Regression that includes two or more non-constant independent variables.

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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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5 views

Use of Excluded Variables Table on Multiple Regression

I'm currently running a multiple regression analysis as part of my first year PhD study, trying to predict Theory of Mind through 2 variables of interest while controlling for socio-demographic data. ...
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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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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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36 views

Is there any conventional small, medium and large effect sizes for ordinal logistic regression?

I was performing a power analysis of articles published in a journal of management using the pwr package in R. However, it seemed to be impossible to compute power ...
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21 views

Multiple Regression - Beginners [on hold]

I'm a beginner in SPSS and statistics, and I'm trying to understand the following. There is an example, which says something about increasing the unit of an independent variable to lead to an ...
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11 views

Correct Feature Selection Methodology?

I am running a weighted multiple linear regression where my independent variables take binary values, 0 and 1. The dependent variable y, takes numeric values (positive as well as negative). The ...
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17 views

Multiple linear regression interactions

When plotting the effects of two factors on the dependent variable, why do parallel lines like the ones illustrated in figure 44 show that there is no interaction between factor (i) and factor (j)? ...
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1answer
24 views

Interpretation of Regression coefficient

I have a five-variable regression equation, and I added any constant (fixed value, say 100) to all the observations of variable $A$, another (or same) fixed value in variable $B$, while the other ...
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11 views

How to Calculate a Pseudo R Squared in R [duplicate]

I am working on my Data and below is my Model: ...
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10 views

dependent variable in shares, independent variable log transformed

I havea question. My dependent variable is in percentage (shares of renewables in total energy supply). Hence, my variable is bounded but values are not closed to the bounds. My professor told me that ...
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1answer
14 views

Modelling a polynomial interaction

Say I have a model with one response variable ($y$, a continuous variable) and two predictor variables ($x_1$ & $x_2$, both continuous). My model includes both the additive effects of these ...
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14 views

Measure dependence/correlation of two variables in a multivariate setting

In a generalized setting, we have a dataset $\{y, x_1, x_2, x_3\}$ where I want to measure if $x_2$ has any correlation with $y$. There are couple of caveats, $x_1, x_2, x_3$ themselves can be ...
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33 views

Getting estimate and CI for dummy variable in linear model

I have a linear model based on some variables (age, gaming and tasks) on response time. It looks like this: ...
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6 views

Multiple variables / continuous outcome / model formula

I have a set of continuous / discrete variables with which I want to model a continuous outcome. How can I know which type of curve would be a good choice and, therefore, which kind of function to ...
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1answer
31 views

Error term in multiple regression model

I am trying to run a multiple regression model to see the effect of field characteristics such as soil texture, slope and hydraulic conductivity on drainage density. My samples are agricultural ...
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0answers
26 views

How to predict when using normalized data?

So, I am taking this course on machine learning by Andrew Ng. Wanted to write my own linear regression program. Everything is fine. I mean normalize data, and run linear regression. Now I'm left with ...
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14 views

Which Statistical Model do I use? [closed]

I want to Study the effects of Commercial Banking on Economic growth in my country. Commercial Banking would be the independent variable and economic growth (GDP) as the dependent variable. I intend ...
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1answer
18 views

Include both linear and non-linear dependency of the same variable in a multi-variate analysis

I am implementing a multi-variate analysis using 5 covariates. My model looks like this: lm1<-lm(Y ~ (T(A) +A + B + C + D + E)^2, data=data) where T(A) is a ...
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14 views

Double Hurdle model with continuous DV and two sources of zeros

I want to regress a data set that contains a lot of zero's (~55%) and is determined by a typical 2-stage decision process generating the zeros: Consumer decides to apply for a bank loan or not (0-1) ...
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29 views

Covariance matrix of multivariate multiple regression coefficients

I would like to perform a regression analysis on a dataset comprising one independent variable (X) and two dependent variables (Y1 and Y2) which may be affected by correlated errors. R's stats::lm ...
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17 views

How do you create an ARIMA model with year wise regressors?

I am trying to understand how discontinuations in products have an effect on sales volume. I have a sales variable and information on product discontinuation sales volume by month and year. For ...
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24 views

How to build a model from data with a proper hypothesis

I have a large dataset of items in a store and how they sell. It looks somewhat like this: ...
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58 views

Is it possible to do a time series analysis with more than one explanatory variable?

I am working on a project, and I am absolutely new to forecasting and not so strong in statistics. I have an employee data for the last 7 years, along with the other variables like economic growth, ...
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23 views

Multivariate linear regression intercept coefficient not significant

I want to do a multiple regression. I have 3 variables, A,B, and C and target Y. I tried to do regression with all of variables I got negative coefficient. From my hypothesis, there is no negative ...
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30 views

How to find standard error of y-intercpet

How do i find the standard error of y-intercept given the coefficient, t-statistic and probability as well as t-statistics of beta1 given the coefficient, standard error and probability?
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14 views

Multiple Linear Regression for repititive values of response variable [closed]

I am trying to create a regression model for Online News Popularity data from UCI machine learning repository. I am using 9 predictor variables to predict number of shares for the news article. The ...
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1answer
32 views

Linear least squares regression with a smoothness penalty vs linear regression with ARIMA errors

I am about to choose between the two options mentioned in the title and I am not really sure what to pick. As a first option, we have classical linear regression plus a smoothness penalty, i.e., ...
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15 views

Using Mahalanobis Distance as Dependent Variable

I would like to know whether Mahalanobis Distance can be used to substitute dependent variable and bring about statistically significant model. Basically, I tried to regress a model using OLS ...
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17 views

multi linear regression model. Modeling with a heavily skewed binary independent variable

Dataset and goal: One continuous measurement( to be modeled as a dependent variable) and four other measurements (one binary and the rest are category variables with multiple levels) to be modeled as ...
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13 views

What do we do with suppressor variables?

I have read about the suppression effect at below. Could anyone kindly help to direct what we do with it? Suppression effect in regression: definition and visual explanation/depiction More details: ...
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21 views

non-trivial coefficients in an OLS regression

I have a problem explaining how and why the coefficient on my regressions is changing signs. I have a continuous outcome variable Y which is a linear combination of two continuous variables y1 and ...
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20 views

Derive a t-test for $\beta_1$ [closed]

I'm having a bit of an issue trying to figure out how to derive a $t$-test for this question. I know I probably need to construct a likelihood function but I'm a bit confused about how to do that. ...
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2answers
36 views

Critical Region for when testing several restrictions using F-test

In the classical linear regression setting, when we want to test $H_0: \beta_r=0$ ($\beta_r$ is a vector of dim. $r$) versus $H_1: \beta \neq 0$, we usually use an F-Statistic. With these two-sided ...
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89 views

Hat matrix and leverages in classical multiple regression

What is Hat matrix and leverages in classical multiple regression? What are their roles? And Why do use them? Please explain them or give satisfactory book/ article references to understand them. ...
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Jackknife residuals formula

I know that the jackknife residuals are $$t_i={y_i-\hat y_{(i)}\over \hat \sigma^2_{(i)}(1+x^t_i(X^t_iX_i)^{-1}x_i)^{1/2}}$$ But there is alsa a formula for computing these residuals: ...
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29 views

How to implement a multiple regression for AR models (time series)?

Let's say I have the following model: So I have an AR model of order 3, and I want to estimate A1, A2, and A3. I understand how regression normally works for two variables x and y. Also, after ...
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1answer
18 views

How to do perform multipe regression of a lagged time series versus 3 other ts?

What steps should I make to perform multipe regression of a lagged time series versus 3 other non-lagged ts? The measurements of these series are taken every 5 milliseconds and I have about 70 000 ...
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2answers
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Why does $R^2$ grow when more predictor variables are added to a model?

I do understand that $ R^2 = \frac{\text{SSR}}{\text{SST}}= 1- \frac{SSE}{SST}$, however, I don't understand what changes when more predictor variables are added and how $R^2$ is affected accordingly. ...
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compare similar multiple linear regression models

I am conducting research on the stock prices (dependent variable) with independent variables (Book Value + Net Income + OCI + Net defined pension benefits) between year 2010 to 2015. My research ...
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19 views

Which model(s) are appropriate for this kind of data

So, I tried to implement a model on some data. The dependent variable is a ratio that can get higher than 1, is lower bounded by zero and, seeing figure 1, is left skewed.Thus, a logit regression is ...
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1answer
47 views

Does the F-test for multivariable regression work with non-normal residuals but large sample size?

Suppose I am building a multiple regression model, perhaps with 5 explanatory variables. Suppose the residuals are not normally distributed (based on a Q-Q plot and a D'Agostino test, due to ...
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32 views

Linear Regression – When is Bad Data “Too Much”?

I am doing a multiple linear regression analysis. One variable, which I think may be quite predictive, has known bad data. I am currently sampling and using analysts to independently verify the ...
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241 views

Regression with skewed data

Trying to calculate visit counts from demographics and service. The data is very skewed. Histograms: qq plots (left is log): ...
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23 views

Posterior Pred. Distribution for Bayesian Hierarchical Regression Model for Existing Group Parameters

For a hierarchical regression model, I understand that there are two posterior predictive distributions potentially of interest: (1): The distribution of future observations $\tilde{y}$ ...
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32 views

How to compare groups when the control group is 100 times larger than the group in question?

I am working with NIS data which are basically a 20% sample of all in-hospital admissions in the US every year. I have small group of cases that carry diagnosis "X" in their record and overall the ...
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1answer
114 views

Analytically linking coefficients from alternative linear models (OLS)

The general problem: I have two alternative models I could use for my estimation Model A: $y = \alpha^A+ X \beta^A_0 + Z\beta^A_1 + \varepsilon^A$ Model B: $y = \alpha^B + X \beta^B_0 + ...
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1answer
28 views

Is it OK to use an original variable & another variable constructed from it in a regression model if there is no multicollinearity?

I'm doing binary logistic regression. I want to predict the chance of being in an advanced class. There no multicollinearity among my variables. I have three predictors: If you passed the test or ...
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10 views

Insignificant alone but significant when inclued in multi variate regression [duplicate]

I have a target variable y, two explanatory variables x1 and x2. x1, x2 and y all have zero mean. x1 and x2 has low correlation. y = b1 * x1 + e gives R^2 = 0.6, p-value of b1 = 0.000, out of sample ...
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1answer
39 views

Multivariable survival analysis: adding another variable lowers the p value?

When I was performing the Cox survival analysis on my data, I tried to look at the predictive value of different variables to survival. For example, here I have two variables: 'size' and 'surface'. ...