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

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Regression and posterior

Let $y_i$ follows $Bin(n_i,p_i)$ and for $p_i$ we consider the logit quadratic model: $\log\frac{p_i}{1-p_i}=\beta_0+\beta_1A_i+\beta_2(A_i-meanA)^2$ where $A_i$ is AGE_i during ith time. It is part ...
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1answer
17 views

Data set generator

I'd like to ask if someone knows a good data set generator for multi-target/Multivariate regression All the best Richard
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33 views

Maximum Likelihood Estimation of Coefficients for Linear Regression Model

I am reading up about using Maximum Likelihood to estimate the parameters of a linear regression equation. I came across this video (https://youtu.be/_-Gnu498s3o) and I thought it explained it very ...
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12 views

Linear Regression for Forecasting: what are the risks?

I have a dataset containing financial data of multiple firms on 7 to 10 years. (yearly data). For each firm/variable i want to predict its value in the next two years. I don't have enough data (7 to ...
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32 views

Covariance Between $\hat{\beta_0}$ and $\hat{\beta_1}$

Our model is $Y=\beta_0+\beta_1X+U$. We know that $\hat{\beta_0} = \beta_0 + \sum\limits_{n=1}^N c_nu_n$ and $\hat{\beta_1} = \beta_1 + \sum\limits_{n=1}^N k_nu_n$, where $$k_n = ...
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52 views

Poisson regression? [on hold]

If we have a $y_i$ random sample with a $Poisson(\lambda_i)$, $i=1,2,...,n$. Also $\log(\lambda_i)=(X\beta)_i$ where $X$ a known $n \times p$ matrix and $\beta\in\mathbb{R}^p$ is not known. Assuming ...
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11 views

Poisson regression residual analysis

In a three factor poisson (log-linear) model $(A*B*C)$, when the highest interaction term $(A:B:C)$ is dropped, the response/raw residuals are exactly the same for different levels of two of the ...
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1answer
18 views

Pattern detection in scatter plot

Below is a scatter plot (capped at $10k) representing the average donation a project receives vs the word count of the funding request essay for all projects represented in the open Donors Choose ...
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1answer
44 views

Exercise in Bayesian Statistics

What I want to do is the first part of the exercise : It is for chapter 14 of the book, an introduction to regression. I have minimal experience with regression and is the first time I see a ...
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7 views

Simultaneous Equation Model with Fixed Effects [on hold]

Can someone please clarify the appropriate method for running a 3 equation simultaneous equation model with fixed effects estimation? In my model, each of the dependent variables depend on the other ...
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1answer
17 views

Optimal Combination Forecasts

Can someone give me an intuitive explanation of how Optimal Combination Forecasts work for hierarchical time series? I've been reading Rob Hyndman's paper and understand that the resolved forecasts ...
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16 views

Heterogeneity testing in logistic regression models

We ran 5 separate logistic regression models testing a set of independent variables on 5 different country-specific datasets. Our goal was to assess the relative contributions of known risk factors of ...
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1answer
14 views

ARMA errors and combining explanatory variables

Currently I'm working on forecasting the employee turnover of an organisation. To do this, I'm using a time series data of the employee turnover over the past 7 years, it is an annual data. To make a ...
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1answer
10 views

Does the sample size for the dependent variable should be the same for all the values of the independent variable in Peason Chi-square?

I have a question about the sample size when using chi-square test of independence or multinomial logistic regression. I would if you provide me with your feedback since I search a lot and I could not ...
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28 views

Generalise multiple linear models in R

I have 3 variables of my cellular recordings and made 9 linear regressions for their connection to fourth variable (a~x1...9, a~y1...9 and a~z1...9). All variables are random, not controlled. Each ...
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9 views

Obtaining AICc weights after glm.nb

I am performing negative binomial regression using glm.nb() function from MASS package and calculating AICc using package "AICcmodavg". I need also to obtain the (AICc) weights using aictab() function ...
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51 views

How is L1 regulaziation derived?

I understand the basic idea of regularization. I am very curious to know the derivations behind it so that I get the complete picture. I was going though this paper and I didn't understand how ...
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1answer
24 views

How does Wiki article go from one line to the next (linear regression)

I am stuck on how to get from one line to the next, as I'd like to understand how. (Our course requires a detailed proof involving this.) Could anybody give me guidance how to show that LHS = RHS? ...
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8 views

Overestimation of the noise precision in Bayesian linear regression when $n\gtrsim p$

I would like to infer the regression coefficients and the noise precision of a standard linear regression problem defined by $$ y=X\theta + \epsilon, $$ where $X$ is a $n\times p$ design matrix, ...
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14 views

2SLS Regression, 1 Instrument effecting more than one explanatory variable?

So in my situation I have my dependent variable Log Income my Instrument Diabetes and my supposedly endogenous explanatory variable Reads Nutri. Where things start to get confusing for me is that ...
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11 views

plot the predictive distribution for a multiple regression

In detail, the file tv.csv contains price (in dollars, rounded), diagonal size of viewing area (in inches), brand (Panasonic, Samsung, or LG), and type (plasma or LED). As a function of size, ranging ...
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1answer
23 views

Building regression model with multicollinear continuous and categorical variables: can I use PCA?

I am trying to build a regression model that has continuous and categorical predictors. Furthermore, the continuous variables suffer from collinearity. My understanding is that PCA can handle ...
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42 views

Weighting variable based on another variable

Problem Suppose I have two variables: (1) heat index for each county in a state, $h_{it}$, and (2) acres in each county, $acres_{it}$. The data has 10 years and also includes a variable for the ...
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1answer
22 views

How to interpret hausman test results?

I'm trying to do 2 stage least squares regression in python using the statsmodels library. ...
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2answers
27 views

Simple Linear Regression, Serially Correlated Residuals, and Interpretation

Question Can I draw any proper conclusions about the linearity and strength of a relationship between two non-stationary time series (I'm considering two series of interest rates, series $A$ and ...
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18 views

Forward selection, using adjusted R square or t statistics?

When it comes to select variable in multiple regression model using forward selection, should we add variables in the models according to its adjusted R square or t statistics/Sig?
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1answer
16 views

3 Stage Least Squares or 2 Stage Least Squares

I am planning on running a 3 equation simultaneous equation model where each of the dependent variables depend on each other (i.e. Y1 is based on Y2 and Y3; Y2 is based on Y1 and Y3; Y3 is based on Y1 ...
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13 views

panel data regression [on hold]

I am currently working on something and have to use panel data regression for it but i am not really good with all those and i was wondering: My question is the following, does trade impact the ...
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3answers
235 views

Strange outcomes in binary logistic regression in SPSS

I did a binary logistic regression with SPSS 23 and I found some strange outcomes. This is for NOACprev until No_Prev_treatment, the last 6 variables. First of all they have very high outcomes for B, ...
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7 views

Function in R that can gives optimal value of each variabe to maximise output of a linear equation [on hold]

i have linear regression equation of which i want the optimal values of each variable to maximise my Y. Is there a function(may be optim) in R, that takes linear equation and a dataset, with historic ...
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OLS or Ridge in Multicollinearity data

I am new to stats and linear regression. I just want to understand the exact scenario and usage between Ridge and OLS. Here is the data sample i have been using. In this both Weight and BSA are ...
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11 views

Is this simultaneous equation model identified?

I have a 3 equation simultaneous equation model where all 3 dependent variables depend on each other (i.e. Y1 is based on Y2 and Y3; Y2 is based on Y1 and Y3; Y3 is based on Y1 and Y2). Would all 3 ...
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Interpreting random slope for a dataset with missing data in mixed model

I am struggling to understand the meaning of random effect for the dataset with missing data based on mixed model, I am appreciated if anyone can help. Here is an example. let us say we have 20 ...
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3answers
103 views

What is the difference $\beta_1$ and $\hat{\beta}_1$?

Suppose I have a random sample $\lbrace x_n ,y_n \rbrace_{n=1}^N$. Suppose $$y_n = \beta_0 + \beta_1 x_n + \varepsilon_n$$ and $$\hat{y}_n = \hat{\beta}_0 +\hat{\beta}_1 x_n$$ What is the ...
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1answer
47 views

What does it mean that coefficient is significant for full sample but not significant when split into two subsamples?

I have a sample of acquisitions 1994 to 2015. When I run an linear multiple regression with the cumulative abnormal return after the announcement my coefficient of interest (HFA_dummy) is statistical ...
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logistic regression - changing predictor scale

In a logistic regression with $5$ predictors, one predictor ($x_1$) results in a $B=-1.563$, $OR=.210$. A one point change in this predictor is very large so I multiplied $x_1$ by $10$. The new ...
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Linear regression with trimmed data

I would like to know how experts deal with real data. Even if statistical text books uses real data I'm always surprised how good the real data are and at the end of the exercises the residuals are ...
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2answers
19 views

Boosted Trees classification

I'm using R's gbm() package to do a boosted classification problem, where my response variable is a binary variable taking values of 0 and 1. I have 11 predictors in my data set. After running the ...
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1answer
33 views

Multicollinearity - continuous and dummy variables

I know that one of the assumptions of Gauss-Markov is no perfect multicollinearity. If I want to run a model that estimates the effect of gambling on wages, would this model be appropriate: Wage = ...
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2answers
55 views

Is it correct to compare likelihood ratio indices between logistic regression and multinomial logistic regression models?

In the paper "Including Transfer-Out Behavior in Retention Models: Using the NSLC Enrollment Search Data" (http://www.studentclearinghouse.org/colleges/files/ST_UofMD_casestudy.pdf) the author ...
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29 views

Does practical insignificance mean no relationship?

I have two problems: 1) I have a regression coefficient that is very significant (large dataset), but has low practical significance. Can I say there is no relationship? And I mean really tiny, like ...
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20 views

Adding dummies as control variables change the coefficient

It has been difficult to formulate a title for this question. But here is something that is puzzling me. I have an ordinal probit model with a bunch of covariates. One of the covariates is the number ...
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Comparing the effects of variables that have the same nature

I'll try to illustrate my question through an example based on a study I'm carrying out right now, but I think it can be interpreted more generally. I'm interested in assessing the effect of some ...
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7 views

Dummy coding a column in R with multiple levels [on hold]

I have a dependent variable measuring the net revenue. One of the major predictor affecting this is "product" i.e. the product sold to the customer. My randomly sampled dataset contains 1.4 million ...
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1answer
31 views

Mann-Whitney U suitable for my analysis procedure?

I examine the relation of written language of investors and investment performance of investors. N=52 Dep. variable -> investment success; binary (1/0) (Acquisition/no Acquisition) (N = 28/22) ...
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19 views

Mixed-effects model: basic questions

I am trying to implement a mixed-effects regression model in Matlab to see the correlation between self-reported stress levels and some physiological features. Data comes from a longitudinal study so ...
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10 views

Applying paired t-test to compare averages

So I did an experiment for 1 month where I investigated the change of pH of 3 different types of milk in the refrigerator and room temperature. I d now like to compare the results of each type of milk ...
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9 views

Maximum likelihood and regression [on hold]

Can someone help me understand what we are trying to do when estimating regression parameters with MLE. In the method of maximum likelihood, we pick the parameter values which maximize the ...
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29 views

Statistical optimization Model for which person should be assigned a lead within each state

I am working on creating an optimization model based on sales on a particular website. The system assigns the leads to different sales people. I want to generate a model doesn't randomly assign leads ...
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1answer
15 views

8 variables for 12 months. Sigmaplot

I have measeured 8 variables for 12 months. n = 5-20. So, now I have mean, STDEV, SEM and n for those variables. I have trying to show relationship within those variables and among months. So, I ...