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

learn more… | top users | synonyms (1)

0
votes
0answers
7 views

Dummy Variable Coding in Multiplicative Model in R

I saw this question online: http://www.actuarialoutpost.com/actuarial_discussion_forum/showthread.php?t=24196 "I am fitting a multiplicative model for insurance using R Cost = [X ^ B1] * [Y ^ B2] * ...
0
votes
0answers
3 views

sklearn.linear_model.RandomizedLogisticRegression : Handle Categorical Value [migrated]

I want to use RandomizedLogisticRegression for selecting variable for my data set. But the problem is that, One of the feature in my data set is Gender. So it's ...
0
votes
0answers
9 views

Coefficient of determination in the presence of a certain measurement error

In page 138 of Green's Econometric Analysis, we consider a simplified type of measurement error that allows the usual OLS estimator to be consistent. In the picture below that model is described. ...
2
votes
1answer
22 views

Percentage of contribution of multiple factors to a single dependent variable

I have a set of data (underway observation along cruise track) which includes one dependent variable A and three independent variables B, C, and D. It's known that A is related with each of the three ...
-1
votes
0answers
25 views

How to do a stepwise regression in R using Mallows' criterion Cp? [on hold]

I have about 20 variables (interactions included) in my multiple regression analysis. I would like to do a stepwise regression(backward, forward, stepwise) in R using the Mallows criterion. I struggle ...
0
votes
1answer
14 views

MLP with 2 outputs vs 2 MLPs with single outputs for nonlinear regression

Assume i want to apply nonlinear regression to two output variables with multilayer perceptrons. Is there difference between using a MLP for each regression with single output and using a single MLP ...
0
votes
0answers
22 views

Multicollinearity and two variable with the same level of significance

I have a high value of correlation between 2 of my explanatory variables (0.79), and they are both significant at the same level exactly. Besides that they are both important to the model. The ...
1
vote
2answers
147 views

Consequence of Multicollinearity

In case of perfect multicollinearity the predictor matrix is singular and therefore cannot be inverted . Under these circumstances, the ordinary least-squares estimator $\hat\beta=(\Bbb X'\Bbb ...
1
vote
1answer
34 views

Transformation of regression model to estimate sum of coefficients

How can the model $y=\beta_0+\beta_1x_1+\beta_2x_2+e$ be transformed so that it estimates the sum of $\beta_1$ and $\beta_2$, that is, $\beta_1+\beta_2$ is a coefficient in the new model?
2
votes
0answers
39 views

drawing confidence interval graphs [migrated]

I've made regression model with 4 variables. And I have gotten the following regression equation $$ Y= 0.0761 - 0687X_1 - 3.46X_2 - 1.937 X_3$$ I calculated Confidence intervals for these four beta ...
1
vote
1answer
32 views

Closed form solution for t-stats and p-values in multiple regression

I am trying to build a spreadsheet that will perform multiple linear regressions on a number of data series using the closed-form solution. It was fairly straightforward to write the solution for the ...
0
votes
0answers
15 views
0
votes
0answers
41 views

Is it ok to have a unit root within an independent variable?

The Dickey-Fuller and ADF tests testing for a unit root in variables are very sensitive. Some econometricians have personally indicated to me that in some cases it may be acceptable to model a ...
2
votes
1answer
21 views

VIFs and condition indexes give different answers about multicollinearity

For a multiple regression model, all the variables have p-values below 0.05. The p value for the whole model is below 0.05 as well. When I checked for multicollinearity, I got VIFs below 5 for all the ...
1
vote
0answers
22 views

When adjusting for X1, have we adjusted for X2, to the extent that X2 is related to X1?

I've just read Elizabeth Stuart's paper on matching methods (http://biostat.jhsph.edu/~estuart/Stuart10.StatSci.pdf), which I find very informative. She discusses propensity score methods and the ...
-1
votes
0answers
9 views

I have data independent variable scoring 1 to 4 and a dependent variable number of people dependent variable,,,,,Which regression or model i prefer [closed]

Access to water , Road , Sanitation, parks etc 1=25% have access 2=50% have access 3=75% have access 4= >75% have access Dependent Variable Number of people in a particular area... Which model or ...
1
vote
0answers
41 views

Removing additive effect in Multiple Regression in R

I have this data set that I will used for my model ...
1
vote
1answer
36 views

Regression coefficient as weights

I am not good enough in Statistics. I have a data in which there are four variables and one response variable. Is there any way so I can use my regression coefficients as weights? I have one ...
0
votes
0answers
15 views

Separating the Intercept in many Dummy Variables in Multiple Regression in R

I did a multiple regression on a dummy variable using R about how much people will pay on a certain product. Given this variables and levels: ...
0
votes
0answers
23 views

Regression Model Data - Changing exponential data into linear data

I have some 20 year monthly economic data that for the first couple of years is growing at a linear rate then grows at a slight exponential rate then in the last few years takes on a linear shape ...
1
vote
1answer
34 views

Interpretation of interaction between a PC and a continuous predictor on a logical response

I'm using lme4 in R to test the effect of various continuous explanatory variables, some of which I've corrected for their collinearity using PCA, on a logical response variable. My optimal model ...
0
votes
2answers
34 views

Doing multiple regression without intercept in R (without changing data dimensions)

I am trying to calculate multiple regression in R without intercept. My data is as follow: ...
1
vote
2answers
39 views

Independent variables in multiple linear regression

I have a set of experimental parameters and my task it to find reasonable descriptors to describe them (chemistry). Since I've got descriptors, I checked Pearson correlation for each of experimental ...
2
votes
1answer
151 views

When to use Log in Regression?

I saw this sentence: "I use log(income) partly because of skewness in this variable but also because income is better considered on a multiplicative rather than additive scale. In other words, ...
3
votes
1answer
25 views

Coefficient Decreases but Standard Errors stay the Same with Inclusion of Control Variables

I estimate 2 models in OLS. $Y=\beta X+e$ and $Y=\beta X+\gamma W +u$ The inclusion of the $W$ variable decreases the size of $\beta$ but does not change the $Var(\beta)$. $X$ and $W$ are not very ...
3
votes
1answer
45 views

Multiple Imputation Using Amelia [duplicate]

I am using Amelia for multiple imputation, and I am satisfied with the imputed results. But I want to restrict the imputed variable to positive values. Is there a way that Amelia can handle it or ...
0
votes
0answers
33 views

Using multiple regression to predict x values

I understand the basics of running regression. I have used it in the past to create predictor values for engineering problems. For instance, how much cooling does a system need if previous systems ...
2
votes
0answers
22 views

Reverse-engineering a (custom goods) pricing algorithm - each db row has: | 3 factors | 2 co-variates | price | (I have over 100k rows of data) [closed]

just wanted to mention up front that my question doesn't concern dynamic pricing, price optimization, revenue management, etc. No time series analysis either. It's just a simple multivariable ...
4
votes
1answer
99 views

How to correctly implement iteratively reweighted least squares algorithm for multiple logistic regression?

I'm confused about the iteratively reweighted least squares algorithm used to solve for logistic regression coefficients as described on page 121 of The Elements of Statistical Learning, 2nd Edition ...
0
votes
0answers
32 views

bonferroni and scheffé' simultaneous confidence interval graph in minitab

I need to calculate bonferroni and scheffé' simultaneous confidence interval by hand as a homework. However, I also want to add minitab outputs and graphs to my homework task. How can I plot these ...
2
votes
1answer
53 views

Can $x'x$ be written as correlation matrix?

$x'x=$ $$ \begin{bmatrix} \sum_{i=1}^{n}(X_{1i}-\bar X_1)^2&\sum_{i=1}^{n}(X_{1i}-\bar X_1)(X_{2i}-\bar X_1)\cdots & \sum_{i=1}^{n}(X_{1i}-\bar X_1)(X_{ki}-\bar X_k) \\ ...
4
votes
2answers
135 views

How is the first column of the matrix orthogonal to all the others

$$ \mathbf{X}_{n\times(r+1)} = \begin{bmatrix} 1 & (x_{11}-\bar x_1) &\cdots & (x_{1r}-\bar x_r) \\ 1 &(x_{21}-\bar x_1) &\cdots & (x_{2r}-\bar x_r) \\ ...
1
vote
1answer
23 views

Can two separate regression coefficients be added to estimate their mutual effect?

Let's say I perform a Cox regression including 3 predictors that relate to the survival: Hazard ratios (HR) for predictors Sex: Hazard ratio for males = HR 1.5 Treatment: Hazard ratio for being ...
0
votes
0answers
16 views

Multiple regression in SPSS

I am trying to figure our which statistical analysis is best for my research. I have 6 cont. IVs and 1 cont. DV as well as several extraneous variables. For my main analysis I want to see if any ...
0
votes
1answer
26 views

How to show the combined effect of two covariates in a simple regression?

Say I want to build a simple model, and I have four variables available to me: Age, gender ($D_1$, 1 is female, 0 male), income, and whether the person is Hispanic or not ($D_2$, 1 is Hispanic, 0 ...
0
votes
0answers
2 views

creating a 3D kernel regression fit from a pool of predictors

i have a ~1k data set that has one response column(z) and 4 hypothesized predictors(x1-x4). i wish to create a 3D plot of the response surface, for example z ~ x1+x1 or z~x2+x4 i know that i need ...
0
votes
0answers
18 views

How do I find and delete Multivariate outliers in Multiple Regression analysis using SPSS

I'm running Multiple Regression analysis using SPSS. I know I have to check for multivariate outliers and my professor requests we use the Mahalanobis distance statistic for this task. The Mah. max ...
1
vote
1answer
26 views

Dummy Variable in OLS regression

I would like to include in my OLS regression a dummy variable with two categories (d=0,d=1)and n=75. When the dummy takes the value of 1, it refers to 19 observations of the 75. Does it matter for my ...
0
votes
2answers
42 views

Multinomial logistic regression analysis or binary logistic regression

I need to analyse data and am unsure whether or not I should use a multinomial logistic regression analysis or a binary logistic regression analysis. I have just conducted a latent class analysis and ...
0
votes
1answer
11 views

Interpretation of Interaction Term with Dummy [duplicate]

I am trying to interpret the following model: y=a1+a2*x+a3*d+a4(x*d)+e where y= growth rate x=debt d= dummy high debt ...
0
votes
0answers
9 views

Inference drawn from multiple correlation coefficient and partial correlation coefficient

I would like an explanation to why we need both types of correlation coefficients. Google helps me with the appropriate formula to calculate the coefficients so I do not think I need any help on that. ...
0
votes
0answers
33 views

When to apply logarithmic transformation to a variable? [duplicate]

My question is in which cases you should think about transforming your variable into a logarithmic one? My dependent variable in the number of patents a long a period of years and one of my ...
-1
votes
0answers
20 views

the suggestion for books on multiple-regression analysis [duplicate]

i have just learned multiple-regression analysis (MSc. level) please suggest most suitable and sound books. thank you. note: we have started the class with the subjcts such as the calculation of ...
0
votes
0answers
26 views

Cyclic Block Design with multivariate adaptive regression splines

I came upon a question of a linear block design for reviewing an article. The experimental design looked as follows: Material of different compostion (9 different combinations) was incubated in a ...
8
votes
4answers
174 views

How can top $k$ principal components retain the predictive power on a dependent variable?

Suppose I am running a regression $Y \sim X$. Why by selecting top $k$ principle components of $X$, does the model retain its predictive power on $Y$? I understand that from ...
0
votes
0answers
7 views

Cluster analysis and multiple regression

I measured market orientation of dealers using customer orientation, competitor orientation and supplier orientation. I used five point likert scales. Using the scores from customer orientation, ...
0
votes
0answers
53 views

How to use residual analysis to remove the effect of confounding variables in a model in R

I want to find which soil variables better explain plant productivity, using a database that contains information for about 100 forests plots across Europe. These plots have only one species per plot, ...
0
votes
0answers
17 views

Can we use Principal component analysis for identifying important Independent variables (X's) [duplicate]

I have read principal component analysis is mainly used for variable reduction. I have the concept that we are converting the variables in to new principal components using orthogonal transformation ...
1
vote
1answer
40 views

How to combine heckman selection and binary endogenous variable in a two-step way?

I want to fit a probit model with a binary endogenous variable and heckman sample selection problem, it's something like ...
4
votes
3answers
219 views

In linear regression, what does $\beta_1 = 0$ really mean?

If granted omniscience and we know that $\beta_1$ in a multiple linear regression model is truly 0, what does that mean in words (and math notation)? The model is: $Y = \beta_0 + \beta_1X_1 + ...