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

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When to use bootstapping in regression analyses?

When I run a regression analysis in SPSS, one of my predictor variables just fails to reach significance, p = .06. When I apply bootstrapping, the output tells me the predictor has a significant ...
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18 views

How to analyze relationship between data pairs

Please change the title if it's not accurate, I might be lacking terminology. I have two variables (x,y) which are directly correlated by pair. For example I have sampled 50 trees in my study area ...
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Regression using a priori knowledge

I am sorry if the title (and probably the question) is not very clear but I have a regression problem which might be a bit over my head if I want to do it well. I am only interested in getting some ...
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Analysis of a longitudinal study where interventions are received at different time points

I have a data set of university students. The university has 8 different assisting programs, mostly like scholarships, for needy students to help them concentrate on their studies. Since it costs a ...
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15 views

mixed effect regression model

I would like to expand over a previous question of mine (What is the correct model for this experiment design?), with slight modifications... To set up the scene : We have a completely randomized ...
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1answer
63 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. ...
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17 views

Phylogenetic General Least Squares, Multivariate Regression

I'm working on a biological question, with species data derived from an external database, which has multiple response and predictor variables. As a result, I want to do multivariate regression across ...
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32 views

Selection of regressors into a regression model

Why is it that backward selection/elimination as compared to forward selection of regressors, is often less adversely affected by the correlative structure of regressors?
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41 views

Choosing predictor for regression

If a predictor is almost the same as the criterion (different tests measuring same domain and they are both highly correlated) would that predictor be worth-including in the regression model? What if ...
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42 views

R regression difference between factor and numeric

I have a set of data that I am using regression analyses on. All of the columns are numeric (as far as I can see) a mix of integers and reals. However, two of the columns are being read from the CSV ...
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34 views

How to efficiently compute Theil-Sen estimator?

The Theil-Sen estimator is of interest to me, however when I implement it myself I end up with something that scales as O(n^2). According to wikipedia, it can be calculated exactly in O(n log(n)). Can ...
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5 views

How can Durbin-Watson and SPEC give Opposite Results?

I am modelling house prices against sales amount using a simple linear regression model. My SPEC (Option in SAS) says IID (p-value > 0.05) but my DW (Option in SAS) says a strong 1st order ...
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26 views

Probability distribution of Y in regression?

I'm trying to predict the probability distribution of $Y$ given $X_0, X_1, ...$ with a nonlinear regression. The probability distribution of $Y$ is likely not normal. So far, I've set up and trained ...
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Correlation? Factors affecting academic performance of students based on their gpa? [on hold]

My friend's thesis. just helping her with the data analysis "FACTORS AFFECTING ACADEMIC PERFORMANCE OF STUDENTS BASED ON THEIR GPA" Divided it into: Internal Factors: Class Schedule & parental ...
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19 views

How do I compare different models? [on hold]

I have four different models with a different amount of significant variables. One is a simple linear model, one is logarithmic, one is logarithmic with a saturation rate and the last includes a ...
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19 views

Incorporating outlier points into a unified prediction model [on hold]

There is a data set including multiple outlier points. If we want to build a regression model based on these points, what are the general approach to handle these outlier points? Are there any good ...
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17 views

The way to evaluate the importance of an independent variable in a regression model

In a regression model, like y~( x1, x2, x3). Is there a test or a way to evaluate which independent variable, x1, x2, or x3, is ...
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What exactly are censored data?

I have read different descriptions of censored data: A) As explained in this thread answer, unquantified data below or above a certain threshold is censored. Unquantified means data is above or below ...
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12 views

Auto.arima choose between lots of regressors

I have to forecast data with two seasonality with ARIMA. I find that I have to use a code like this: ...
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Distinguishing between different notions of $R^2$

What is the distinction between $R^2_{pop}$ – the population R-squared $R^2_{out}$ – the out-of-sample R-squared $R^2_{c.v.}$ – the squared population cross-validity coefficient ? These ...
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49 views

Linear regresson lm or stepwise regression here using R?

It is a basic question but I could not find clear answer on my reading. I am trying to find independent predictors of Infant.Mortality in data frame 'swiss' in R. ...
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1answer
10 views

Generating Regression For Unknown Beta in Matlab

My professor wants me to generate a regression problem based on the following: B is fixed unknown 100,100 matrix, X is random 100,100 matrix and y and noise are a random scalar for every output. He ...
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3answers
98 views

How to fit regression to custom model in R

This is for my honors thesis. I have a large data set, of which I'm sharing only what I call the "Low phosphorus" series: ...
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Run Tobit with missing values (from linear prediction) or change them to zeros - STATA “practical” question

I want to run a Tobit linear regression in order to especify a labor-supply curve (linear-linear). As dependent variables I have personal characteristics, enviromental ones, and wages (predicted using ...
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25 views

Modelling interaction

How does adding interaction term in the model adjust for it or why do we need to add interaction? I am working on logistic regression model with treatment and race as predictors. I have added ...
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2answers
35 views

Fitting decaying exponential to binary response

I have this data that I want to fit with $y = e^{-bx}$, but the y:s represent probabilities and the outcomes are either 0 or 1, so I can't say $ln (y) = -bx$ since the values will just alternative ...
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70 views

Sample $R^2$ consistent?

In a linear regression context: is the sample $\widehat{R^2}$ a consistent estimator of the population parameter $R^2$? Maybe this depends on distributional assumptions?
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When to use skew normal regression via MCMC (mixture models)?

when do i use skew normal or skew t Regression via MCMC? Do I use them when the data are heavily skewed, for example income data? Or do I fit a normal Regression model first and inspect the residuals ...
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algorithm to predict cost function

The goal of problem is to predict the weight for missing data . I have a dataset of categorical type as shown below ...
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2answers
146 views

When can a continuous variable be treated as categorical?

I have a continuous variable, which can take any value between 0 and some large, though not infinite, number. Let us assume that the maximum possible value is 1000. The values are nowhere near ...
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1answer
36 views

Interpretation of logged regression

I have run a linear regression with the following equation (in r): lm(formula = logTotal ~ Continent + logArea + Method + Servs) where ...
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22 views

Predictive model with combinations of dummy variables of different length

I would like to try to predict the amount of a public contract based on historic records where the main variables that I can fit against include: contact duration (continuous) number of buyers ...
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11 views

Combining outputs from multiple regressions

I have a sample N with K independent variables (and 1 binary dependent variable) Of this K, a number (L) are avaliable for all of N. Of the rest (K-L), different subsets of N have different sets of ...
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1answer
23 views

3D surface plot for least square & ridge regression

I'm very impressed by this plot: Why does ridge estimate become better than OLS by adding a constant to the diagonal? Does someone has any clue about how to plot this on R? I mean, how to get RSS ...
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32 views

Stochastic Gradient Descent for Logistic Regression always returns a cost of Inf and weight vector never gets any closer

I am trying to implement a logistic regression solver in MATLAB and i am finding the weights by stochastic gradient descent. I am running into a problem where my data seems to produce an infinite ...
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Regression using indicator variables and time t. (R code) [duplicate]

I'm having difficulties setting regression R code.... This is the data that I need to use: ...
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11 views

Regression using indicator variables and time t. (R code)

I'm having difficulties setting regression R code.... This is the data that I need to use: ...
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Regression Equation Question [on hold]

Two researchers use the same data set of social support scores in depression scores collected from a group of 40 women which chronic fatigue syndrome. One uses the original scores of each variable. ...
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1answer
26 views

Least Angle regression coefficient reaches zero after included

In LARS how is it possible that after including a variable it could reach zero again? http://www.cc.gatech.edu/~isbell/reading/papers/lasso_simple.html.pdf I understood that it works like: 1) choose ...
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Conditions on ratio of $\frac{\sigma_{BIB}^2}{\sigma_{RB}^2}$

I'm attempting to solve a question about the variances of random block design versus balanced incomplete block design. I feel I am close to having an answer, but I'm not quite there yet. Question: ...
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21 views

How to Predict using Quantile Regression [duplicate]

Were you able to figure out your question about what quantile prediction to use for the forecasting purposes? Different quantile regressions produce different estimates.
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1answer
94 views

What is the difference between logistic and logit regression?

What is the difference between logistic and logit regression? I understand that they are similar (or even the same thing) but could someone explain the difference(s) between these two? Is one about ...
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1answer
48 views

“Better fit” using aggregated data in comparison to disaggregated data: explanation?

I have fitted multionomial regression models to two different datasets, but from the same country, corresponding to the same event. Dataset A is an aggregated dataset (at country level), relating a ...
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30 views

How Are Regression Residuals Calculated - Specific Example

I am trying to figure out how regression residuals are calculated using the specific example in the attached graphic. Would I simply B-A (Red letters in graphic) to get C so: 22-30 = - 8 in this ...
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Weka SMOreg and LIBSVM with linear kernel problems

I want to test a dataset in weka using either LIBSVM with an e-SVR or SMOreg for regression. I also choose a linear kernel in both (in SMOreg i use an exponent=1 in a non normalized polykernel). ...
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small observations but many independent variables for multiple regression, can i have dummy variable?

The dataset i have consists of Y(N=80) and X(60 IVs). they are all numeric. I run multiple regression using two variable selecting methods: 1. stepwise 2. all possible regressions. I get very high ...
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Is there a list/catalog of types of regression to choose from?

In my practice, I often find out that after the basic assumptions for linear regression are not met, I have to discover a new type of regression I should use. For example, in the past, I came across ...
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13 views

Is it Complete or Partial mediation? Or no mediation at all?

Kindly explain the given situation, is it partial, complete, or no mediation at all?
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1answer
26 views

Big Data Regression Coefficient Estimation

I am working on a very large data set (n = 6.5 million) and I am trying to come up with a simple linear regression between two variables. I am working in R and using a monte carlo style simulation to ...