Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

learn more… | top users | synonyms (1)

4
votes
0answers
13 views

Adding a variable to linear regression - multicollinearity

Say I have two models $y=a_1x_{1}+b+\epsilon$ and $y=a_2x_{2}+b+\epsilon$ with significant coefficients and $R^2=r_1$ and $R^2=r_2$, respectively. Now I consider $y=a_1x_{1}+a_2x_{2}+b+\epsilon$. ...
0
votes
0answers
7 views

How can the relative importance of a categorical variable in a linear regression model be determined?

A simple example can be seen here:http://www.ats.ucla.edu/stat/spss/output/reg_spss.htm Gender is a dummy coded variable. I completely understand how to interpret this variable. I cannot use the ...
0
votes
0answers
13 views

Correlation to use for non-scale data( counts)

In our college we run some extra non-required sessions that we hope help the students. To decide whether they help, I wanted to correlate the number of sessions attended with the cumulative gpa of ...
2
votes
1answer
25 views

How Residuals of Instrumental Variables Estimation are calculated and why you can have a negative R-squared?

I would like to understand, precisely, why you can have a negative $R^2$ with a 2SLS estimation, such as you have in commands like ivreg2 in Stata. There is ...
0
votes
0answers
10 views

What is the complexity of locally linear regression on many grid points?

Say I have $n$ data points and I want to estimate $f(x)$ at $m$ locations, where both $n$ and $m$ are large. Are there any common algorithms for computing locally linear regression estimates at all ...
0
votes
3answers
73 views

What is the difference between variable and random variable?

I know that "variable" means "values which vary." In a simple linear regression model : $$Y=\beta_0+\beta_1X+\epsilon$$ $X$ is variable that is the values of $X$ vary. Why is $X$ not a random ...
3
votes
1answer
64 views

How is the sigma^2 value (or MSE) for the link function computed in logistic regression in R?

For example, if you have a logistic regression on certain dataset: fit <- glm(y ~ x, data = test, family = "binomial") If you do ...
0
votes
0answers
6 views

Assessing the significance of a linear trend

I am helping a colleague to analyse the trends of some geographical indicators. I'm trying to draw rigorous conclusions on the significance of a linear trend and, given that we are performing the same ...
3
votes
0answers
21 views

What is the relationship, if any, between Stein's Paradox and linear restrictions in regressions?

Suppose we have $$y = b_1x_1 + b_2x_2 + b_3x_3 + e$$ as our regression model. Setting a linear restriction, say $b_1 + b_2 + b_3 = 0$, allow us to rewrite the model as, $$y = (b_1)(x_1 - x_3) + ...
2
votes
1answer
20 views

How to decide which main variable is modified by the interaction term?

Given the following linear model $Y=int+aX1+bX2+c(X1*X2)+e$, where $X1$ and $X2$ are the main variables, ($X1*X2$) is the interaction term, and $a$, $b$, and $c$ are the corresponding coefficients. ...
1
vote
1answer
55 views

Fixed Regressors

In simple regression model regressors are treated as fixed rather than stochastic. This treatment is supported by the argument that whoever designed the experiment set these values. In some domains ...
0
votes
1answer
32 views

How to derive the variance of slope parameter in linear regression

My question directly relates to one that has already been answered - here: Expected Value and Variance of Estimation of Slope Parameter $\beta_1$ in Simple Linear Regression However, there is an ...
1
vote
0answers
32 views

What are the latest developments in new statistical learning algortithms? [on hold]

I was trying to implement statistical learning algorithms in my research. I started with Artificial neural network but later I found out that Boosted decision trees was much better for the task. Now ...
1
vote
1answer
17 views

Regression and contrast codings with multiple categorical variables

In regression with multiple explanatory categorical variables, how should I model the problem to compare the effects of the categorical variables with each other? Most contrast coding schemes (e.g. ...
0
votes
0answers
20 views

the decision of being White noise on e-view

And for example, let's take SMA(2) model in this table does there exist white noise ? Which value I observe to decide the existance of white noise? Please explain it. Thank you
0
votes
0answers
35 views

White noise ACF - PACF

I found PACF and ACF like the following table . But, how can I decide whether there exists white noise? And what is white noise? If there is no white noise, can I say being stationary?
2
votes
0answers
13 views

Examining trends with interactions and with stratification - obtaining discordant results

I'm examining the effect of income (categorized into quintiles) on a response variable during different years (from 1996 to 2014). I adjust for some other covariates and have repeated measurements on ...
1
vote
1answer
36 views

f(y | x) or f(y,x) in regression and MLE

In $Y = aX + b + \epsilon$ where $\epsilon$ ~ $N(0,\sigma^2)$ and i.i.d regression setting If X is stochastic and $E(\epsilon\mid X) =0$, then which one is correct: (1) $f(x,y) = ...
0
votes
1answer
34 views

Does a lower t-stat suggest better evidence for rejection? [duplicate]

Say I have these two models: $y = \beta_0 +\beta_1x_1 + u$ $y = \beta_0 +\beta_1x_1 +\beta_2x_2 + u$ and the $p$ value for $H_0:\beta_1 = 0 $ with $\alpha = 10\%$ for both is less than 0.001, but ...
1
vote
0answers
27 views

Paired homogeneity test: Compare two regression coefficients in R

I have a question about paired homogeneity test. By using Cox regression analysis Im evaluating 2 new biomarkers (A and B) in association with a outcome (X). I developed 2 multivariable models and on ...
3
votes
0answers
30 views

Modelling flight delays with negative values

Modelling flight delays with negative values I am working on a model to predict whether a flight will be delayed. The data consists of some explanatory variables for flights from a specific airport. ...
0
votes
1answer
28 views

Could you use Randomized Optimization in order to find weights for linear regression?

Let's say you are doing linear regression. We are trying to fit $w^Tx=y$. One way to do that is by utilizing gradient descent to minimize this function: $J(w) = \frac1{2n} \sum_{i=1}^{n} (w^Tx-y)^2$. ...
0
votes
0answers
33 views

What happens if I square the variable in my log in OLS regression?

Say I have a model: ln y = B0 + B1(x1) + B2 ln(x2) + u and the B2 estimate I get is 0.5 If I change the model to be ln y = B0 + B1(x1) + B2 ln(x2^2) + u the estimate will change to 0.25, but why ...
0
votes
0answers
17 views

Logistic Regression with significant variables and bad predictions

Can someone explain to me how my stepwise logistic regression model has variables with very low p-values but does not predict very well?
0
votes
0answers
12 views

How to do regression model selection if dummy variables are involved?

Original post on stackoverflow: http://stackoverflow.com/questions/28773153/how-to-do-regression-model-selection-if-dummy-variables-are-involved I am trying to do a logistic regression analysis in R ...
1
vote
1answer
53 views

Maximum Likelihood estimator for family of binomial distributions

For the below example, I am considering Heads as a success and Tails as a failure, when I toss a coin. (Ex: The first row in the the below tables says, when I tossed the coin 10 times I got 3 ...
0
votes
0answers
14 views

Best procedure for evaluating group differences in a Lasso regularized regression

I am evaluating 25 predictors (continuous, ordinal, multinomial) on an ordinal outcome variable using a lasso regularized regression. I am using the lasso for variable selection, to determine which ...
1
vote
0answers
31 views

what is the differences between LDA and MLR? [duplicate]

I know that Linear Discrimination Analysis (LDA) is used for classification and Multiple Linear Regression (MLR) is for regression. Lets say I have a matrix X (independent variables) and Y(dependent ...
0
votes
0answers
13 views

Technical Indicators reference [migrated]

I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
0
votes
0answers
3 views

R Packages for rank-preserving structural failure time model (RPSTM)

I have a randomized clinical trial data that has high percentage(30-40% in both arms) switching over to a different treatment regimen. By browsing through some literature, I am inclined to perform ...
1
vote
1answer
39 views

How to emphasize on specific data points in Linear Regression?

I'm now solving linear regression problems. $y = wx + b + e$ So I have $(x, y)$ data set and want to learn weights $w, b$. Additionally I know that certain data points are not polluted by noise ...
3
votes
1answer
25 views

Is the significance of difference in slopes equivalent to the significance of the slope of the difference of two series?

Say you have an independent variable, $x$, and two dependent variables $y_1$ and $y_2$. I want to calculate whether these two variables have a significantly different slope. I can do it by calculating ...
7
votes
0answers
58 views

When do coefficients estimated by logistic and logit-linear regression differ?

When modelling continuous proportions (e.g. proportional vegetation cover at survey quadrats, or proportion of time engaged in an activity), logistic regression is considered inappropriate (e.g. ...
4
votes
1answer
40 views

completing the square for Gaussian multivariate estimation

I have been trying to derive the posterior distribution in the case of weighted Bayesian regression in the case of multivariate normal distribution for a few days and have been stuck. I am not sure if ...
1
vote
1answer
19 views

Relationships of two regressional coefficients

I have two one dimensional dataset $X$ and $Y$. I run regression and obtained $A$ from $Y = AX$. And another regression and obtain $B$ from $X = BY$. What's the relationship between $A$ and $B$? Is ...
0
votes
1answer
17 views

Relation between R2 and the covariate correlation matrix (multidimensional case)

Following the post : Relation between $R^2$ and the covariate correlation matrix Does it exist a formula for N>3 when N is the number of covariates ? Many thanks
1
vote
1answer
47 views

Detecting anomalies in a time series where new data points will be continuously added

I have a time series data and I will be adding more data points in a consistent manner. I want to figure out whether the new data point added is an outlier, in regards to the previously observed data ...
1
vote
0answers
20 views

Statistical model Regression

The statistical model induced by multiple linear regression problem is: $p(y)=\mathcal{N}(y;w^\top x,\sigma^2)$. $y$ is (obviously it has a density) interpreted as realization of a random variable ...
0
votes
0answers
17 views

best way of univariate prediction for sparse data

I have a client who has sparse hourly data (by sparse I mean there are too many hours with 0 calls). I used TBATS in R to forecast hourly data for them. Regardless of the point forecast, the actual ...
0
votes
0answers
6 views

How to understand the Direct and Indirect Effect tables in AMOS?

I have finalized my model and am trying to understand the standardized direct and indirect effect tables i obtained using AMOS for my path analysis model. If I interpret correctly, the tables are ...
0
votes
0answers
4 views

How polynomial regression is used in arima model [on hold]

i am padmaja pursuing my MTECH ,and i am doing project on big data analytics in which i selected to work with arima model,can you give me mathematical equation to use polynomial regression to work ...
0
votes
1answer
36 views

Relation between $R^2$ and the covariate correlation matrix

I'm quite new to Statistics and I'm facing a problem. Is there any relation between $R^2$ and the correlation matrix of the covariates? A short example is (case with 2 covariates) : A7 ~ A1 + A2 ...
1
vote
1answer
19 views

Logistic regression: Overall significance of categorical predictor in SPSS

In SPSS, when performing binary logistic regression using multiple categorical predictors, a significance level is detailed for the variable overall in addition to each category. This strikes me as ...
0
votes
0answers
11 views

Anti-correlated regression predictors

Intuitively perfectly anti-correlated predictor variables in regression would have the most stable coefficients since they contain no shared information, e.g. \begin{equation} \begin{bmatrix} y_1 \\ ...
0
votes
0answers
23 views

Regression of Higher order [migrated]

I want to fit a model $Y = X_1^2 + X_2^2 + X_1 + X_2 + X_1\cdot X_2$ How to build this in R glm(Y ~ poly(X1,2) * poly(X2,2) how to generalise it to higher order ...
0
votes
0answers
8 views

Different results in stepwise and enter method in linear regression

Using linear regression on my data with enter method gave significant ANOVA and significant beta coefficients but when stepwise method is used no variables entered the regression equation. T values ...
0
votes
1answer
19 views

Statistical independence of least square estimator and residual in multiple linear regression

I'm currently self studying linear regression. Following is an entrance exam problem of a graduate school. Consider the regression model with usual assumptions of the errors $y=X\beta+\epsilon$. Show ...
0
votes
0answers
7 views

Adjusting for outlier in Fractional logit in R when dv is very small proportion

I used the code from this site: http://stackoverflow.com/questions/19893133/fractional-logit-model-r to estimate a fractional logit model. There are 90 observations in my dataMy dependent variable is ...
2
votes
1answer
6 views

Is the order of parameter estimates preserved from multiple simple regressions to one multivariable regression?

Assume I have $y$, $x_1$ and $x_2$. I regress $y\sim\alpha_0 + \alpha_1 x_1$, $y\sim\beta_0 + \beta_1 x_2$ and $y\sim\gamma_0 + \gamma_1 x_1 + \gamma_2 x_2$ using Ordinary Least Squares. Does ...
0
votes
0answers
7 views

Using different sets of binary indicators based on another indicator - R, linear regression

I'm trying to come up with a prediction model for an output based on the hour of the day. I already have a simple model that predicts the output based on 23 factors that represent each hour of the day ...