Questions tagged [regression]

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

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7
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2answers
2k views

Difference between bias-variance dilemma and overfitting

I'm wondering what difference it makes whether we talk about bias-variance dilemma where fitting a regression line to the given dataset reduces bias and increases variance or whether we talk about ...
16
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2answers
30k views

Interpretation of incidence-rate ratios

So, I want to fit a random effects negative-binomial model. For such a model STATA can produce exponentiated coefficients. According to the help file such coefficients can be interpreted as incidence-...
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1answer
3k views

When can I suppress the intercept using treatreg?

Can I suppress the intercept if I know the treatment will be zero if the independent variables are zero. Also, can I suppress the intercept if I know the right hand side of the primary regression ...
8
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1answer
6k views

How to specify Bayesian mixed effects model in BUGS

I posted this earlier in the week then retracted the question when I found a good source, not wanting to waste people's time. I haven't made much progress I'm afraid. In trying to be a good citizen ...
12
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2answers
10k views

Difference between t-test and ANOVA in linear regression

I wonder what differences are between t-test and ANOVA in linear regression? Is a t-test to test whether any one of the slopes and intercept has mean zero, while ANOVA to test whether all slopes have ...
2
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1answer
172 views

How do I transform a data generation specification in R into a BUGS/JAGS specification

I am at a loss about what the BUGS/JAGS specification of the following should look like. The background is that one person has four measurements taken by four different instruments. Each instrument ...
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2answers
5k views

Which link function for a regression when Y is continuous between 0 and 1?

I've always used logistic regression when Y was categorical data 0 or 1. Now I have this dependent variable that is really a ratio/probability. That means it can be any number between 0 and 1. I ...
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1answer
2k views

Why would significance of F-value change in linear regression if you change the reference group?

For categorical predictors with k levels, it doesnt matter what you choose as your reference group. So why would the F-value significance change in the linear regression if you change the reference ...
4
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1answer
3k views

What distributions are for the slope and for the intercept in linear regression?

In linear regression from a dataset $\{ (x_i, y_i), i=1,\cdots,N \}$, I wonder what distributions are for the slope and for the intercept? In Excel output, t-tests are used to tell whether the slope ...
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0answers
55 views

Summation representation for multivariate regressions (or other time-saving techniques) [duplicate]

Possible Duplicate: Efficient online linear regression Is there a summation representation for multivariate regressions? For example, if I regress $y$ on $X$ instead of using $\hat \beta = (X'X)^...
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1answer
286 views

Can you still use the least significant difference test even if the F test is not significant?

Can you still used the least significant difference test even if the F test is not significant?
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1answer
1k views

How do I adjust standard errors in a research study in which the control group is constructed via matching with replacement?

I have a treatment sample of 200 firms. I'm using propensity score matching to pair each treatment observation with one control (sampling with replacement, in order to minimize bias associated with ...
2
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1answer
27k views

Interpreting percentage units regression

log(sales) = beta_0 + beta_1 * GDP The usual process of transforming a variable such as price into log(price) to measure an approximate percentage change means ...
4
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1answer
216 views

Does a huge difference in the number of observations in a dummy variable influence its regression result?

I'm currently working on my thesis where I performed a multiple linear regression. The analysis is basically about the impact of projects. Among other independent variables I have the dummy variable ...
6
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1answer
2k views

Weighted regression for categorical variables

I have been trying to use weighted regression on some data where there are three categorical variables with the lm(y ~ A*B*C) command in R. This was to try ...
3
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1answer
150 views

What rules should guide scaling variables to maximise interpretation, particularly within a regression context?

Context: In this previous question @adhesh asked about the benefits of coding a binary variable zero-one rather than one-two. I realised when I wrote this answer that I had quite a lot to say about ...
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4answers
451 views

Fitting data based on an unobserved variable

I have pairs (x, y) which I would like to regress (x independent, y dependent). Plotting them, I see distinct bands which can be attributed to a third variable. Unfortunately this variable cannot be ...
2
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0answers
42 views

How to control for firm survival and allow for different firm types to emerge?

I'm doing program evaluation. I want to use the finite mixture model to test the hypothesis that different types of firms may exist. This is an endogenous determination of the number of different ...
9
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2answers
665 views

Which type of regression to use, considering one variable with upper bound?

I'm not sure which method to use to model the relationship between two variables ($x$ and $y$) in the experiment described as follows: There are 3 variables: $x_{aim}$, $x$ and $y$. The value of $x_{...
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2answers
4k views

Multiple linear regression on a data set with Python?

I'll preface my question with the fact that I'm just learning about linear regression so I may be thinking about this wrong. I have a set of data. In this set I have one dependent variable and about ...
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2answers
540 views

How to interpret basic output from a regression analysis?

I have been trying to interpret the results below, but I am finding it difficult. I wonder if someone could help me. All answers highly appreciated. Number of obs = 30 F( 2, 27) = 19.73 Prob > ...
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3answers
18k views

What is the effect of dichotomising variables?

When dichotomising variables, what information is lost in the process? How does a dichotomisation help in the analyses?
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2answers
120 views

What type of regression analysis is best to model the relationship between self-efficacy and activity level over three time points?

I am conducting an orthopedic study to predict improvements in activity level (dependent variable) based on a type of self-efficacy scale (the independent variable). There are, however, other ...
2
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1answer
5k views

How to perform repeated measures ordinal logistic regression using SPSS? [closed]

I'm usually a UseR. However, for didactic purposes I have to use SPSS today. I have to specify a General linear model with ordinal structure because what I'm examining is: Change in Likert scale ...
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2answers
125 views

Determine gradient from past samples

Again this question may be simple for you, but it is an important aspect for my classification problem. Let`s say I have 5 attributes, which are: ...
17
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1answer
4k views

Properties of logistic regressions

We're working with some logistic regressions and we have realized that the average estimated probability always equals the proportion of ones in the sample; that is, the average of fitted values ...
6
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3answers
1k views

In linear regression, is the $R^2$ value enough to assess whether the relationship between the independent and dependent variable is linear?

In linear regression, is the $R^2$ value enough to assess whether the relationship between the independent and dependent variable is linear? It gives the amount of variability in the dependent ...
104
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6answers
167k views

Difference between confidence intervals and prediction intervals

For a prediction interval in linear regression you still use $\hat{E}[Y|x] = \hat{\beta_0}+\hat{\beta}_{1}x$ to generate the interval. You also use this to generate a confidence interval of $E[Y|x_0]$....
4
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1answer
8k views

Minimum cluster size requirements? Minimum sample size requirements for clustered standard errors?

I have a sample with little over 100 observations and 50 clusters, one quarter of which have only one observation. Is it correct to calculate clustered standard errors in a linear regression that uses ...
3
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1answer
97 views

Does including both raw and per capita measures as predictors reduce significance of either predictor?

I'm running a regression on independent variables, some of which are measured in different units, for example: The amount of broadband connections in a country The amount of broadband connections in ...
0
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1answer
205 views

How to interpret the conditional expected value in a regression?

If you want to estimate $E[y| x = \text{some value}]$ is this just a matter of plugging $x$ into the regression equation? Because you estimate the regression coefficients, therefore the value you get ...
10
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3answers
29k views

Adjusted vs. unadjusted effects in regression

Is "unadjusted" basically just simple linear regression whereas "adjusted" is multiple regression? For example, looking at the effect of x on y adjusting for other variables like a, b and c versus ...
84
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10answers
39k views

What is a complete list of the usual assumptions for linear regression?

What are the usual assumptions for linear regression? Do they include: a linear relationship between the independent and dependent variable independent errors normal distribution of errors ...
7
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1answer
2k views

Selecting regression model for a non-negative integer response

I have a series of non-negative integers $y=(y_1,y_2,..., y_n)$ and a design matrix $y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_1 x_2$, where $x_0$ and $x_1$ are $0$ or $1$, $x_1x_2$ is the ...
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2answers
2k views

How low multiple R-squared value is enough to reject a model?

I'm doing a simple linear regression I tried: ...
2
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2answers
148 views

How to get the points of a linear regression line?

I do a simple linear regression mod <- lm(y ~ x) and I plot its residuals, doing plot(mod$residuals). Two questions: I need ...
2
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0answers
378 views

How to perform coefficient test in linear model with robust covariance matrix?

I estimate a linear model and then calculate a (White) heteroscedasticity robust variance covariance matrix. This allows me to do the following (where hr.cov is ...
9
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2answers
181 views

Is it possible for $R^2$ of a regression on two variables be higher than the sum of $R^2$ for two regressions on the individual variables?

In OLS, is it possible for the $R^2$ of a regression on two variables be higher than the sum of $R^2$ for two regressions on the individual variables. $R^2(Y \sim A + B) > R^2(Y \sim A) + R^2(Y \...
4
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1answer
227 views

How is it possible that these variances are equal?

I'm using the Fligner-Killen test to analyze the residuals of a linear regression. I subdivide those residuals in three groups and then I do the FK test to check the homogeneity of variances. The ...
0
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2answers
524 views

Which type of regression analysis should I use for consumption pattern data?

I conducted a survey asking people of various income groups about their annual expenditure under various consumption brackets such as food, clothing, housing etc. I then calculated the amount spent by ...
4
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1answer
2k views

Local polynomial (linear) regression of binary data — logit transformation?

I got a bit confused about how to fit a local polynomial to binary outcomes if I would rather approximate the underlying index (within a link function) instead. (Basically for the same reason why ...
3
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3answers
4k views

Is it reasonable to treat a three point Likert dependent variable as a continuous variable?

I have a three point Likert scale question: How happy are you? 1= low levels of happiness 2= medium levels 3= high levels I want to do multiple ...
3
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2answers
150 views

Determining the influential features for an outcome

I have a small table like this ...
2
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0answers
126 views

Regression in projective space?

Is there a method for (nonlinear? kernelized?) regression of functions with output in projective space? That is, given a series of examples $x_i\in\mathbb{R}^n$ (or $x_i\in\mathbb{P}^n$) and $y_i\in\...
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1answer
2k views

Distribution of betas in multiple linear regression

I have a question on the distribution of betas in a multiple linear regression scheme The estimated parameter vector is $\hat{\beta}=(X^′X)^{−1}X^′y$ where $X = [1 \; \;x]$ is the $n \times 2$ data ...
2
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2answers
2k views

Why don't my two-stage least squares results make sense?

I used SPSS 19's 2SLS procedure (which is very straightforward, with almost no optional specifications) to predict Y from X after X was predicted based on I, an instrumental variable. Then I tried to ...
1
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1answer
756 views

Is $SSE/(n−2)$ an estimator of the variance of the dependent variable for a fixed x-value?

For linear regression, is an estimator of the variance of the dependent variable for a fixed x-value $SSE/(n-2)$?
0
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1answer
5k views

How to test if the errors of a linear regression are constant? [duplicate]

Possible Duplicate: Kruskal-Wallis or Fligner test to check homogeneity of variances? Is there a test to check if the residuls of a linear regression are constant? Thanks
3
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2answers
168 views

How to analyse a study where measures are taken repeatedly from right and left legs in five walking conditions?

There are 13 subjects in this study, each subject was "repeatedly measured" right and left legs on five walking conditions. The variables of this data set is: ID, Y, Leg, Conditions. The research ...
5
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2answers
2k views

How do I test whether one predictor is significantly better than another? Is Hotelling's T the best option?

Overview: I want to test if "emotional numbing" is a significantly better predictor of "lower intimate relationship functioning" than "reexperiencing" or "hyperarousal" It was suggested to me to use ...

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