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

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Simple ways to forecast US GDP

Forecasting US GDP sure is hard, even the Fed's FRB/US gets it wrong. I am an undergrad doing a US GDP forecasting project, and was wondering if there were simpler ways to do so and produce decent ...
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4 views

Leave-one-out cross validation with bayesian networks - R

I have a dataset with 1000 rows and 10 columns and s/n values. The head of the data : ...
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2answers
83 views

Error term in linear regression

I'm reading about a linear model which is fit to an equation, $Y = \beta_0 + \beta_1X + \varepsilon$, where $B_0$ is the intercept, $B_1$ the slope, and $\varepsilon$ the error term. My question is, ...
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18 views

How to interpret the coefficient of tax rate

I have used the marginal tax rate as an independent variable in my regression. The data is in decimals, meaning it is lesser than 1. How do I interpret its coefficient if the dependent variable is no. ...
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7 views

Comparing logistic regression models with different predictors [duplicate]

What measure do I use to compare two logistic regression models with different predictors but the same response? y ~ x y ~ z I've used lrtest and anova before ...
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19 views

Finding the optimal combination of independent variables for a constrained dependent variable

I'm currently working on power plant time series data and my main objective is finding out the optimal combination of independent variables which would keep "SO2 concentration (dependent variable) ...
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6 views

Interpretation of non-significant parameters in significant Cox-model as prognostic factors

I want to analyze predictive factors for patient survival after surgery. I have variables that are based on investigations at the time of the surgery, known predictive factors (age, KPS) and data on ...
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1answer
40 views

Comparing models in linear mixed effects regression in R

I have a very large data set with repeated measurements of same blood value (co) (1 to 7 measurements per patient). Each measurement is coupled with time which is the time interval between surgical ...
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62 views

Comparison between regression of $a = bc^t$ and $\log a = \log b +t \log c$

This question is more qualitative then about the maths behind the equation. Variables: a = month (1, 2, 3, ) t = shipments of a product in that month You wish to derive the relationship between $a$ ...
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8 views

Interpreting the lift curve

Suppose we have two classes: A and B. Suppose we use a logistic regression to assign each unit to A or B. The curve lift is calculated through this formula: $\frac{n_{22}/n_{.2}}{n_{2.}/n_{..}}$ ...
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Contributions of variables in Log-Log models

I have built a model on log(units) sold and want to measure the contribution of each independent variable in the model (which are also in ...
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14 views

What regression analysis to use? IVs with two levels and a DV with two conditions?

I'm trying to figure out what the best regression test to use for my data. I have three predictor IVs each with two levels. I also have a DV values belonging to two different conditions (A & B). ...
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1answer
15 views

Statistical Modeling with the combination of two models

I'm having a modeling problem now. Assume we have discrete random variable Y and continuous random variables X and Z. First, we assume a logistic regression between Y and Z.(Assumption One) Also, we ...
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If the null hypothesis is true, how will the test statistic be distributed?

I went with T~(50-6) The question goes.... "A regression is estimated with 50 observations, five explanatory variables and with a constant. Suppose You want to test the following hypothesis $H0: ...
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1answer
35 views

If $ln(y) = 5 - 0.1X$ what is the elasticity of Y with respect to X, when X=10?

So i got the following model $\log(y) = 5 - 0.1* X$ ...The question is "The elasticity of $Y$ with respect to $X$, when X=10 is..... " i said -0.1 but apparently i'm wrong Isn't the coefficient ...
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10 views

Logistic Regression Question (Adjustments)

I had a question revolving around logistic regression. I'm looking at a data-set for my work that yields somebody as approved or denied (think credit rating applying for a mortgage, similar but not ...
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21 views

Question on Tobit Regression

Does Tobit regression assume that the dependent variable is continuous above the lower bound? I am trying to model mortality (ie. "dead" or "living" but not the time) based on a set of independent ...
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28 views

Linear regression - Simulation - “what if” scenario

I have an assignment at university and I have been given a simple situation which I would like to explain here. Situation: I would like to perform simulation on purchase price of a product and see ...
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18 views

How to fit intensity peaks from a image?

I have a image - that I can convert to a txt/table with all intensities. In this image, several regions show higher intensity, i.e. a peak. A peak may be stored in a 10x10 matrix with rows and colums ...
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7 views

Comparing models using different sample sizes from the same longitudinal cohort

Epidemiological study: All of the 5 models I am comparing are derived by data from same longitudinal cohort. Each model contains the same IV, DV, and covariates. The difference between each model ...
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1answer
27 views

Question about training set and test set

Suppose I need to compare 3 different regression models. Suppose that my only purpose is to select the model which predicts the response variable better. Be M1, M2, M3 these three models. So I ...
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2answers
276 views

Question about the true nature of errors

In frequentist statistics, in regression analysis, errors, like random variables, have a distribution. Errors, like parameters, can be estimated and the residuals of the model are their estimates. So ...
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46 views

Comparing datasets from 2 biological replicates

I have two datasets containing experiment data based on two biological replicates. I wonder what the best statistical methods are to find out and test how similar these two datasets are, and also how ...
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59 views

Other unbiased estimators than the BLUE (OLS solution) for linear models

For a linear model the OLS solution provides the best linear unbiased estimator for the parameters. Of course we can trade in a bias for lower variance, e.g. ridge regression. But my question is ...
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70 views

linear regression - modelling explanatory variables which depend on each other

I'm trying to estimate the value of an apartment, by doing a regression through similar apartments. The regression model looks now like this ...
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12 views

Robust scoring to rank data

I am trying to develop a score for a dataset to rank (both numeric and categorical features, although I could just use numeric variables only as they are the more consequential ones) and would like to ...
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6 views

Hedge ratio formula

Help to clarify this fact. In the simplest case, in order to find the hedge ratio using a linear regression of the form: $S_t = \alpha + hF_t + \epsilon_t$, where $h$ is the hedge ratio or the slope ...
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9 views

Is a skill score based on mean squared error appropriate for evaluating regression models based on maximum likelihood estimation?

I would like to evaluate a generalized linear model assuming a gamma distribution of the target variable and with one to several predictors by means of cross-validation. With the observational series ...
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1answer
42 views

Understanding the Durbin Watson test

The test statistic for the Durbin Watson test can range from 0-4 from what I have gathered. Now the lower limit of 0 makes sense considering the test statistic consists of two summations which are ...
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30 views

replication of findings in regression models

Let's say we have a dataset for which we constructed a multiple linear regression model and obtained a particular set of $\beta$ coefficients and their significance values. Now, we want to replicate ...
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54 views

Understanding Gauss Markov theorem

According to the Gauss Markov theorem, in a linear regression model, if the errors have expectation zero and are uncorrelated and have equal variances, the best linear unbiased estimator of the ...
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14 views

Quantitative or qualitative variable

Suppose I have a response variable $y$ and $p$ explanatory variables $x_1, x_2, \dots,x_p$. Suppose one explanatory variable is the month of the observation. Suppose that the variable "month" is ...
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12 views

Comparing fixed effects regression and robust regression

I have panel data on countries' renewable energy net generation (and installed capacity) over time. I am regressing these dependent variables on various socioeconomic variables, as well as binary ...
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32 views

Uncertainty of sum of values estimated through linear regression

I have a continuous record of a variable X, which I want to use as a surrogate for another system value Y. I have a number of measurements of Y, which I can plot against concurrent measurements of X ...
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27 views

I'm in trouble while I'm estimating regression in R [on hold]

I try to estimate robust regression with R. But I am in trouble. I determine all variables. And I estimated regression with OLS (ordinary least square. I can do it. while I doing for robust, I ...
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37 views

How to interpret regression coefficients when the dependent variable is square root transformed? [duplicate]

I have problem with interpreting the OLS regression result with the dependent variable square root transformed when doing difference-in-differences analysis. Our regression model is: $$ Y = β_0 + β_1 ...
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13 views

Aggregated probability using irregularly-spaced time series data

I have dataset describing a group of animals' size and growth over 2 years. These particular animals grow in non-continuous growth steps and I wish to model the frequency of these growth steps during ...
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2answers
29 views

Evaluating a factor variable

I am seeking feedback on the theoretical appropriateness of two approaches I am planning to follow. I have a dependent continuous variable (y) and several independent variables some of which are ...
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Multiplying coefficient to get yhat

I am trying to build a model using this equation equation: yhat = b0*b1(x1)*b2(x2)*b3(x3) Most of the data are dummy variables and I can't used interaction ...
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1answer
27 views

GridSearchCV Regression vs Linear Regression vs Stats.model OLS

I am trying to build multiple linear regression model with 3 different method and I am getting different results for each one. I think that I have to get the same results but ...
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1answer
19 views

Groupwise contribution to regression coefficient?

My intution tells me that the following is a straight forward question, but I could not find relevant answers when I searched for it. I assume the reason for that is that I don't know the relevant ...
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1answer
21 views

Bonferroni Correction for Post Hoc Analysis in ANOVA & Regression

As we know, if we are doing many tests or multiple comparison, we don't use the same $\alpha$ value and use some $\alpha$ correction methods like Bonferroni. This is done because when we do multiple ...
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25 views
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Standardization for regularized, sparse hashed logistic regression

As the question states, I'm fitting large, sparse logistic regressions (with hashed interactions, a la vowpal wabbit) for a machine learning system. The features are on different scales, and I'm a ...
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1answer
27 views

Which tests to run for this interesting study about discrimination?

N = ca. 41000 Dependent variable: subjective wellbeing (values from 1 to 10). Independent variables: job autonomy; income; social trust; political trust; Moderator: ethnicity (dummmy, 1=yes; 0=no) ...
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1answer
29 views

How can we have non-random patterns in the plot of simple linear regression residuals vs the predictor variable?

A) When considering a simple linear regression model, it is important to check the linearity assumption. Graphing the residuals vs the predictor variable can often give a good idea of whether or not ...
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“Multi-Task” Logistic regression with time series data [on hold]

I'm trying to create model for consumer loan defaults that incorporates individuals payment behavior as time series. Typically this kind of problem is modeled using Cox/Allen model. Then, the other ...
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17 views

Interpretation of Bootstrapping's Result

i am currently doing my final thesis and in need for help. I am using Bootstrapping to identify the indirect relationship of celebrity endorsement (CE) to purchase intention (PI) with brand image (BI) ...
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1answer
50 views

Regression Analysis:

linear regression model is fitting the data properly as in shape of the curve of predicted value is same as curve of actual value but the predicted values are lower than the actual values. Imagine the ...
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18 views

Normalize time series data - Wikipedia article counts

I have: 3 wikipedia article access counts (weekly) (A-B-C) Ground truth data (weekly) Total wikipedia english article traffic counts (weekly) My purpose is, build a multiple linear regression ...
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1answer
25 views

Quadratic term has positive coefficient in simple regression, and negative in multiple regression?

Is it normal, that quadratic term has positive coefficient in simple regression, and negative in multiple regression? Linear term of the same variable is positive in both cases...