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

learn more… | top users | synonyms (1)

0
votes
0answers
2 views

Calculating distances between centroids in an Primer 6 MDS plot

I am just wondering if there is a way to calculate the distances between the centroids generated by Primer 6 in a MDS plot. I am trying to find if the centroid distances change beween samples but I ...
0
votes
0answers
3 views

Decision Level Fusion of SVR outputs

I have two sets of features predicting the same outputs. But instead of training everything at once, I would like to train them separately and fuse the decisions. In SVM classification, we can take ...
0
votes
0answers
26 views

$\text{cov}$$(\hat{y_i}, \hat{y_i}^*)$ follow-up

Consider the model: $ {y_i}= {\beta_0} + {\beta_1}x_i + \epsilon_i$, where $E[\epsilon_i]=0$ and $var[\epsilon_i] = \sigma^2$ for $i = 1, 2, ..., n.$ Let $\hat{\beta_0}$ and $\hat{\beta_1}$ be the ...
5
votes
1answer
27 views

Multiple regression in directional / circular statistics?

I'm trying to develop a predictive model for an angular dependent variable (on $[0,2\pi])$ using several independent measurements – also angular variables, on $[0,2\pi]$ – as predictors. Each ...
0
votes
1answer
11 views

PLS Regression and collinearity

From what i know PLS regression is used when there is more variables than observations and when there exist multicollinearity between the independent variables. I have data for a regression model that ...
1
vote
2answers
39 views

How important is it to include a hypothesis for a report?

I am writing a research report for my final university project. For my analysis I have used logistic regression. I have provided research questions which have been answered. So, how important would ...
0
votes
0answers
7 views

F statistic (degrees of freedom) with lagged regressors

Hopefully a very simple question: If we're told we have n observations, but the model we're evaluating is $$ y_t = \alpha + \sum_{i=0}^{p}{\beta_i x_{t-i}} + \epsilon_t $$ And say I'm testing $ ...
2
votes
0answers
26 views
0
votes
0answers
5 views

Lagrange-Multiplier test for Serial Correlation

When testing whether $\epsilon_t$ is serially correlated in: $$y_t = \alpha + \beta_1x_{1t} + \ . . . \ + \beta_kx_{kt} + \epsilon_t $$ Can we simply test: $H_0: p = 0$ in the following: $$y_t = ...
0
votes
0answers
33 views

Is the distribution of my residuals from a multiple regression normal?

I have a dataset with 17 variables (one numerical, 16 categorical). The numerical variable is the execution time of a process. I'm using one of the categorical variables (100 levels) to predict the ...
0
votes
1answer
14 views

Interpreting percentage units regressions

I am using a panel of 2249 schools with data from 2002-2008. Some of the schools are single sex schools whilst others are mixed sex. Some background on my regression: Consider the determinants of ...
2
votes
1answer
32 views

Converting log odds coefficients to probabilities

Suppose we've ran a logistic regression on some data where all predictors are nominal. With dummy coding the coefficients are ratios of log odds to the reference levels. A colleague claims that we can ...
1
vote
0answers
19 views

Which Non-parametric test to use with two Ordinal sets of data

I am trying to find which factors in Ques 6 are strongly related to company being rated high as compared to others in Ques 4 and similarly factors related to company being rated low.
0
votes
0answers
14 views

country-time dummies and specification error

Currently, I am doing research about the impact of free trade agreements to trade flow. I have panel data that consist of 86 countries from 1980-2012. I use panel data estimation : Pooled Least ...
0
votes
1answer
60 views

What is $\text{cov}$$(\hat{y_i}, \hat{y_i}^*)$?

Using the simple linear regression model: $ {y_i}= {\beta_0} + {\beta_1}x_i + \epsilon_i$, where E[$\epsilon_i$]=0 and var[$\epsilon_i] = \sigma^2$... If $ \hat{y_i}= \hat{\beta_0} + ...
1
vote
1answer
32 views

Conceptual issues in AR model representation

Q1 : I am looking for a $k$ lagged (k order) AR model where k >2 conveniently k >20. I am unable to find any large AR model other than the popular second order AR model, $x_t = 0.195x_{t-1} - ...
0
votes
0answers
26 views

What is ${\rm cov}(e_i, \hat y_i)$ in simple linear regression?

The model is $y_i = \beta_0 + \beta_1x_i + \epsilon_i$ What is ${\rm cov}(e_i, \hat y_i)$? What is ${\rm cov}(\epsilon_i, \hat \beta_1)$? What is ${\rm cov}(e_i, \epsilon_i)$? For 1, I am writing ...
0
votes
0answers
10 views

Modelling receipts, volume and price

I am trying to model receipts from a transaction tax, where the the tax rate varies by band according to the price. The basic equation is something like this: receipts = volume * price. I am using ...
1
vote
1answer
26 views

Regression Discontinuity Group Level Program Evaluation

I'd like to use a regression discontinuity design to evaluate a program where the discontinuity/assignment to treatment occurs at the group level. However, I'd like to measure the outcome at the ...
4
votes
2answers
64 views

Robust regression - a better understanding

I looked at robust regression for the first time today and I am a bit confused, comparing it to something like ordinary least squares and I am not sure if I am on the right track. I read a few ...
1
vote
0answers
13 views

Regression of family of marginal density functions

From a large sample of triples $(X, Y, U)$ I need to estimate a function $(x, y, u) \mapsto f(x, y, u)$, such that for each fixed $x, y$, the function $u \mapsto f(x, y, u)$ is a density function; ...
0
votes
0answers
6 views

How to add regression functions in python, or create a new regression function from given coefficients? [migrated]

I have one regression function, g1(x) = 5x - 1 for one data point. I have another regression function, g2(x) = 3x + 4. I want ...
1
vote
1answer
26 views

Variance-covariance matrix of survival model

Suppose I have a survival model like this: ...
0
votes
0answers
34 views

Is the F test for R² in (multiple) Regression one- or two-tailed?

I have been wondering about the F test that is provided by many statistical packages along with the standard regression output. As I understand it, F can be computed by $$ F_{df_{reg},df_{res}} = ...
1
vote
0answers
9 views

Optimum value of minlev in function combine.levels

I have a dataset of 306967 rows and 23 columns, and I am building a regression model for predicting a factor based upon 6 other iid variables. Doing this I encountered an issue of large no of levels ...
1
vote
0answers
29 views

Interpreting coefficients of Regression Model (Mincer Model)

Hi all, I am an undergraduate student who is currently doing an assignment. I am now facing a few problems which are:- 1) Age is usually a positive return to wage, but in my regression output, ...
0
votes
1answer
30 views

Estimating finite sample bias for Instrumental Variables

Are there ways to estimate the finite sample bias with instrumental variables? I guess this would be conditional on assuming some structure to the problem and also would involve simulation, but, at ...
1
vote
3answers
57 views

When to divide data into training & test set in logistic regression?

I am using Logistic Regression in a low event rate situation. Overall universe: 46,000 Events: 420 Conventional logistic regression models divide the data into ...
9
votes
1answer
75 views

Why does non-normally distributed errors compromise the validity of our significance statements?

There is a normality assumption when it comes to consider OLS models and that is that the errors be normally distributed. I have been browsing through Cross Validated and it sounds like Y and X don't ...
2
votes
0answers
33 views

Interpreting regression coefficients of log(y+1) transformed responses

I have measurements $y_1$,...,$y_i$,...,$y_n$ taken from a set of replicates in a factorial designed experiment. In order to use a linear regression I define my response $z_i = log(y_i + 1)$. The ...
1
vote
1answer
16 views

Signs on logistic regression betas flip relative to observed - expected counts from chi-squared test

I conduct a chi-squared analysis on some bins and conclude that an association between the bins and an event exists. I then calculate logistic regression coefficients to validate my hypothesis. Also, ...
0
votes
0answers
9 views

Using non-stationary time series in basic regression models

I know that non-stationary time series data cannot be simply used in OLS regression. But if I take the first difference and the log of the variables, making them more stationary around zero, can I use ...
2
votes
1answer
50 views

Using non-stationary time series data in OLS regression

I am using 1983-2008 annual data to test if both gini coefficients and gross national saving in China and the US can affect the US current account balance. The data seem to be non-stationary, but I am ...
1
vote
1answer
25 views

Naming of dependent and independent variables in simple linear regression

I'm currently doing some self-study on Simple Linear Regression. I came across the terms "dependent variable" (eg. Sales volume) and "independent variable" (eg. Advertising budget). I am curious to ...
0
votes
0answers
16 views

What is the practical array implementation of a regression function on a multidimensional array and reusing it?

I'm going through an algorithm that does regression on some training data. My objective is to practically implement this algorithm. The training dataset $X$ consists of $n$ samples, where n = 10. Each ...
0
votes
0answers
12 views

Differences between groups using proportions

I am doing two analyses. First, I want to compare performance of two groups (p<.05), with the independent variable being proportional data (total number of categorical responses/total number of ...
2
votes
4answers
75 views

How to deal with curvature in residuals plot

I am trying to do a multiple linear regression in R but am having some problems. I have a set up where I am trying to develop a multiple linear regression model for one variable (y) using six other ...
0
votes
1answer
37 views

Power transformation using Box-Cox transformation

I have a dependent variable Cost and an independent variable VPT. I want to perform a power transformation on ...
3
votes
1answer
87 views

How to explain control variables and interaction effects

I'm a bit stuck on running a GLM between 3 continuous variables in R. I can't make them categorical as it removes the significance. I have two questions. I'm analysing data on eggs. I had read that ...
0
votes
0answers
13 views

Empty significance in SPSS linear regression

I got a weird result with linear regression: there are empty significances. I use 'Stepwise' method, so it should drop the inadequate variables. (Of course the "000" Betas are confusing too.) What ...
0
votes
1answer
28 views

Interpreting Labour Economics regression model

Instrumental variables (2SLS) regression Number of obs = 603 F( 5, 597) = 41.96 Prob > F = 0.0000 R-squared = 0.1386 Root MSE = .26523 ...
0
votes
0answers
8 views

Non-Normal residuals after Box-Cox

I applied Box-Cox on a regression model with heteroscedasticity. Although the transformed model looked better than the original, it still showed signs of heteroscedasticity. What else can I do after ...
2
votes
1answer
37 views

What exactly does the 'boxcox' function in R do?

I am familiar with the power transform family and I know how to estimate the MLE for $\lambda$ for given samples of a random variable. I have been using the 'boxcox' function in R for a sample of a ...
0
votes
1answer
42 views

What do the coefficients of the crossproduct of regression mean?

How can I interpret the coefficients of the crossproduct of each of the following codes? What do they mean? How can I deduce that they correspond to our expectation? ...
3
votes
2answers
104 views

Efficiency of beta estimates with heteroscedasticity

I need something clarified and that is when you have non-constant variance, estimates won't be biased but will be a problem when it comes to the S.E. formulas and efficiency. Therefore OLS estimates ...
2
votes
0answers
10 views

How to test for mediation with a continuous mediator and DV, but a categorical predictor?

I want to explore whether attitudes mediate the relationship between employment status and intention to apply to graduate school. (See image below). I planned to use the Sobel Test to explore the ...
1
vote
1answer
27 views

Observations with very high or very low residuals in regression

If a regression model is applied and there exist residuals that is very high or very low (meaning outliers compared to the others), is it good practice to get rid of those observations and then do the ...
1
vote
0answers
31 views

Forecasting after Box-Cox transformation

I did a regression and found that my residuals pointed out that my data is heteroscedastic. I applied the Box-Cox transformation and my new model looks as follows, ...
1
vote
2answers
19 views

Percent error for linear regression model

Suppose I fit a linear regression $y = \beta x + \rm error$. In this situation, $x > 0$, $\alpha > 0$, and therefore $y > 0$. Moreover, the $\rm error$ is normally distributed with mean $0$ ...
0
votes
0answers
15 views

Model matrix of a mixed model

Suppose I have a mixed model like this: ...