normality of the residuals with R code is ...
15
votes
2answers
9k views

Interpreting residual diagnostic plots for glm models?

I am looking for guidelines on how to interpret residual plots of glm models. Especially poisson, negative binomial, binomial models. What can we expect from these plots when the models are ...
1
vote
1answer
678 views

Shapiro-Francia test error

I'm trying to run a normality test on the residuals after fitting a mixed-effect model (with lmer). I read that the Shapiro-Francia test can deal with data with more than 5000 observations (I have ...
2
votes
2answers
417 views

Bayesian inference of parameters: residuals are independent but not normally distributed

I would like to compute belief intervals (confidence intervals; CI) for the parameters of an environmental dynamic model within the Bayes' theorem. The measurement model of the data is $$ ...
2
votes
1answer
115 views

Is it ok to bin residuals before examining them?

I'm analyzing the residuals from a regression model fit to a dataset that covers several years worth of data. I want to report the sum of the residuals from that model, by year, as a measure of how ...