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

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Measuring impact of advertising on retail sales

I have a dataset of retail products which contains weekly sales for 12 different items in a single category. For each item, I have three dummy variables representing different types of advertising ...
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25 views

Difficulty plotting regression in R

I am trying to plot a regression using plot() and keep getting the error message "Error in xy.coords(x, y, xlabel, ylabel, log) : 'x' and 'y' lengths differ". ...
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7 views

Multilevel models benefits vs. separate group models

What are the benefits of multilevel models vs. running a separate model for each group? My understanding is that MLM offer a method to effectively model interactions against all the base predictors. ...
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13 views

Assessing the need for random effects terms

During the model selection phase for mixed models, there are typically several possibilities to choose from; in fact, the number of possibilities is increasing in the number of covariates used. How ...
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19 views

When adjusting for X1, have we adjusted for X2, to the extent that X2 is related to X1?

I've just read Elizabeth Stuart's paper on matching methods (http://biostat.jhsph.edu/~estuart/Stuart10.StatSci.pdf), which I find very informative. She discusses propensity score methods and the ...
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Help with analyzing/planning a physical experiment — regression

This is a real experiment about to be performed Batches of samples will be prepared. A Striker will be used to see if a reaction will occur. For example: 20 drops might be performed, and number of ...
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I have data independent variable scoring 1 to 4 and a dependent variable number of people dependent variable,,,,,Which regression or model i prefer [on hold]

Access to water , Road , Sanitation, parks etc 1=25% have access 2=50% have access 3=75% have access 4= >75% have access Dependent Variable Number of people in a particular area... Which model or ...
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26 views

Analyzing ordered factor vs continuous variable

I have data of 50 students of a class as follows: build : an ordered factor with levels 'low', 'medium' and 'high' score : a continuous variable (values 1-9) ...
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10 views

what could be the best method to find best scenario based on revenue(statistical analysis) in python?

I have a data set on which I want to do some statistical analysis. The sample data set is in a csv file and of following nature: ...
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6 views

MSE of training set and validation set for linear regression

The wikipedia article on cross validation http://en.wikipedia.org/wiki/Cross-validation_(statistics) makes the claim that "under mild assumptions that the expected value of the MSE for the training ...
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Can I mix data and do a regression and/or can I sum multiple regression formulas for a 'master' formula? [on hold]

I need to make a formula for the line of best fit (trendline) for multiple regressions, (and I'm working with a college-level statistics knowledge so please forgive my ignorance). The data I will be ...
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8 views

Standardized Regression Coefficients for categorical interactions: lm.beta() vs. regressing standardized variables

I am working with a regression model from which I would like to compute standardized regression coefficients. I am writing primarily regarding an observed discrepancy between coefficients obtained by ...
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Maximum number of alternatives in a discrete choice model

We are modeling a discrete choice scenario, with alternative-specific coefficients. We also break the assumption of independence of irrelevant alternatives. To model this, we are using an ...
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37 views

What is the significance of a linear dependency in a polynomial regression?

I'm trying to find the best polynomial regression for a dataset where the polynomial's power is between 2 and 10. So the regression can have an x10 term at most in it. The dataset itself is simply a ...
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9 views

Can you regress index valued variables with variables (actual numbers)?

I am doing my dissertation and looking at the impact of education on economic growth. I will be carrying out Panel data regression on certain variables to see how this impacts Economic Growth (GDP per ...
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28 views

Preventing overfitting with Least Squares Linear Regression via QR decomposition

I am trying to solve a linear regression problem in an automated fashion, however am having a problem with extremely large weights. I have several thousand datasets, and am running linear regression ...
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16 views

How does one interpret a demeaned log interaction term?

I am having problems interpreting my regression equation. I want to know the effect of an increase in variable $x$ on $y$ for different values of $z$, but as it's in logs and the interaction term is ...
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37 views

Can bootstrap re-sampling be a re-sample of a smaller size

i am attempting to run a smaller instance of my regression panel data , because it is a pretty huge regression (Fixed effect, heckman selection) and it takes 4 hours to run every time. I am ...
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7 views

Question about a control variable for ''progressitivity measures (Taxes)'' (economics and econometrics)

Can anyone explain to me what this sentence means econometrically? I mean what I am supposed to do in order to conclude to one control variable (time series). ''The progressivity measures are obtained ...
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29 views

How to find the long-run relationship using this regression (3rd time posted)

I know this is unorthodox but the exam is in 15 hours and if a question like this turns up I'll be unable to answer it. I've posted this twice already, the first time it was put on hold and the second ...
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Problem Using leaps() code in R [migrated]

In my case, the data consisted of 8 variables and 500 observations. When I used the leaps() code, instead of showing the $2^8 - 1$ submodels, the output showed only ...
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10 views

Analyzing whether several categorical variables might have a causal relationship with a dependent variable

I am trying to use some data to assess why certain terrorist organizations claim responsibility for their attacks and why sometimes they don't. I have a data set that contains relevant information so ...
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10 views

relation within Gauss-Newton method for minimization

If we study model fit on a nonlinear regression model $Y_i=f(z_i,\theta)+\epsilon_i$, $i=1,...,n$, and in the Gauss-Newton method, the update on the parameter $\theta$ from step $t$ to $t+1$ is to ...
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49 views

Simple question on odds ratios interpretation

I am trying to interpret the Odds Ratios (ORs) from a multiple logistic regression model that compares the performance of various clinics in terms of preterm birth rate (measured as "Yes/No preterm ...
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35 views

OLS with ordinal dependent variable - do the coefficients mean anything?

I currently read a paper in which the author has asked people 3 different questions regarding their life satisfaction, all of which are to be rated on a four point scale: 1) very low, 2) low, 3) high, ...
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33 views

Doing multiple regression without intercept in R (without changing data dimensions)

I am trying to calculate multiple regression in R without intercept. My data is as follow: ...
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which test should I apply to find which model is significantly different? [on hold]

I have two questions 1- regression I have performed many regression models (10 models) and therefore, I have 10 prediction columns and one column or more for my real data (independent variables) ...
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multiple linear regression with interactive categorical variables

I want to include in a multiple linear regression model, the interaction between categorical variables. I have three categorical variables: CO2 (0,1) Temperature (0,1) Soil (1,2,3) But when i ...
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141 views

When to use Log in Regression?

I saw this sentence: "I use log(income) partly because of skewness in this variable but also because income is better considered on a multiplicative rather than additive scale. In other words, ...
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24 views

Coefficient Decreases but Standard Errors stay the Same with Inclusion of Control Variables

I estimate 2 models in OLS. $Y=\beta X+e$ and $Y=\beta X+\gamma W +u$ The inclusion of the $W$ variable decreases the size of $\beta$ but does not change the $Var(\beta)$. $X$ and $W$ are not very ...
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How predictive means in a GP could become negative when both the prior and the training target values are non-negative?

I am training a Gaussian process regression where the training target values are between 0 and 1 and the prior mean is the fixed zero function. The predictive mean sometimes becomes negative e.g. ...
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Working on my dissertation, and need to figure out whether to use MANOVA, MANCOVA, Multiple regression, or multivariate regression

I am working on my dissertation, and I am having a hard time choosing a statistical model to follow. I am using an existing dataset. The results of the dataset are self-reported. There are more than ...
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76 views

Why $y_i$ becomes $(y_i-\overline y)$ in linear regression

Trying to figure out why $y_i$ becomes $(y_i-\overline y)$ in the below expression for finding $\widehat{\beta}$. Any help is highly appreciated.
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How to calculate likelihood of linear regression

This is a pretty basic question, but one I am having a hard time finding an answer to. How do you calculate the likelihood of a simple linear model? Like, say, $$y=\beta_0+\beta_1x+e$$ I am working on ...
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24 views

Relationship between the parameters of the Normal distribution and parameters in the probit with multiple predictors?

According to A. Agresti (2007, p. 73) in binary probit regression: "The parameters of the normal distribution relate to the parameters in the probit by mean (mu = -alpha/beta) and standard deviation ...
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8 views

Approaches for multivariate nonlinear (but parametric) regression?

I have some data that is nonlinear, but is curvilinear. It is two factors and a dependent variable. Each factor has a pretty good fit when applying a growth curve when compared against (this is ...
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Weighted GLM without weights

Suppose we have at our disposal a glm() that's got all the typical features except the ability to specify weights. Intuitively, I can trick it into using weights ...
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32 views

Using multiple regression to predict x values

I understand the basics of running regression. I have used it in the past to create predictor values for engineering problems. For instance, how much cooling does a system need if previous systems ...
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how to specify range of lag in prewhiten CCF using package TSA in R

I am doing time series regression using package TSA in R. I have 2 time series, say x and y, so I started by doing prewhitened CCF. So, 2 issues I have encountered and would really appreciate if any ...
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Reverse-engineering a (custom goods) pricing algorithm - each db row has: | 3 factors | 2 co-variates | price | (I have over 100k rows of data) [on hold]

just wanted to mention up front that my question doesn't concern dynamic pricing, price optimization, revenue management, etc. No time series analysis either. It's just a simple multivariable ...
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74 views

Regression results have unexpected upper bound

I try to predict a balance score and tried several different regression methods. One thing I noticed is that the predicted values seem to have some kind of upper bound. That is, the actual balance is ...
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22 views

bonferroni and scheffé' simultaneous confidence interval graph in minitab

I need to calculate bonferroni and scheffé' simultaneous confidence interval by hand as a homework. However, I also want to add minitab outputs and graphs to my homework task. How can I plot these ...
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37 views

Appropriate regression-like model where the response is on half-integers

What is an appropriate model for the above scatter plot? I am not fully satisfied with a simple linear regression model. Any suggestions? Y in this problem is discrete in nature. It only increments ...
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How to fit quantized data

I have some measurements which were quantized during measuring. Now I want to get the overall change during a period of time, e.g. 15 minutes. Because of quantization however, the same value is ...
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27 views

Project help [undergraduate] [R]

I have to do a project for my statistics course, which I have now finished but would very much appreciate some insight and help as I've never done anything like that before. I was given data on ...
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44 views

Fitting GLM with Quasi-Newton method

I'm trying to code my own quasi-Newton algorithm for fitting GLMs in R. My results do not match up with glm and I've been over my code many, many times so I'm ...
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Negative real wages and log wages [duplicate]

Can someone please explain conceptually what a negative value on the min value of log of wage means? If wages are not negative why would the minimum value of real wages in PPP and log of wage be ...
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Can $x'x$ be written as correlation matrix?

$x'x=$ $$ \begin{bmatrix} \sum_{i=1}^{n}(X_{1i}-\bar X_1)^2&\sum_{i=1}^{n}(X_{1i}-\bar X_1)(X_{2i}-\bar X_1)\cdots & \sum_{i=1}^{n}(X_{1i}-\bar X_1)(X_{ki}-\bar X_k) \\ ...
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How is the first column of the matrix orthogonal to all the others

$$ \begin{bmatrix} 1 & (X_{11}-\bar X_1)\cdots & (X_{r1}-\bar X_r) \\ 1 &(X_{12}-\bar X_1)\cdots & (X_{r2}-\bar X_r) \\ \vdots &\vdots &\vdots\\ ...
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How to determine the accuracy of regression? Which measure should be used?

I have problem with defining the unit of accuracy in a regression task. In classification tasks is easy to calculate sensitivity or specificity of classifier because output is always binary {correct ...