Tagged Questions

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

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2
votes
2answers
44 views

How to estimate the individual components from a sum for a random process?

We have $N$ realisations of five individual, IID random variables $X_1$, $X_2$, $X_3$, $X_4$ and $X_5$. We define another random variable $S = X_1+X_2+X_3+X_4+X_5$. Now, for a given $S$ generated from ...
1
vote
1answer
36 views

Multiple Linear Regression Variable Selection

Using all possible subsets we consider the adjusted $R^2$, Akaike's Information Criterion (AIC), corrected AIC ($AIC_c$), and Bayesian Information Criterion. The model with the highest adjusted $R^2), ...
0
votes
1answer
29 views

Solving a regression equation

This is a simple question but I am new regression analysis. If my regression model is of the specification, $\ln(y) = \alpha + \beta_1 X_1^2 + \beta_2 X_2^2 + \epsilon $, and I have estimated ...
0
votes
0answers
11 views

VAR model for price forecasting in multiple time-series context. How to get “real figures” as forecasts?

Sorry for the rather long introduction, but since I was (legitimately) critizised for not explaining my cause and questions enough, I will do so now. I would like to conduct a (price)-forecast based ...
-1
votes
1answer
24 views

What does the Argument “type” in VAR() - function do?

Right now I am working with vector autoregressive models in order to make 3 months forecasts for a commodity good (sawlogs) y. I have several time-series of "follow-up-products" of sawlogs that should ...
0
votes
1answer
36 views

Analysis where dependent variables are proportions

I have a set of demographic data (age, race, social class, etc.) for selected geographic areas. These independent variables are each proportional in each type, i.e. Area A: White 70%, Black 20%, Asian ...
2
votes
2answers
52 views

Why monthly stock returns instead of daily returns in multiple regressions?

This is probably a naïve question. Why do many multiple regression analyses of the Fama-French 3 or 4 factor model of fund returns use monthly return data instead of daily return data? I would have ...
0
votes
0answers
18 views

Can one make a Tukey post-hoc test for multiple regression in R and how?

I saw that the Tukey post-hoc test is usually done after performing an ANOVA. However, is it possible to carry it out in the case of multiple regression with the following model: ...
1
vote
0answers
9 views

Fixed effects in panel data, correlations/coefficients don't add up

I am doing a regression on panel data for firms. The dependent variable is the Marginal revenue product of labour (RPL), i.e. labour productivity, and the independent variable is the average wage of ...
1
vote
2answers
50 views

Multicollinearity in multiple regression

I really hope you can help! I'm in the last stages of my PhD. My supervisor is keen on including all variables in the multiple regressions I am running. Some of the scales are intercorrelated (some ...
0
votes
0answers
6 views

Moderation Analysis with more than two moderators

Can anyone help? I have collected data of over 800 cases (from 5 countries- data is divided into 3 groups). My main research question looks into how do the moderating factors affect the relationship ...
0
votes
0answers
12 views

Jackknife Ridge Regression

I'm Betzy, Mathematics undergraduate student from University of Indonesia. I'm currently doing my undergraduate thesis. My topic is about jackknife ridge regression. In Hinkley's paper (1977), the ...
1
vote
0answers
52 views

testing interaction terms in regression model [duplicate]

Based on domain knowledge and preliminary variable selection, we have decided a set of 10 variables as predictor variables for building regression models. What are the general approaches to identify ...
2
votes
1answer
36 views

Is it valid to use quantile regression with only categorical predictors?

I am new to quantile regression and most of the examples I see are in a multiple regression context with continuous predictors. I am analyzing a designed experiment and was wondering if quantile ...
1
vote
0answers
25 views

Forecast of spot electricity prices

I recently started a job in power trading. But due to a sudden change in employment I am required to work on econometric models to gauge the supply and demand side of national power markets. So ...
1
vote
0answers
33 views

Geometric interpretation of multiple correlation coefficient $R$ and coefficient of determination $R^2$

I am interested in the geometric meaning of the multiple correlation $R$ and coefficient of determination $R^2$ in the regression $y_i = \beta_1 + \beta_2 x_{2,i} + \dots + \beta_k x_{k,i} + ...
0
votes
2answers
45 views

Multiple linear regression, backward selection : Normality of the residuals?

I need to create a Multiple Linear regression model on those data explaining max03 T9 T12 T15 Ne9 Ne12 Ne15 Vx9 Vx12 Vx15 maxO3v !My data 1 My first intuition was to make a backward selection : ...
0
votes
0answers
23 views

Finding the R squared value in Multilinear regression

I currently have 3 different models to run against a specific output. The data is stored in 4 columns: a, b, c, d where a is the actual value that b, c and d are trying to model. Columns b, c, and d ...
4
votes
1answer
129 views

Regression model for road accidents data

I want to model road accidents data to identify 1) the major causes of accidents and 2) predictors that can explain the accident severity measured by the passengers injury level (minor, major, fatal). ...
0
votes
0answers
11 views

Centering my variables decreases the condition indices

In my regression model, one of my condition indices is very high, but only the intercept and one of the eplanatory variables have a large variance decomposition proportion for this index. The VIF is ...
1
vote
0answers
28 views

R-squared adj. in multiple linear regression of 75% = high correlation?

I have a response column and a column of categorical predictors (around 25 categories) and I get with minitab linear regression analysis a R-sqr adjusted of 75%. ...
3
votes
1answer
42 views

Need an Introduction to Generalized Non Linear Multiple Regression

I have been searching the internet for a generalized method for doing regression analysis on non linear data. My model can be represented as $$Y = \beta_0f(X_0) + \beta_1g(X_1) + ... + \beta_nz(X_n) ...
0
votes
1answer
11 views

analysis Methods

I want to analyze following three of my research objectives, • Determine the position of women in the tourism and hospitality workforce as a diverse culture in an organization in four departments ...
1
vote
1answer
34 views

Multiple Regression with Categorical Predictor Variables of More than Two Levels

I'm planning on running a hierarchical multiple regression. In the first step, I would like to enter demographic characteristics, second step continuous predictor variables of interest, and third step ...
2
votes
0answers
31 views

Very low VIF values, but extreme high condition index

In my multiple linear regression model, all of my explanatory variables have a VIF score, lower then 3, but the highest condition index is 709. The constant and one of the explanatory variables have ...
0
votes
0answers
13 views

Small sample size : dealing with bootstraping for linear or nonlinear multiple regression

I am wondering to heal my ignorance from your experiences or your modeling knowledge. I have many matrices of quantitative variables, let me start with three matrices of proportions.To express ...
0
votes
1answer
25 views

How to properly conduct regression analysis with correlated variables

I am trying to assess the impact of a policy change using multiple market variables which are all correlated to some degree. In essence, there are domino effects in response to one policy. I have a ...
1
vote
1answer
37 views

Reporting an ANOVA with a continuous predictor (multiple regression)

I have a design involving 1 between-subjects categorical factor, 1 between-subjects continuous factor and 2 within-subjects categorical factors. This is theoretically a case of multiple regression, ...
1
vote
2answers
90 views

Unusual linear regression results in R

I am doing multiple linear regression analysis in R and I got the following summary: ...
0
votes
1answer
23 views

How to deal with variable new behavior in multiple linear regression

Let's suppose that I have one model with 6 dummy variables for the days of the week and from those in 5 days a specific event happen, suppose that is daytostudy variable and I want to predict how many ...
0
votes
0answers
19 views

questions on glmnet result

I am trying to experiment with glmnet for a data set, which has 41 independent variables is 41. There are 80 data points in total. ...
3
votes
2answers
60 views

After having performed a regression, how can I estimate the probability of a specific outcome?

Let's assume that I have a dataset which gives me the two variables: height ($x_1$), daily calorie intake ($x_2$) and weight ($y$) of a person. In this dataset, we assume I have a large enough number ...
0
votes
0answers
17 views

Testing interaction terms in regression model (and more)

I want to do a regression on the study results of students using a few covariates and 2 factors 2010 and 2011 each with 2 levels A and B. Basically A and B represent 2 different types of classes. If I ...
8
votes
0answers
179 views

Advanced regression modeling examples

I'm looking for an advanced linear regression case study illustrating the steps required to model complex, multiple non-linear relationships using GLM or OLS. It is surprisingly difficult to find ...
0
votes
0answers
9 views

Comparing two different questionnaires but both on a 5-point likert scale

I have three self-reporting questionnaires, all on a 5-point likert scale. Is it possible for me to use a Pearson moment correlation to compare the summation of measures between two questionnaires? If ...
0
votes
0answers
19 views

How Can I Build A Regression Model With Collinear Data?

Hello there my fellow Cross Validated members; I’m here today to brainstorm a little bit with all of you out there, to flesh out our collectively acquired data analytic skills, and to try and find new ...
1
vote
0answers
26 views

$R^2$ (coefficient of determination) and linearity in multiple linear regression

For simple linear regression (SLR), in order for $R^2$ (the coefficient of determination) to be a meaningful measure, it must be true that $X$ and $Y$ are linearly correlated. Specifically, $R^2=r^2$, ...
1
vote
0answers
27 views

How to test heteroskedasticity at the independent variable level?

I know how to test the heteroskedasticity of a model's residuals. I am inquiring about how to test for heteroskedasticity for each specific independent variables included in the model. What is the ...
2
votes
2answers
99 views

How can I get more precise regression tree?

I am a complete newbie to regression trees so maybe I am not understanding it properly. I got the following tree from my analysis (function tree() from R package ...
4
votes
3answers
105 views

What is the distribution of the conditional mean E(Y|X) in a multiple regression?

Suppose the model is $$ Y = b_0 + b_1X_1 + b_2X_2 + b_3D + b_4X_1D + e \\ e \sim\mathcal N(0, \sigma^2) $$ Where $D$ is a categorical variable. $$ E(Y|X_1, X_2, D=1) \sim\mathcal ?? \\ E(Y|X_1, ...
0
votes
1answer
32 views

Why can't we add all the individual Pearson's $r$'s in a multiple regression and calculate $R^2$ based on this sum?

Why can't we add all the individual Pearson's $r$'s in a multiple regression and calculate $R^2$ based on this sum? Is there an easy mathematical explanation to this as $r^2$ is squared and don't add ...
0
votes
1answer
30 views

What is the relation between multiple-regression and pearson's r?

What is the relation between these two, not $r^2$, but Pearson's $r$ and multiple $r$?
4
votes
4answers
129 views

Determine where hazards starts to increase for a continuous variable

I'm interested in a continuous variable, namely blood pressure. The higher the blood pressure, the greater the risk of heart attack and stroke. However, studies frequently report that also low blood ...
0
votes
1answer
9 views

Citation for IV coefficient sign change with inclusion of interaction term

I have a two step regression model where I entered my three IVs in step 1 and one interaction in step 2. One of the IVs in the interaction has a positive coefficient in the first step and then a ...
0
votes
0answers
19 views

Why does Frisch-Waugh produce inconsistent estimates with Weighted Least Squares?

Frisch-Waugh's theorem states that in the setup $Y = X^T \beta $, where $\beta = [\beta_1,\beta_2]^T$, $X = [X_1, X_2]$, $\hat{\beta}_2$ obtained from the multiple regression is the same as that ...
0
votes
0answers
12 views

Assumptions on a multiple linear regression model and elastic net

I am interested in using elastic net regression in place of an multiple linear regression. I know when you perform a multiple linear regression you should check the assumptions such making sure the ...
0
votes
0answers
10 views

Substantial changes in significance level when adding more variables to the model [duplicate]

I have a multiple regression model. When I add one more independent variable to the model the significance level of two of my original independent variables suddenly get insignificant. How come? All ...
1
vote
0answers
19 views

Regression to chose questions which better correlate with a 10 points likert like score

We have a survey with several questions with 5 likert scale points and we would like to compare the answers to those of another likert like question with 10 points. The approach we thought of is a ...
1
vote
0answers
15 views

Multiple Reg with 2 Independent Variables that are Correlated - Orthogonalizing the IV's

I have two Ind. V's, $x_1$ and $x_2$. They are slightly correlated with eachother. $x_1$ explains a significant portion of $y$'s variability. Rather than just modeling $y = \beta_0 +\beta_1 x_1 ...
2
votes
0answers
26 views

Possible to code contrasts comparing each level to grand mean with no reference category?

I'm working on a health care outcome regression model using the deviation contrast scheme described on the UCLA SAS help page here for a collection of dichotomous predictor variables measuring medical ...