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

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How to make stochastic gradient descent algorithm converge to the optimum?

(Background info taken from my blog) In logistic regression, the hypothesis function, which models the relationshiop between the dependent variable $P(y = 1)$ and the independent variable $X$, is : ...
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18 views

Explanatory power of variables in multiple regression

Suppose that I have a regression model with two explanatory variables: $Y = b_0 + b_1X_1 + b_2X_2 + \epsilon$ The determination coefficient $R^2$ measures the amount of variability in the response ...
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14 views

Feature scaling for classification and regression

Is it true that one should generally scale each of the features before feeding them into common classification models such as Support Vector Machine, Logistic Regression, etc? What about for ...
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Are these descriptions of batch gradient descent algorithm conflicting each other?

The first one is from Andrew Ng The second one is from Francis Bach I might be a little confused, but why is there a summation of partial derivatives in the second description and none in the ...
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10 views

Standardizing count variables in panel data with overdispersion - R or Stata

I'm running a regression where the dependent (response) variable is a highly dispersed (slightly zero-inflated) count and the explanatory (independent or predictor) variables are continuous, counts as ...
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20 views

What is multi run lasso regression?

I have problem in understanding of multi-run lasso regression. Basically, I know what is lasso regression, but don't know what is multi-run lasso regression, which sometimes I see literatures. Does ...
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What is PRINCIPAL HESSAIN Direction Model, how and where can I use it?

I'm a M.Tech student going through my academic project-work, here I have asked to develop a optimal design and a best equation to fit the data for a Leaching, Solvent extraction and Electrowinning ...
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33 views

confidence intervals for values estimated from the nonlinear regression model

I have a question about nonlinear regression and confidence intervals for values estimated from the model. Here is my problem. I have sets of data where $X$ is the logarithm of the dose of a chemical ...
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Fit a GARCH (1,1) - model with covariates in R

I have some experiences with time series modelling, in the form of simple ARIMA models and so on. Now I have some data that exhibits volatility clustering, and I would like to try to start with ...
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Parameter tuning in lars (lasso) matlab

I am trying to use lars (matlab implementation:http://www.ece.ubc.ca/~xiaohuic/code/LARS/LARS.htm). I want to do a leave one out cross validation on my data using this code. I have the following ...
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35 views

Multiple linear regression through orthogonal matrices

An example of linear regression could look like: $min \sum_{i=0}^{m}||x_i A - y_i||_2^{2}$, where ${x_i, y_i} \in \mathbb{R}^n$ and $A \in \mathbb{R}^{n\times n}$. I am interested in knowing how do ...
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How to interpret a regression that includes GDP, GDP per capita, and population

While GDP per capita = GDP / population and are obviously related, these three measurements are not perfectly collinear and can be included in OLS just fine. ...
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Goodness of fit of a SUR model

I know that McElroy R^2 is a measure of goodness of fit for Seemingly Unrelated Regressions (SUR models), but how can one judge that the estimated equations are well fit by using the McElroy R^2?
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How to fit a model for a censored binary outcomes?

Suppose I have a data frame such that: ...
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21 views

Difference-in-difference more than two periods [on hold]

I try to use a difference-in-difference design in my study. But I have three periods. So I would like to know how I could run my difference-in-difference analysis on Stata with more than two periods.
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50 views

What does it mean to “fit a regression function” and then use it to update other functions?

Referring to the algorithm on page 11 in this paper on boosting algorithms, I really don't understand step 2, (ii) and (iii). What does this mean: (ii) Fit the regression function $g_j^h (x)$ by ...
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41 views

Why do both the VIF and tolerance statistics exist, when the latter is just the reciprocal of the former?

Is taking the reciprocal helpful in some way, or is it just a matter of historical accident that there came to be two terms to describe the same thing? (From the Wikipedia page.)
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55 views

Treating ordinal variables as continuous for regression problems

In the social sciences I have encountered that it is common to treat ordinal variables as continuous, for example variables originating from rating or Likert scales (strongly disagree, disagree, ...
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48 views

Combining summary statistics

Currently I add Var1+Var2=VarSUM12 and then perform a linear regression on VarSUM12~x to obtain test statistics for x and get insight into weak associations that are present across Var1 and Var2 with ...
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Analyzing relationship between two Likert-items (is it possible?)

I am a student and am new to statistical market research. Is it possible to analyze the dependence of answers to one Likert-type item on answers to another Likert item (predictor)? If so, what test ...
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Why would I want to use my data this way, and what am I supposed to do with this regression?

I'm writing an essay on the effects of M&A announcements on stock prices. I understand most of the requirements perfectly fine, but I'm stumped on one particular part of the essay. I have for ...
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40 views

How should you fit ANOVA and linear regression models, if the equal variance assumption is violated?

This is my topic for the paper I'm working on for an undergrad stats class. It's supposed to be 20 pages... and I'll be honest, I understand very little beyond the basics and am over my head. From ...
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61 views

Piecewise regression

I have a time series where I want to fit a piecewise regression equation. Now the problem occurs when I try to fit equations of different degree in different segments of the series. Please provide me ...
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51 views

How to get the regression from a plot?

I have a dependent C and a independent variable VPT. The plot between the two looks like this: I want to create a regression ...
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Considering correlation of independent variables in regression model

I have one dependent variable C and four independent variables: S SD ...
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36 views

Can a Linear-Log model be used instead of Robust Standard Errors?

If your regression model has heteroskedastic residuals, one should calculate White Standard Errors that correct for the mentioned heteroskedasticity. If the residuals are also autocorrelated one ...
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51 views

Interpretation of Maximum likelihood estimation

I have some problem to interpret the result of MLE estimation : Is it possible to get some advise about how to interpret it? the log likelihood function : $\sum^{n}_{i=1}\log\left( \phi\left( ...
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21 views

Dummies instead of the Chow test

I have found somewhere a mention to the possibility of using dummies variables instead of the Chow test to test whether the coefficients in two linear regressions on different data sets are equal. ...
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Testing whether two regression coefficients are significantly different (in R ideally)

If this is a duplicate question, please point to the right way, but the similar questions I've found here haven't been sufficiently similar. Suppose I estimate the model $$Y=\alpha + \beta X + u$$ ...
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25 views

What statistical tests for headache journal?

I track my pain levels in an online spreadsheet along with daily habits and trigger events. I want to test whether changes in my pain over time follow a trend (not concerned whether it is linear or ...
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47 views

Homoscedastic and heteroscedastic data and regression models

How to understand the homoscedasticity and heteroscedasticity in context of regression models? Is there a way to check these properties in R?
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52 views

How do I run Ordinal Logistic Regression analysis in R with both numerical / categorical values?

Base Data: I have ~1,000 people marked with assessments: '1,' [good] '2,' [middle] or '3' [bad] -- these are the values I'm trying to predict for people in the future. In addition to that, I have some ...
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12 views

Partitioning variance from logistic regression

Short version How can I partition the variance from the different levels in a nested mixed-effects logistic regression? Preferably using R, but even general principles would be helpful as a start. ...
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44 views

correlation with logarithmic transformation

I have a dataset of 2.000.000 projects. Each project is defined by its size and the number of active developers. After applying a logarithmic transformation on the project size, I've plotted the ...
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How to estimate the effect of changes happened in employee designation/title on salary?

Is it possible to estimate the beta changes corresponding to employee's departmental change or title change or Both. Can i follow linear regression directly to calculate slope ? I am using R ...
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How to adjust for a continious variable when the value 0 is distinctly different from the others? [duplicate]

Lets say I want to regress a variable on a covariate which has distinct value zero but the all other values are following some smooth function? It could be e.g. Years of beeing a mom (once you are a ...
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2answers
54 views

Variable Selection [duplicate]

I am using R/RStudio to code a regression (and to optimize the function) over 50+ different variables. For the optimization to work I need to fit a higher order function (I am not sure to what degree ...
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Investigating relationships between variables- multiple and simple linear regression

please help a sociologist struggling to get to grips with R and statistics in general..! I've got a data set with 11 variables. I need to investigate the possible relationships between one of the ...
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Difference between RMSE and Spearman Correlation

I am trying to evaluate model performance (regression problem). In literature, some use RMSe and others use correlation. Is there any difference between both the approaches? Here: What are good ...
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74 views

Adding predicted probabilites from logistic regression instead of using cut value

I am using a logistic regression model to predict a binary decision (purchase, don't purchase) based on several independent variables (income, age, education, etc.) for a population of individuals ...
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Including non-hypothesized interaction terms in moderation analysis

I am running a moderation analysis using hierarchical linear regression. I have two predictor variables and two moderating variables and I am interested in their interactions to predict an outcome. My ...
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Understaning coefficients in summary output of logistic regression in R

This question is about understanding the logistic regression output using R. Here is my sample data frame: ...
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In simple linear regression, why the covariance between y bar and beta1 hat is zero? [duplicate]

In simple linear regression, where the model $$\mathbb{E}(y) = \beta_0 + \beta_1 x$$ is estimated using least squares (and the errors are assumed iid of mean $0$), why is the covariance between the ...
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15 views

Non-independence of data in regression model

I have a problem concerning non-independence of data in an experimental economic game. Participants are in groups of three and interact with each of the other two. Each interaction between persons A ...
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25 views

Regression - volatility vs return

I am attempting to estimate a linear model as: $$ y = a +bX +e $$ I have a series of annual returns and I would like to estimate the effect of volatility on losses. My Null hypothesis is that ...
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Derivation of a fixed effects estimator

I've come across parts of a derivation of a fixed effects estimator in a paper i don't understand. The log likelihood function is where $Y_i=(Y_{i1},...,Y_{iT})'$, and $X_i$ is a $T\times k$ ...
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7 views

Newey–West estimator - Number of time observations needed

Are there some general rules, or Monte Carlo studies, that shows evidence of the approximate number of time periods needed to implement the Newey–West estimator?
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Calculating relative importance of independent variables in Linear and Logistic regression

After fitting Logistic/Linear regression model, we get estimates of parameters showing importance of attribute in predicting dependent variable and can be considered as weight of that independent ...