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

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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 ...
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1answer
21 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) = ...
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1answer
28 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 ...
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19 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 ...
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29 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. ...
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1answer
25 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: $J(w) = \frac1{2n} \sum_{i=1}^{n} (w^Tx-y)^2$. ...
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32 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 ...
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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?
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11 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 ...
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1answer
51 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 ...
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12 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 ...
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30 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 ...
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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 ...
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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 ...
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1answer
38 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 ...
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1answer
24 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 ...
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48 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. ...
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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 ...
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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 ...
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1answer
16 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
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1answer
46 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 ...
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19 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 ...
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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 ...
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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 ...
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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 ...
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1answer
35 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 ...
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1answer
18 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 ...
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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 \\ ...
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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 ...
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7 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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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19 views

Incorporating systematic error in (spatial) predictive modelling

I have created a model (random forest) and withheld 20%. When I apply the model to the withheld dataset and check the residuals against the real values I can see there is a systematic error e.g lower ...
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23 views

Question on a line

Suppose we perform a linear regression on a set of data, and get the following least-squares regression line: y=3.2x-17. Without even looking at the specific data, what can we conclude from the ...
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23 views

What regression to use for integral and scale data?

I am hoping to compare reaction times with ratings from a questionnaire. The ratings on the questionnaire could range from -9 to +9 although in the data as expected they are only positive results ...
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10 views

Bias linear regression R [on hold]

Consider a linear statistical model: y = a + bx1 + cx2 + e The parameters are a, b, c and sd(e), The analysis is focused on valid estimation of b. If cov(x,z) =/= θ, and z is excluded from the model ...
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16 views

Can I have a Bayesian approach to analyse my data set? [on hold]

I have a data set with tax incomes from different types of taxes(eg: Income tax, VAT, Goods & services).I want to forecast the each tax revenue for next 10 years using an statistical approach.Can ...
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1answer
98 views

finding out 2D transformation given a list of sample points

I have a bunch of 2D points that are rotated by $\theta$ and translated by $(\Delta_x, \Delta_y)$. I.e. $$ x'=x \cos(\theta)-y \sin(\theta)+\Delta_x \\ y'=x \sin(\theta)+y \cos(\theta)+\Delta_y $$ ...
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2answers
53 views

Newey-West standard errors in OLS

I am trying to compute robust coefficient estimates for OLS, using the hac() function in MATLAB (see description of function in MathWorks). In my case, I am ...
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1answer
20 views

Multiple regression a-priori analysis of power for sample size

I have used g-power and an online calculator to determine the sample size for a 2 factor regression but am unsure as to what effect size I desire as for a large effect size (.5) I only need 20-30 ...
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1answer
30 views

Regressing a discrete variable

I have a discrete dependent variable (say, number of units bought) and want to run a linear regression with in-store promotion, seasonality, trend etc. as predictor variables. I'm not sure if it is ...
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7 views

mean difference test with cross-sectional dependence

I have the following question: I am estimating two Regression models both having the same dependent variable. I want to test the mean difference of the two regressions (difference of Regression ...
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57 views
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Regression, Loss Function, Estimation Dilemma

I am trying to have an in depth understanding of predicting the value of a random variable given additional information. Suppose that $Y$ in $R$ and and $X$ in $R^n$. We would like to have a best ...
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42 views

Why would a regression model predict super huge numbers?

I have a set of 55 items. Each item is defined by 6 values. I am doing 55-fold cross validation: training a model on 54 items, predicting on the 55th. The 6 values of the 54 items are used in some ...
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18 views

What are the advantages of using logistic regression with kernel over others?

What are the advantages of using logistic regression with kernel over others type of logistic regression(e.g.,dot)?
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1answer
22 views

Adjusted R-squared: number of terms or independent variables?

When applying a multiple linear regression, does the adjusted R-squared value depend on the number of independent variables in the model or the number of terms? Specifically, I'm concerned that adding ...
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0answers
32 views

Asymptotic distribution of $\hat{B_1}$in simple linear regression

I am currently studying how to $\bf{identify}$ the parameter $B_1$ in a simple univariate regression model where we have $Y=B_0+B_1X+\epsilon$ with the usual assumption of $X$ being exogenous, ...
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2answers
41 views

main effect in logistic regression with the presence of interaction

I just have a question about how to get the main effect in the presence of interaction effect. I have two cohort: say cohort A and cohort B . For cohort A, I have this code as 1. Zero for cohort B. ...