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

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Degrees of freedom of t-test in multiple regression [duplicate]

In regression, $ t=\frac{\hat{\beta}-\beta}{se (\hat{\beta})} $ and the degrees of freedom of t-test is (n-k) because we estimate $\hat{\sigma}^2$ from RSS and the RSS has (n-k) degrees of freedom ...
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19 views

Interpreting multiple fixed effects interactions

I have the following model (mcmcglmm in R with data based on this paper). Sex is a two level factor (M or F), Group a three ...
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16 views

Mixed model random effects term is arbitrarily correlated to dependent variable, does it bias model?

I have a mixed model where a random effects term I am thinking about including is totally arbitrarily related (in a non-informative way) to the response variable. I would not include it as a predictor ...
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16 views

How to automate linear model in R [on hold]

I have a data wherein for a specific brand weekly promotional information are there along with its sales. Now while running a linear model we generally look at the desired signs of the variable along ...
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34 views

Regression ceoff. p-value as a 'summary measure' of 'indication of relative importance'

I have a paper in front of me (peer reviewed, etc.), which states "comparison of the P values for [the covariates] gives a good indication of the relative importance of each [covariate] for ...
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1answer
31 views

Is imputation needed for $0$'s in regression?

I am working on a dataset of 2000 records using SAS Enterprise Miner in order to predict insurance payment (compensation) from insurer, a motor insurance company, to its customers. Though there are no ...
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1answer
26 views

Heteroskedasticity Question

I have a model that's affected by Heteroskedasticity: bptest(m1) studentized Breusch-Pagan test data: m1 BP = 65.055, ...
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28 views

Logistic Regression: Should I include a non-significant variable that notably increases the OR of a significant variable?

I am studying the effect of different pollutants on the probability of a genetic mutation. My binary logistic regression models are as follows: Model 1: Dependent variable: genetic mutation (binary ...
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8 views

Type of regression to use in analysing Likert scale

Please i want to know if its correct to use multiple regression in analysing Likert Scale (dependent variable) against Likert items (Independent variables). I used 5 point likert scale for all. I want ...
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7 views

Partial R2 for regression containing both linear and quadratic terms

I’m trying to get an idea of whether age or rate of aggression in my study species explains more of the variance in cortisol level across individuals. I already know that the relationship between age ...
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2answers
35 views

How to summarize R-squared of several regressions, one per subject

I will explain my question, I have made a study and I have 10 regression (one for each subject). I have a significance for each regression, but in some subjects the value of R-squared is 0.5 and in ...
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17 views

How to write a regression equation with an unknown structural break point? [on hold]

Using STATA's estat sbsingle function post a multiple regression I have found a statistically significant structural break. However I am struggling to express this in a mathematical/statistical ...
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4 views

Finding right lags for several independent variables in linear regression

I have gone through several other Stats.stackexchange posts such as these experiment lags, Cross-correlation function suggestion,VAR model approach and lagged dependent variable approach. One of the ...
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1answer
51 views

Predictive model developement for logistic regression?

In the statistical courses I've taken, which are mostly introductory, when I have a model I would make hypotesis tests to reduce it to the simplest form and am effectively done. It is my understanding ...
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7 views

how to separate the effect of two confounding variables

Now I have difficulty to choose between two modelling approach. Here is th story. To examine the clutch size laid by a parasitic wasp into caterpillar hosts which varies in age and size, we exposed ...
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17 views

Predicted values in random intercepts model with more than one explanatory variable [closed]

I want to obtain predicted values from random intercepts model by setting some predictors constant. I have a data set of 58 countries. I ran a RI model after controlling for demographics (...
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1answer
68 views

Meta-regression with highly-correlated predictors? Should I do 2 analyses? Example in R

I'm trying to do meta-regression with a lot of trials (>40 trials with >100 'arms') investigating the efficacy of a procedure (abl) and any 'addon' procedure. Each trial will have 2 or more arms. In ...
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1answer
23 views

How to analyze difference in dependent variables

In a simple scenario I'm analyzing impressions and click thru rate (CTR) of banner advertisements. Multiplying these together equals the number of clicks. Say I have the following example dataset ...
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13 views

How do you appropriately adjust some variables in a regression that are normalized to a particular covariate?

If a variable is normally expressed relative to body weight and then I want to include that variable in a regression model that also includes body weight among other variables as covariates, do I have ...
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11 views

Linear regression summary in R: Standard vs car.Anova

I am running some linear regressions in R. I am dealing with a linear dependent and linear as well as categorical independent variables using lm. So far, I have looked at the output that ...
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7 views

subsample approach versus dummy variables, Fama MacBeth (1973) procedure

I am running an asset pricing test (Fama MacBeth); regressing six month ahead excess stock returns on past six month return (momentum) and a number of control variables (B/M, Size etc). I have run my ...
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11 views

Income Elasticity of Demand [migrated]

My model has been given as: $$\ln (H_ed) = 18.08 - 0.53\ln Pop -2.01 \ln Distance + 0.47\ln RGD $$ Where (Hed) is higher education, pop is population, and RGD = Real GDP per capita. I have been ...
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21 views

Inferencing from Pearson correlation and multiple linear regression

My hypothesis is: Company size (CSIZE) is positively correlated with Return on Assets (ROA). I create a Pearson correlation matrix in Stata using pwcorr csize roa ...
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40 views

100-Dollar Game Responses as Predictor

Outcome = Feature Sentiment Participants will rate on 1-5 point scale how much like a particular feature. Potential Predictors = Developments A - E Participants will be asked to imagine they have ...
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26 views

In R : how to perform a multiple regression? [closed]

im really stuck on this. I'm working on a research paper that I have to do for school (studying psychology btw). hypothesis i want to test is : does social support (moderator variable, measured with ...
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12 views

Dummies that take only one non-zero value

According to this blog post in the section «2. Dummies That Take Only One Non-Zero Value», the author states that when the dummy has only 1 observation where it's different than one, then we can drop ...
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1answer
29 views

Degrees of freedom in multiple regression thought of as a path analysis using standardized variables

I keep reading that multiple regression is "just-identified" (df = 0) when viewed as a path analysis. If I'm using unstandardized variables, I believe this. For example, with five variables—4 ...
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12 views

endogenous and exogenous variable interpretation

I am running a regression of the form $$\log\left(Y\right)=x_0 + x_1\beta_1+\log (x_2)\beta_2 +x_3\beta_3+ \epsilon$$ where all the covariates $x_1$,$x_2$,$x_3$ are endogenous. I have an instrument ...
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2answers
56 views

Regression Interpretation conundrum

I am running an OLS regression of the form $$\log\left(Y\right)=x_0 + \left(\frac{x_1}{Y}\right)\beta_1+\log (x_2)\beta_2 + \epsilon$$ I have one covariate as $\left(\frac{x_1}{Y}\right)$ which is a ...
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1answer
24 views

How to get the best out of a “bad” set of features for regression?

I'm trying to learn a regression model for a computer vision / pattern recognition task, where I try to estimate a continuous variable from a set of visual features. I have done preliminary ...
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9 views

How to interpret linear regression with different categorical variables

I am looking at the effect of 1 categorical dependent variable A (3 outcomes) on 1 independent variable. This is significant. Then I want to see if this effect is still true if I add another ...
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13 views

recommender system for hotel prices and promotions

We are trying to build a web page that will list the rooms (and promotional packages) of a hotel, along with automatically produced prices/rates. Top 2 in the list has special importance. These two ...
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1answer
58 views

> 1 interaction variable, single regression versus multiple regressions

In my study I have an independent continuous variable x1 (momentum) and four dummy variables D1 D2 D3 D4 which indicate industry type. I am investigating the four interaction variables between the ...
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1answer
11 views

how to disentangle the influence of two correlated dummy variables

I am analyzing the effect of two factors on performance in an easily measured test. The two independent variables are "category" variables, let's call them "strategy" and "manager". Each test result ...
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20 views

What technique do I use to predict number of calls based on credit score?

I'm new to stats, but have been given this project: There is a call center which calls up leads and tries to get them to buy one of our products. (These are people who came to our website and filled ...
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13 views

How to interpret and constrain the 'bias' from an OLS multiple regression?

I'm trying to solve a linear system with OLS and understand how the output coefficients deviate from the input values of mock data. The basic ideas are as follows. For the linear system ...
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1answer
31 views

Why is the coefficient in this Washington Post fixed effects regression output considered significant?

I'm trying to understand the multiple regression fixed effects model the Washington Post used for a story. See the outputs here: What is confusing me is the first predictor: ...
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3answers
68 views

Forecast time series data with external variables

Currently I'm working on a project to do forecasting of a time series data (monthly data). I am using R to do the forecasting. I have 1 dependent variable (y) and 3 independent variables (x1, x2, ...
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31 views

Is controlling for cluster good enough in an analysis with a clustered sample?

I'm trying to understand how sampling design affects analyses and I'm a little confused about how to adjust for clustering. From what I've read, when you have a clustered sample you are supposed to ...
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24 views

Different sample sizes, skewed data and unequal variances

I am a bit confused regarding best statistical test to use for my data. Here is the details: I did a study to compare two independent groups; 31 patients and 60 healthy controls. I have around 50 ...
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16 views

What to do with highly unequal sample sizes in a dummy linear regression? (+ nonnormal distribution)

I am conducting a multiple linear regression analysis in SPSS. My DV is continuous (score between 0 and 6), and my predictors are: one dichotomous nominal variable (native vs. non-native speakers) ...
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21 views

Comparing a regression model with a single unit to the original model with all units

Background information: I have a regression model consisting of 230 companies (entity) for 20 years The model has 9 X-variables, and the P-value < 1% for the whole model The ...
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25 views

Evaluation of Logistic Regression

I have ran these two Logistic Regression models (below) on some small data and I am able to interpret the output - significance and direction - of the regressors, but I do not know for sure how to ...
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30 views

Does increasing sample size have any effect on omitted variable bias?

Say I have a multiple linear regression model, where two of the variables are positively correlated, and I omit one of these variables from the model. First question - if I increase the sample size, ...
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1answer
35 views

$Y=\epsilon$ in GLM?

In general linear model $$Y=X\beta +\epsilon $$ the LSE for $\beta$ is $$\hat \beta=(X^TX)^{-1}X^TY$$ and so $$\hat Y=X\hat \beta=X(X^TX)^{-1}X^TY=HY$$ where $H=X(X^TX)^{-1}X^T$. Then the ...
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1answer
46 views

Can I apply OLS (multiple regression) to panel data to identify significant variables?

I have panel data for a 5-year period and want to explore the determinants of car prices (number of doors, house power, etc.). Is it appropriate to use OLS or multiple regression to explore the ...
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2answers
36 views

Help interpreting interaction terms in proportional cumulative logistic regression- ordinal regression

I am using the polr() function in R to analyze the relationship between a students score on their first exam, their score in their prerequisite course, and their beginning of semester GPA on their ...
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1answer
29 views

Regression technque to use for continuous data behaving like ordinal

I am trying to create a model to explain/predict fulfillment ratio of a product by a store i.e orders placed divided by orders delivered.The QQ-plot of the fulfillment ratio is: The QQ-plot of the ...
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1answer
31 views

Interpret regression coefficients when independent variable is a ratio

I am running an OLS regression of the form $$\log\left(Y\right)=x_0 + \log\left(x_1\right)\beta_1+x_2\beta_2 + \epsilon$$ where the dependent variable Y and some independent variables are log ...
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7 views

Assumption of normal distribution in MANOVA/MANCOVA having determined various var lag structures

I am currently running a multivariate regression (MANCOVA) using macroeconomic factors and exchange rates. To fulfill the assumption of uni-/multivariate normality, I transformed all variables ...