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

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21 views

Linear model / models analysis

Above are three plots of the Linear model I am trying to analyze. The first one is a basic plot of the linear data: ...
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1answer
28 views

Standardized coefficients for linear models with numeric and factor variables in multiple linear regression using scale() function in R

I have on question regarding standardized coefficients (beta) in linear models. I have already asked one question here. From the answers I assume that I should use R's ...
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1answer
36 views

When to use bootstapping in regression analyses?

When I run a regression analysis in SPSS, one of my predictor variables just fails to reach significance, p = .06. When I apply bootstrapping, the output tells me the predictor has a significant ...
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4 views

Survey analysis: relationship between one discrete outcome variable and multiple categorical and ordered categorical variables

I have a data set that is the result of a survey. The survey asks the respondents to name 5 people in their community whom they turn to for advice. It then goes to these 5 people and asks the same. I ...
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20 views

Analysis of a longitudinal study where interventions are received at different time points

I have a data set of university students. The university has 8 different assisting programs, mostly like scholarships, for needy students to help them concentrate on their studies. Since it costs a ...
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27 views

Multivariate regression with categorical response variables

Explanation of Data: I started with a data set where each user belong a specific group and their contribution to different domains. After multiple pivots and pre-processing attempts, I got my data in ...
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14 views

Getting estimates of hospital specific stroke admissions data

I am analysing a small data set on stroke process of care gathered from public sources but don't have access to hospital specific emergency stroke admissions over a period of time data except for ...
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33 views

Selecting a multiple linear regression model with categorical variables

I am trying to analyze the Berkeley Guidance Study to practice multiple regression models, which has 10 continuous variables, 1 categorical variable (with two categories) and the response variable. ...
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1answer
17 views

Determining amount of change in correlated variables

Say I have 3 independent variables (x1..x3), all highly correlated with a dependent (Y). I'm looking to determine if x1 changes by a certain amount, how much Y would expected to change. Also, looking ...
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26 views

Modelling interaction

How does adding interaction term in the model adjust for it or why do we need to add interaction? I am working on logistic regression model with treatment and race as predictors. I have added ...
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20 views

How do I choose the optimal number of lags?

I am making a model (multiple regression) that predicts credit growth. Many of the independent variables are leading indicators and should therefore be lagged. How do I choose the optimal number of ...
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1answer
22 views

Oaxaca Decomposition: Unexplained Constant

I am doing an Oaxaca decomposition of the Log Wage Differential between Whites and non-Whites. I would like to find out if there is any interpretation for the constant term under the unexplained ...
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1answer
20 views

Finding Bias$(s^2)$ in incorrect linear model

I am unable to find the bias of the sample variance estimator in this problem. Unfortunately I keep coming up with Question: Suppose that the true linear model is $y = X_1\beta_1 + X_2\beta_2 + ...
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2answers
36 views

Magnitude of standardized coefficients (beta) in multiple linear regression

Being aware of that article, I am curious about the question how big standardized coefficients can get. I had a discussion with my professor about that issue and she was arguing standardized ...
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6 views

Estimate standardized coefficients (beta) using lm.beta() from package 'QuantPsyc' [migrated]

I want to estimate the standardized coefficients (beta) from a multiple linear regression object using lm() function in R. I use the lm.beta() function from the package 'QuantPsyc', but I do get a ...
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1answer
39 views

I am having trouble understanding (and implementing) logistic regression for classifying into three classes

(For reference, i am using Kevin P Murphy's Book "Machine Learning: A Probabilistic Perspective" and implementing with MATLAN - without any toolboxes) I have a dataset with 392 samples (rows), each ...
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10 views

Help required in controlling for socioeconomic factors when mapping travel behavior

Background I have cleaned disaggregate travel data down to a household level (of about 75% of households) and socio economic data aggregated into 100-200 household blocks. What I'm wanting to do is ...
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32 views

questions on characterizing a variable into ordered, categorical or continuous

When building the regression models, some independent variables are of the following type x={1, 2, -100.1, 200.1, 300, 0, 0, 0, 0, 4000} If there exists an ...
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1answer
36 views

Interpretation of betas when there are multiple categorical variables

I understand the concept that $\hat\beta_0$ is the mean for when the categorical variable is equal to 0 (or is the reference group), giving the end interpretation that the regression coefficient is ...
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19 views

How to compute F-statistics for each features of regression models in glmnet?

I have learned lot's of Lasso regression models(20000) using glmnet. I need to compute somehow test statistics for each features of models. like F-statistics,... Can I do this using bootstrapping ? ...
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1answer
30 views

build model with complicated types of feature variables

I have been asked to build a model to predict a life span of a material based on a couple of features. The features can be classed into the following categories: 1) The feature variables just have 0 ...
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14 views

How to share weights of between regression models when you learning them simultaneously?

I have many phenomenons which I want to model them as lasso regression problem. every phenomenon have it's own distinct features set. but for some phenomenons, the subset of features set are the ...
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1answer
21 views

Confused Beginner Question: Multiple simultaneous tests and signal pollution

The best way for me to explain the problem might be to just give an instance of our data and the type of test/conclusion I am attempting to draw. Unfortunately as the title suggests, my understanding ...
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1answer
21 views

Which mean to use for centering variables when sample definition varies

I am centering variables that enter interaction terms in my linear regression. To check the robustness of my results, I exclude certain cases from the original sample, and re-run the regression ...
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7 views

How to fit a series into another one so that their mutual information is minimized?

I am building a multivariate model of an output series. I have many series-candidates for inputs. I want to select the inputs based on the mutual information between these inputs and the output. My ...
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1answer
28 views

Inflation of Adjusted R2

Consider the case of a multiple regression model, with about 10 regressor and very few observations (about 15). I have to choose 10 out of 20 available regressors, to be included in the model. In many ...
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1answer
41 views

What is the benefit of knowing the F statistic in multiple linear regression?

One of the basic figures you get when running multiple linear regression using almost any off-the-shelf software is the F statistics. However, I cannot recall one situation, where the F value was low ...
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38 views

Can factor analysis improve the fit of a predictive regression model?

My company is working with a client who have built a logistic regression model to predict whether kids with psychiatric disorders will successfully complete a State intervention program (Yes or No). ...
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2answers
43 views

Multiple logistic regression and public behavior

I'm trying to develop a model to forecast the behavior of the public... specifically, in horse racing. Most models in horse racing use whether or not the horse won as the dependent variable and then ...
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2answers
56 views

Controlling for age in multiple regression

I am a little stuck on what how to implement and interpret a multiple regression while controlling for age. I am interested in seeing if there is a positive relationship between depression and use ...
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31 views

Regression where Dependent Variable Ranges in Value

I need to perform regression analysis, but my dependent variable doesn't have a fixed value, but is in a range. So, for example, one of my dependent variables might have a value of $51\pm 3$, another ...
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1answer
86 views

Summarizing and plotting several combined relationships with LMER

I want to see if there is a significant relationship between predictor and value. I've measured these two variables in 6 different conditions (not a full 3 x 3 design because feature1 and feature2 ...
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2answers
91 views

How to perform residual analysis for binary/dichotomous independent predictors in linear regression?

I am performing the multiple linear regression below in R to predict returns on fund managed. reg <- lm(formula=RET~GRI+SAT+MBA+AGE+TEN, data=rawdata) Here ...
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31 views

Correlation and regression giving opposite results

I have two variables with ordinal, not normally distributed data. A Sperman's correlation coefficient indicates a negative correlation. After, I performed a multinomial regression which indicates a ...
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Regression analysis is dependent of non-parametric test result?

If I have two variables (i.e. number of pills and skin sensitivity) and I performed a Kruskal-Wallis or ANOVA which gave me no significant differences in any group. Does it make sense to perform a ...
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Factor analysis using “outliers-only” time series

Some background I run a factor analysis of a time series $Y$ using a standard OLS model with n+1 independent variables $(F,X_1...X_n)$, where $F$ is the main factor (from an explanatory power ...
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1answer
18 views

Predict after using Box Cox Transformation

I am doing a Multiple Linear Regression on a data set where: The response variable is continuous One of the explanatory variables is continuous and the rest are binary(categorical) 1 if it is there 0 ...
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1answer
47 views

A regression specification problem: what if one control variable is a function of another—does this cause any issues?

Suppose you run a regression: $y_i = \beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2} + \epsilon_i$ but you believe that: $x_{i1} = f(x_{i2})$ will this cause any issues for your estimation and ...
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3answers
51 views

What happens to R squared when you take out a variable from a regression?

Im assuming the model & estimations would be less accurate, causing the residuals to be larger, therefore, it makes R^2 larger. Just want to make sure and see if anyone has any insight for me. ...
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1answer
85 views

(Automated) feature selection in multiple regression with categorical variables

I need a general guide on what are the appropriate approaches to automated feature selection in multiple regression with categorical variables. In my case, I have several numeric and categorical ...
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24 views

Sample size for a moderated hierarchical multiple regression analysis?

I am having a challenging time finding out the necessary sample size for a moderated hierarchical multiple regression analysis. The first five steps of the regression will include the 17 variables ...
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2answers
61 views

Calculate necessary “treatment group size” for power in a regression setting

I have an observational quasi-experimental study, where I try to estimate the effect of a "treatment" (participation in a programme) on a continuous outcome. Participants (some two-thirds of all ...
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15 views

What methods should I use to determine which poll is the best at predicting college football game outcomes?

Okay, So I'm interested in trying to determine which college football polls (AP, Coaches, ESPN, and reddit.com/r/CFB) are best at predicting the outcomes of the Top 25 weekly games. I've collected ...
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1answer
65 views

R and JMP produce different regression results

I recently started transitioning from JMP to R and to get started, I've been trying to reproduce some of my old JMP results in R. However, when I run a multiple regression with one continuous variable ...
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25 views

creating a equation for non linear multiple regression to predict a value based on the inputs given in excel

I have data with 5 columns namely Botany,Zoology,Chemistry,Physics and Rank in a excel sheet . The data here is non linear . So I want to generate a equation in the form of y=a+bx1+cx2+dx3 In ...
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31 views

How to interpret residual vs fitted values plot with clustered points

I am performing a multiple linear regression and I have a plot of the my first two explanatory variables vs the residuals and also a plot with the residuals vs the fitted values. I am not quite sure ...
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3answers
188 views

What kind of residual plot does this variable have?

I am doing a multiple regression analysis and my focus is finding the best set of independent variables for prediction. I am starting to know my dataset and the behavior of each variable. I am doing a ...
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1answer
29 views

Survey data: finding the most significant predictors?

I have about 5,000 responses to a survey in which users were asked how strongly they agreed with a statement on a 5-point Likert scale. This response is my dependent variable - I want to find out what ...
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1answer
102 views

OLS vs IV estimates - Sign and Significance

Assume I have an equation with 1 endogenous variable, and many other exogenous variables. Also assume I have 2 valid instruments for the endogenous variable for IV estimation. If I were to estimate ...
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1answer
13 views

Pretest posttest regression

I am trying to set up a multiple regression for a pretest posttest dependent variable. There will be no control group, and we will be controlling for age (continuous) and gender. Want to use the ...