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

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Negative fitted values in OLS regression

I am running a regression where my dependent variable is a cross-section of variances. Therefore, I require my predicted values (fitted values) to be positive. However, when running a simple OLS ...
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7 views

How to perform regression with a sensitivity analysis in R

Without using non-base packages like plm, how can I perform a fixed effects regression in R with a sensitivity analysis for one or several other variables? Some ...
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1answer
13 views

How to find optimal penaltyparameter C for SVM (regression)

I am training an svm regressor using python sklearn.svm.SVR From the example given on the sklearn website, the above line of code defines my svm. ...
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1answer
28 views

Regression function of “non-regressible” data

I have some background in probability, and now trying to understand statistics, which sometimes leads to the questions of the following kind. Let $X$ and $Y$ be two random variables that represent the ...
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1answer
13 views

Bootstrap glm and extract pvalue

I am running a glm model using bootstrap, I can extract the coefficient mean and the confidence intervals for all the factors in my model. But how can I get the pvalue from there? Model: ...
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1answer
50 views

How to compute confidence bound in linear regression

In a simple linear regression problem, let $A$ be an $m\times n$ matrix of samples, $A=[x^T_1; x^T_2; ...;x^T_m]$, $w$ is the $n\times 1$ parameter vector, and $b$ is $m\times 1$ response vector. The ...
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1answer
11 views

main effects of moderating variables

I am sorry if this is very trivial and a repetition. I could not find a direct question on the website that addresses my question I am studying the relationship between X1 (independent variable) and ...
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16 views

Can I interpret a simple slope if the product is not significant?

I used Hayes' PROCESS macro to run a simple regression. The interaction product was not significant (p=0.13) however the conditional effect (simple slope) was significant (at high levels of the ...
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1answer
16 views

Estimate effect on mean of dependent variable of an increase in the independent variable in a linear regression

Suppose I have the linear regression equation: Y = B0 + B1(x) How do I find the estimated effect on mean Y of an additional 50 to x? I believe this is the multiplicative effect.
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39 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 ...
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9 views

Regression Output from R [on hold]

I'm trying to regress the outcome variable "count number" on its lag and season, where 1,2,3,4 represent spring, summer, autumn and winter, respectively. However, I got some very weird output from R. ...
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14 views

Excluding Outliers and Influential Observations ($R^2$ and AIC/BIC)

I am working on a cross-sectional data set relating mortgage payments to debt-income ratios. I have some extreme outliers and experimented with excluding them from the model (some 30 observations of a ...
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17 views

One Step Ahead Forecasts Using Predict() in R

I just fit a model to a time series. I am now required to generate a 10-year extrapolation forecast of my model. My model includes a time term, a time^2 term, 12 seasonal dummies, and 4 lagged ...
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1answer
34 views

Comparison of predictive models

I am trying to compare the predictive ability of various models in predicting survival in patients. I would like to examine the predictive performance of each model using 4 tests: squared Pearson ...
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20 views
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40 views

Econometric Model : [on hold]

I don't understand which model to use for "Socioeconomic factors affecting non farm labour supply for households" . I am working with Household Income and Expenditure Survey data (HIES) Bangladesh, ...
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23 views

Handling Sparse Data Frames - algorithm selection

I am new to machine learning/statistical modelling. I am trying to run a classification on a highly sparse dataset with 100 features, most of which are categorical (TRUE/FALSE) with the remaining ...
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13 views

Relation of distributions

I want to predict a distribution using multiple related distributions. One method is to use multiple regression (the model specification is that the dependent variable, yi is a combination of the ...
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0answers
7 views

Binary Logiistc regression and covariates in SPSS

When running binary logistic regression, where there is an dependent variable, multiple independent variable and covariates, where do I put the covariates in SPSS? Would they go in the covariate box ...
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12 views

R: Error with mlogit Conjoint modelling - system singularity

I am building choice models on a dates about coffee preferences. I have 5 alternatives: Brand, Cup, Price, Certification and Local Community Support. The data looks like this: ...
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32 views

The effect of the order of observations on the distribution of $\hat{\beta}$ in Linear Regression

Consider linear regression. It is known that if $Y \sim N_n\left(X\beta, \sigma^2 I_n\right)$, where $X$ is $n \times p$ of rank $p$, then $$ \hat{\beta} \sim N_p\left(\beta, ...
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29 views

$\hat{\beta}^{(M)}_i\sim \hat{\beta}^{(N)}_i$ for linear regression?

Consider an i.i.d. sample $(X_1, Y_1), \dots, (X_N, Y_N)$, where each $X_i$ and $Y_i$ are $n$-dimensional column vectors, let $M \leq N$ and denote by $\hat{\beta}^{(M)}$ and $\hat{\beta}^{(N)}$ the ...
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1answer
27 views

Linear regression with redundant features (perfect multicolinearity)

Suppose $X \sim N(0,1)$, $Z=X$, and $Y=X$. An ordinary least squares regression problem is solved: $min_{(b1,b2)} \|Y-(b1*X+b_2*Z)\|_{2}^2$ This is a strictly convex function which must have a ...
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1answer
22 views

Unstandardize the slope of standardised variables in a linear regression

If I standardize my dependent and independent variable, and run a linear regression between them, the slope estimate which I have will be standardised. The variables were standardised by subtracting ...
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22 views

ANOVA result with insignificant factors

I'm having difficulty interpreting the result I get from ANOVA. Specifically, if some of the factors I put into the model have an insignificant impact (p-value > 0.05) on the output, does it mean I ...
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1answer
49 views

What is the value of “X” in a regression equation when dealing with a time series?

I am using excel to add a polynomial trend line to a chart. The chart and the formula of the trend line are shown below. I want to add lines indicating different confidence intervals so I need to find ...
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122 views

How do residuals relate to the underlying disturbances?

In the least squares method we want to estimate the unknown parameters in the model: $$Y_j = \alpha + \beta x_j + \varepsilon_j \enspace (j=1...n)$$ Once we have done that (for some observed ...
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20 views

Two-Step estimation

I am currently trying to fit a model that has the following properties: (1) data for several years (2) two decisions/equations (probably involved): (a) one that explains an initial choice of product ...
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47 views

Linear regression with log dependent variable

I have the following regression: $log(Y) = \alpha + \beta X + \epsilon$ with $E[\epsilon] = 0$ and $var(\epsilon) = \sigma^2$. There is no assumption on the distribution of the errors $\epsilon$. In ...
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27 views

Y vs X with spatial variation

I am trying to learn a relationship between two variables Y and X, X is the independent variable. The nature of the relationship is quadratic, from some domain experience, but the relationship itself ...
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1answer
41 views

Improving a regression model based on diagnostics

I have two continuous predictors ($x_1, x_2$) in my data set and a continuous response variable $y$. The data set is by date for 6 quarters. Since I observed a clear pattern of surge during weekday ...
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1answer
38 views

Investigating interaction

Please I need to check for interaction before building an explanatory model (logistic regression). I have 16 interaction terms in total. Please how what is the best way to go about it. Will I need to ...
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26 views

Aggregating forcast to higher spatial level

I am working on a project doing out of sample estimates of wheat yields at the village level in STATA. I am using a short panel 3 years and a sample of ~3000 villages. We are estimating a RE model and ...
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20 views

How to account for data taken at $t=0$ when using $\log(t)$ in the model? [duplicate]

I have a data set with four observations consisting of the variable $Y$ measured at time $t_0=0$ and at times $t_1, t_2$ and $t_3$. I would like to fit the following model: $$\log(y_j) = \alpha + ...
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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 ...
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11 views

Support vector regression in weka

I am using SVR for statistical down-scaling of precipitation. I have taken the first 3 factor scores in principal component analysis of variables as predictors and precipitation as predictand. As ...
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2answers
148 views

Is there an elegant/insightful way to understand this linear regression identity for multiple $R^2$?

In linear regression I have come across a delightful result that if we fit the model $$E[Y] = \beta_1 X_1 + \beta_2 X_2 + c,$$ then, if we standardize and centre the $Y$, $X_1$ and $X_2$ data, ...
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Transform time-dependent data

I have pollution data (quantitative) plotted against time (categorical), the hours of the day. Via ANOVA testing I've found significance at many of the hours, however, the relationship is definitely ...
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29 views

OLS derivation question [duplicate]

How come I always see the derivation of $\hat{\beta}$ in OLS using matrix differentiation and solving for when the derivative is $0$. Couldn't one just derive it also by noting that in $Y = X\beta + ...
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12 views

How to calculate y predictor based on ANCOVA estimates? [duplicate]

My formula looks like this: lm(formula=BearWeight ~ honey + age, data=BearData) my output looks like this I am told to interpret each estimate and then use the estimates to predict the weight of a ...
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29 views

KNN Regression in R - using KKNN package [closed]

I have been trying to figure out how to plot a multiple regression for a training set with the K(KNN regression). The package name is KKNN for R. The line below expresses the multiple regression model ...
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35 views

Regression using aggregate (averaged) data

I'm doing a project involving the impact that different college grading systems have on MCAT performance. I have access to the following data: Grading system by college Median MCAT score by college ...
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1answer
19 views

Correlation of change in expression with response variable

I have paired gene expression data before and after a treatment, as well as an ordinal response variable with 3 levels for each sample after treatment. I am interested in the correlation of the ...
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0answers
11 views

handling trend in predictor and response variable

I am trying to create a linear regression model containing two predictors and 1 response variable. My response variable has a short term pattern, i.e. surge during weekdays and slump during weekends ...
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22 views

regarding skip the intercept term once it is not statistically significant [duplicate]

After building the regression model, the intercept value is not statistically significant Is that reasonable to just skip it in the final regression model?
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31 views

Bootstrapping with bootstrap sample greater than original sample

My original sample has 350 observations drawn randomly from a population of 60,000 people. My independent variable is Default, with 35 observations with value of ...
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0answers
18 views

Ideas , if possible to reduce number of predictors before applying shapley value regression

The shapley value regression method (lmg) is very computational intensive for a larger number of variables. Is there/Is it possible to limit the number och screen the variables in some clever way ...
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23 views

Intution on Interchangability of Regression and Classification

Dear Oracles of CrossValidated, I've been trying to gather intuition on the relationship between methods that seems to be escaping me. Can someone explain how regression and classification can be ...
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1answer
29 views

Are insignificant variables included in calculation of predicted probabilities?

When calculating the predicted probabilities in a logistic regression model, do we consider all the variables or just the significant ones? For eg: Let's say my model has: dependent variable Y and 3 ...
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33 views

Deriving single linear regression parameters in terms of multiple linear regression parameters

Suppose the true population model is $$\ln(wage) = B_0 + B_1 \cdot education + B_2 \cdot experience + v,$$ where $v$ is the error term. Suppose the model is estimated as $$\ln(wage) = B_3 + B_4 ...