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

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F-test of joint significance vs multiple t-test for regression parameters? [duplicate]

In the context of linear regression, I don't understand why you need to perform an F-test for the H0 that all parameters are zero, instead of just looking at all the t-tests for each parameter. I ...
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13 views

Analyzing a continuous by categorical interaction using R

I want to analyze a significant interaction effect of a continuous variable and a categorical variable which has 15 levels. My equation looks like this: rlm(z~X+Cat1+Cat2+Cat3+XCat2+XCat3) z is ...
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1answer
14 views

What is the regression test equivalent to a repeated measures (factorial) ANOVA?

As in the title, I'm trying to figure out what would be the regression test equivalent to a repeated measures one- and two-way ANOVAs? So, in the case of having different dichotomous IVs and two ...
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11 views

Backward selection (with fastbw) in penalized logistic regression

I have a dataset with more than 20 predictors and a single binary response variable. With only $n=181$ observations, I decided to apply penalized logistic regression to modeling, with all predictors ...
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35 views

use of dummy variables in regression equation

I have data where the regressor of interest is 7-point Likert scale responses to a questionnaire regarding experiences. These people are answering questions regarding a group with which they have ...
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15 views

Methods to deal with latent variables

I had a general question about methods to adjust for the effect of latent variables (specially variables that are suspected to be confounder) in observational studies. In particular, I'm working on a ...
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7 views

gnet solution path plot in spike slab regression

in spike slab regression in R, please someone answer me that how we comment the plot of gnet solution path below? I know that the blue ones represent the zero and red ones for nonzero but what does it ...
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442 views

Can I use linear regression on this model y = a/b *x + 1/b

Can I use linear regression on this model y = a/b *x + 1/b? y = (1+ax) /b since a/b and 1/b are related I suspect I cannot use linear regression (least square) directly. Is it possible to transform ...
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31 views

Exponential Regression to Forecast Future Growth

I need to use Exponential Regression to forecast the future earnings of a company. I have the past 10 years of quarterly data. I can do linear regression but the data is not in a linear fashion. ...
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13 views

Non linear regression using R [on hold]

I am working on a prediction problem for continuous data. I have some data which I want to fit in the equations. It's non-linear in nature. Can anyone suggest me good non-linear regression algorithms ...
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37 views

Is glm(A~B*C*D) the same as glm(A~C*B*D)?

When I run these two in R, I get different values. I thought that I should get the same values since it just includes their interaction terms.
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28 views

Various methods for predicting multiple dependent variables (python)

I would like to model and predict multiple dependent variables depending on one or more independent variables. The most straightforward method appears to be multivariate regression. I was wondering ...
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The effect of scale of predictor variables in regression techniques

In polynomial regression, it is recommended to center predictor input variables to break multi colinear relationships of x to x^2. From Wikipedia: The underlying monomials can be highly correlated ...
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27 views

Largest or smallest confidence interval at $\pi_{i}=0.5$ in logistic regression

A binomial GLM can be written as: $Y_{i}\thicksim B(1,\pi_{i})$ $\mathrm{E}(Y_{i})=\pi_{i}$ and $\mathrm{var}(Y_{i})=\pi_{i}\times(1-\pi_{i})$ ...
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8 views

**Kappa measure in Random Forests** [on hold]

Following is the detailed summary of trained model by Random Forests: ...
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22 views

How to forecast (extrapolate) within a (B-)Spline setting

Suppose I observe a random variable $Y$ for a co-variable $p\in\{70,90,100,...,170\}$. My goal is create a forecast of $\mathbb{E}(Y)$ for $p\in\{50,70,...,350\}$, i.e., a wider range of $p$ as ...
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51 views

Regression analysis low R2 value - Result interpretation

When I run linear regression on my test data I get the following report: You can find the test data in here. The graph of actual vs predicted looks like: I would like to know if this is fairly a ...
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regression for class variables [on hold]

Suppose i have a dataset "IQ" with variables: iq-Intelligence quotient value, Age, Country, quantitative ability,verbal ability, logical ability. we can say that a person with more score in ...
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25 views

logistic regression R and Stata [on hold]

I mostly use Stata for my regression analysis. I want to conduct a logistic regression on a proportion/number of success. Because I receive errors in Stata I did not expect nor understand (if there ...
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7 views

JAGS equivalent to R's I() (Inhibit Interpretation of Objects) function?

I'm wondering if anyone has come across the JAGS/BUGS equivalent to R's I() function. I am interested in using this in a polynomial logistic regression, i.e.: mod1 <- glm(Employment ~ Density + ...
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61 views

Plotting simulated data points from f(X)+e using R [on hold]

Below is a chart from An Introduction to Statistical Learning by Hastie and Tibshirani. The authors use the chart to explain overfitting. In the chart, Y is the response variable and X is the ...
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24 views

What is cost-sensitive regression?

Is there such a thing as cost-sensitive regression? If so, where can I find information about it? For example, the regression assigns different costs/penalties to different features beforehand.
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25 views

Why non-negative regression?

I've seen this as regularization technique: impose that the coefficients are non-negative. When is this a good idea? What's the intuition and logic behind it?
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16 views

Correlation between dependent and independent variables [on hold]

When I did a course on multiple regression in SAS, before proc reg we did proc corr on dependent variable with all the independent variables. I forgot what we are trying to check through it? Is it ...
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25 views

Incremental Gaussian Process Regression

I want to implement an incremental gaussian process regression using a sliding window over the data points which arrives one by one through a stream. Let $d$ denote the dimensionality of the input ...
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20 views

Choosing the correct anova model

How does one choose the best model based on ANOVA's result? I mean I have 3 model outputs 1st is linear+all interaction, 2nd is linear+pair wise interaction and 3rd is linear and I am asked to choose ...
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25 views

Customer life time value prediciton [on hold]

I'm interested in predicting lifetime value for new and existing customers. Which data mining techniques are common for this? I've thought of using Linear regression or a multiple logistic ...
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16 views

Can we perform logistic regression on cross section data?

Can we perform logistic regression on cross section data? My friend says that logistic regression only works for panel data.
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38 views

Linear regression results: How to interpret the plot?

I have a dataset where I am comparing two variables, activity is dependent and days_existed is independent. The correlation between the two variables is 0.41 and I ran an OLS linear regression ...
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25 views

regression with scikit-learn with multiple outputs, svr or gbm possible?

I have been trying regression with scikit-learn with a problem with multiple outputs like this: ...
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13 views

Why would standardized betas be high (e.g. .66) but non-significant in moderated regression?

Running a moderated regression using PROCESS macro in SPSS (issue replicated by running the same moderation using mean-centered variables in SPSS linear regression command box), I am finding that the ...
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8 views

How to rescale “linear predictor” in drawing nomogram with “rms” package in R? [migrated]

I am trying to draw a nomogram from a logistic regression in R by using the rms package, but currently I have a problem: indeed, I can get the nomogram, but the "linear predictor" axis ranges from ...
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1answer
36 views

linear regression: y “percent”, x “year”?

Is "year" as discrete or continuous variable..? is it proper to use linear regression with "year" (every year from 2009 to 2014) on the x axis and "percent" on the y axis..? Simple question from a ...
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1answer
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What are analytical strategies for a system consisting of correlated components?

The data I'm working on consists of several components that are arguably correlated with each other. In particular, the study collects information of health care facilities related to the organizing ...
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2answers
40 views

Implement cross validation for a prediction model

I am trying to assess the predictive performance of two competing linear regression models. $$ model 1: Y \sim X_{1} + X_{2}$$ $$ model 2: Y \sim X_{1} + X_{2} + X_{3}$$ where y is continuous. I ...
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18 views

What is canonical r squared?

I know r-squared is the the percent of variance explained by a model. I am currently reading materials about canonical correlation and found a new concept "canonical r squared". The material does not ...
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Understanding lm() function in R with weights

Consider the following simplified dataset (sales and percent of color-type sales by region): ...
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ridge regression in R

I used ridge regression on a data with multicollinearity ..but I was expecting that the standard error of each predictors would be smaller compared to the ols version.....but from the output inR,, I ...
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Regression - testing for propeties of subsamples

Consider two variables: FERR and MEANREV. Assume that MEANREV takes values from -1 to 0. I consider "fast" MEANREV if it lies between [-1 and -0.9] and "slow" if it lies between [-0.1 and 0]. My ...
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17 views

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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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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124 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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30 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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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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27 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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11 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
62 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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72 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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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: $\frac{n_{22}/n_{.2}}{n_{2.}/n_{..}}$ ...