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

learn more… | top users | synonyms (1)

2
votes
1answer
45 views
0
votes
1answer
43 views

About stepwise regression and correlation

I am trying to fit a model to some observed data with the least squares method. Now, I am at the stage where I have run a stepwise regression (traditional), with Entry level $=0.025$ and Stay level ...
2
votes
1answer
127 views

How to implement model in R?

i would like your help to implement this model in R or more explicity where yt = monthly mean values μi = mean value in month i, i = 1 . . . 12 . I1;t = Indicator series for month i of the ...
1
vote
0answers
31 views

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

Assume that there are $N$ realisations of five individual, random variables$X_1$, $X_2$, $X_3$, $X_4$ and $X_5$, which in general could be correlated. We define another random variable ...
0
votes
1answer
46 views

How to interpret redundancy?

I have trouble making sense (i.e. real-world sense…) out of some of my results. I have Y and X1 and X2 for different geographic areas. Meaning they are the same variables, but their actual values are ...
1
vote
0answers
26 views

Multiple, multiple regressions?

I have done a study on whether personality and demographics predict interaction on Facebook brand pages. I have used a Big five personality scale and the demographics include, sex, age, marital ...
0
votes
3answers
46 views

Testing and reporting interactions in multiple regression

I have a model with two between-participants predictors -- one continuous (a), and one categorical with two levels (b) -- and ...
2
votes
1answer
73 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
48 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), ...
1
vote
2answers
90 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
13 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
29 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 ...
1
vote
1answer
41 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
61 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
21 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
13 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
54 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
7 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
13 views

Jackknife Ridge Regression [on hold]

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
56 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 ...
3
votes
1answer
40 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
27 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
40 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 : ...
2
votes
1answer
29 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
135 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). ...
1
vote
0answers
13 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
31 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
44 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
50 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
33 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
40 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
93 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
23 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
61 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
18 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
185 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
12 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
28 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
103 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
32 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
132 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 ...