Questions tagged [multiple-regression]
Regression that includes two or more non-constant independent variables. Also known as multivariable regression.
4,969
questions
0
votes
0
answers
6
views
Z-scores and comparisons of means
I am interested in comparing mean protein levels (dependent variable) across four groups (independent variable), while controlling for covariates/ confounders. I will perform this (essentially ANCOVA) ...
- 23
0
votes
0
answers
12
views
Calculate P values for regression models(Linear,Lasso Ridge,Elasticnet) manually [closed]
I want to calculate P value manually from the Multivariate Regression models(Lasso,ridge,Linear and Elasticnet) and i had tried to use the following code as shown below as one person suggested using ...
1
vote
0
answers
8
views
Comparing constants multiple regressions
My objective is to compare the returns of several portfolios. In short, I have multiple independent variables (4) that benchmark returns of different market portfolios (Carhart 4-factor), where the ...
0
votes
0
answers
10
views
Appropriate test for categorical independent variables with sub-level
I am stuck on how to analyse my data. I have one dependent variable and a number of indepen dent variables which are divided into further sub-categories. Some of the options for the value of an ...
- 1
2
votes
1
answer
271
views
How to assess normality under the OLS assumptions?
When we have a multivariate regression function, which assumption has to hold so that the OLS assumptions are not violated:
...
0
votes
0
answers
27
views
How to choose between one-way, two-anova and multliple regression?
I am working on the data set that consists of Patients' id (after stroke), Time (they can walk after going through the program), the Program they follow and the Visit number when the Time was measured....
- 45
1
vote
1
answer
27
views
Find a linear scale factor and offset that minimizes total variance between two observed data sets
I have two discontinuous observational datasets that should roughly match up after linear scaling is applied to one and its offset is adjusted. They will not follow any kind of trendline, so ...
- 111
0
votes
0
answers
8
views
Calculating global and local effects in a multiple regression using Cohen's d
I am calculating the effect size Cohen's d using linear regression. I am looking at the effect of disease on memory, and have also added age, education and sex as confounds to the regression. Cohen's ...
- 121
1
vote
1
answer
26
views
Permutation testing: Which variable should be shuffled?
When conducting a permutation test, which variable should be shuffled?
Let's say I have two variables X and Y, and my test statistic is the correlation coefficient between the two variables. To test ...
- 21
0
votes
0
answers
9
views
QQ Plot help meaning [duplicate]
How can I interpret the following QQ Plot?
Can you explain it for example for the point 20 and 12?
- 1
0
votes
0
answers
12
views
Difference between the siginificance in the summary table and the ANOVA table in R [duplicate]
In R when I am evaluating a linear model I have created, I often use the summary table and the ANOVA table. The first image below is the output from my summary table, the image below that is the ...
- 113
0
votes
1
answer
15
views
How to interpret output of a moderation with 2 moderators (1 Dichotomous, 1 continuous) and a dichotomous predictor?
I have conducted a moderation analysis with two independent moderators. (Using Hayes Process model 2). I have attached the output for your reference.
I am looking for the impact that financial ...
- 21
1
vote
1
answer
26
views
How to check confounding and mediation in large dataset?
Given a large dataset, one cannot possibly check every model. In particular, it does not seem clear to me that one can check confounding or mediation in either cases.
How does one check confounding/...
- 839
0
votes
0
answers
9
views
Why is the value of SE for the transformed regression model higher than the initial model with autocorrelation?
Is it right that with positive autocorrelation in the errors, the model underestimates the SE? Hence, using generalized differencing (such as Cochrane-Orcutt), the transformed model has a higher value ...
- 1
1
vote
1
answer
26
views
Question about deciding whether something is a moderator or covariate
Based on what I know, a moderation analysis is simply looking at an interaction between an IV and a moderator. However, people often refer to moderators when they talk about ANCOVA (analysis of ...
- 21
0
votes
1
answer
44
views
What does it mean if my confidence interval includes zero with a significant p value in linear regression analysis?
I performed linear regression analysis to assess the associations between continuous variables. I found a significant p-value but my confidence interval includes zero. What does it mean? Here are the ...
- 51
0
votes
0
answers
23
views
how to measure the effect of a recurring event?
I have multiple cities with data on theatre visitors and an event as the mentioning of the theatre in the local news.
I want to estimate whether the event of a mentioning lead to more visitors for the ...
- 1
0
votes
0
answers
10
views
Adjusting for multiple testing: unsure about number of tests to correct for
I am interested in comparing mean protein levels between 4 groups (Control, Stages 1-3), after controlling for age, gender and a genetic risk factor. My pre-specified comparisons are : Control vs ...
- 23
1
vote
0
answers
15
views
Derivation of residual bivariate expression to find the value for a multiple linear regression coefficient
Could someone help me with this derivation? How do I get to this expression of $k$?
$y_i=\beta_0+kT_i+\beta_1X_{1i}+...+\beta_kX_{ki}+u_i$
$k = \frac{Cov(Y_i, \tilde{T_i})}{Var(\tilde{T_i})}$
where $\...
- 113
0
votes
0
answers
29
views
Unbiased estimator of regression coefficient in high dimension
Is there any unbiased estimator for the regression coefficient $\beta \in \mathbb{R}^p$, p >> 1, where
$$
y_k = x_k^T\beta + \epsilon \in \mathbb{R}?
$$
Note that $x_k \in \mathbb{R}^p$ and $\...
- 21
0
votes
2
answers
191
views
What's WRONG with my multiple regression model
I am working on a regression model, more precisely, multiple regression model for predicting one single value. I have a dataset of cars and some technical data.
For example, I have the following ...
- 59
-1
votes
0
answers
39
views
Most marketing channel have negative coefficient in econometrics model for sales?
This is the first time I am working on an econometrics model (Market Mix Modelling) for sales. I have weekly sales data and the number of impressions from various marketing channels like FB, Twitter, ...
0
votes
1
answer
22
views
How to interpret fitted coefficients in a multiple regression model: binary, continuous, and interaction terms
Suppose I have a multiple regression model:
$y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \beta_3x_1x_2 + \epsilon$
where
$y$ is continuous
$x_1$ is dichotomous (0 or 1)
$x_2$ is continuous
If $x_1 = 0$, ...
- 674
0
votes
0
answers
10
views
How to compare return percentages of products in different lifecycles?
For my project, I am trying to predict return ( when a product in ecommerce sale is returned) rate of products. For the same of simplicity, assume I have 3 static features (dont change in time) and ...
- 1
0
votes
1
answer
23
views
What is the intercept in a regression model with demeaned dependent variable?
Suppose you have a regression model
$\tilde{y}$ = $X\beta$ + $\varepsilon$,
where
$\tilde{y}$ = $y$ - $\bar{y}$
and $X$ contains a constant.
If you estimate the model by OLS, does the estimated ...
- 1
0
votes
1
answer
39
views
Multiple linear regression: Do all independent variables need to have good adjusted R-squared independently?
I'm very sorry if this should be obvious, I'm just feeling a little lost with this assignment..
I have four independent variables X1,X2,X3,X4 plus a constant, modelled against Y. I know X4 to be ...
- 1
0
votes
0
answers
28
views
Covariance matrix of errors for homoskedasticity/heteroskedasticity
I've seen homoskedasticty and heteroskedasticity defined as the following
The error term of our regression model is homoskedastic if the variance of the conditional distribution of $u_{i}$ given $X_{...
- 93
0
votes
0
answers
29
views
Transforming for assumption of normality (normality of residuals)
My model has a violation of normality assumption so the residuals are not normally distributed. I have tried log transformation and Box-Cox, but nothing worked. Any suggestion?
- 13
1
vote
0
answers
26
views
Distribution-free prediction intervals in linear regression
I've found some literature on the subject, but it is rather difficult to read. I am wondering if the following simplified method makes sense. My question is what part is correct in this methodology, ...
0
votes
0
answers
14
views
Should I use a heckman model?
I'm currently working on my master's thesis. For my research, I have to measure the impact of two independent variables (amount of infographics and amount of other images) on a dependent variable (...
- 1
0
votes
1
answer
28
views
Linear regression has good performance in validation set despite not meeting the linearity assumption
I have a dataset with about 8000 samples and 18 predictors (16 continuous, 2 categorical). I am trying fit a linear regression, but despite trying multiple transformations, I can't make it meet the ...
- 33
1
vote
2
answers
34
views
Identifying confounders in multiple linear regression
I am currently trying to identify confounders in a multiple linear regression, but I am a little unsure of a couple of steps. These are the steps I am taking:
Check to see if the potential ...
- 11
0
votes
0
answers
13
views
Constructing a time dummy variable in DiD fixed effects model
I am using a Diff-in-Diff regression design to evaluate the impact of a county-level tax hike (i.e. treatment variable) on tobacco sales (i.e. outcome variable) in a given county, relative to counties ...
- 105
0
votes
1
answer
23
views
Difference In Differences with Daily Numbers
I ran a DID regression and found my estimate on the DID coefficient to be .022. The units of time I am using are days, and at a certain day around halfway through my data, the treatment group was ...
- 23
0
votes
0
answers
17
views
(Undergrad looking for help) In logarithmic regression (log-log), what does it mean if your explanatory variable is already a percentage?
So I'm hoping to a regression of Human Development Index against some economic variables I think could affect it. Some types of aid per capita, education spending by government as a percentage of gdp, ...
0
votes
0
answers
14
views
Interpretation multiple linear regression with cumulative coding for ordinal variable
In order to do a multiple linear regression with categorical variable, I transformed them with the cumulative coding :
My problem is in the interpretation of the results of the regression : from what ...
- 13
0
votes
0
answers
29
views
Confused about pooled statistics from multiple linear regression on multiply imputed data in R
I have a dataset with 75 cases. I did multiple imputation of missing data in SPSS. I am running a multiple linear regression on the data. I have 7 binary independent variables, one continuous ...
- 11
-1
votes
0
answers
11
views
Power with a hierarchical multiple regression analysis [duplicate]
I was wondering how to calculate the power with the below data? I did a hierarchical multiple regression with two models. There are a total of 87 participants and the alpha level is 0.05.
-1
votes
0
answers
11
views
How to calculate your power in GPower? [duplicate]
I was wondering how you calculated the power via Gpower with these results? There are a total of 87 participants and the alpha level is 0.05.
2
votes
1
answer
168
views
Logistic Regression on multiple classes (Shouldn't it be only on binary?)
I'm a bit confused with the usage of logistic regression for multi-class classification. My understanding is that a logistic regression is dichotomous (two possible classes), so in the example of the ...
- 123
0
votes
0
answers
20
views
build a linear regressor with labels in different scales
I just ran into this linear regression problem where the labels are in entirely different range for example for 25% of the samples, the labels are in [0.001,0.01], then for another 25 % of the samples,...
- 1,586
0
votes
0
answers
49
views
How to treat dummy variables and their interactions terms with an endogenous variable in a IV context?
I have the following regression function:
# fictitious regression function
Y = α1 + α2*X + α3*W + α4*D + α5*(D*X)
Y is the dependent variable, X is the main ...
-2
votes
0
answers
12
views
linear regression with variables as linear combination [duplicate]
Consider a linear regression model with two variables x1 and x2. Suppose, I fit a new model with two new variables x1+x2 and x1-x2.
Are these two models equivalent?
What is the relationship between ...
- 17
0
votes
0
answers
12
views
How to structure multiple linear regression model given dependent variables with multiple timepoints?
I'm working with a dataset that has multiple independent variables (e.g. height, sex, zip code, etc.) as well as dependent variables with multiple timepoints (i.e. test A results reported at 1wk, 2wk ...
- 1
1
vote
0
answers
20
views
Help me find (ok, calculate/approx/uncover) the p-value in the following tables
For a meta-analysis I am looking for the p-values in these tables derived from reports dating back to 1980...
I am, by now, pretty sure I can't but maybe there is someone smarter than me here... If ...
- 111
0
votes
0
answers
18
views
Relationship between regression coefficients in regression vs. reverse regression (with controls)
Question: How do the coefficients in a multiple linear regression (Regress y on x and controls Z) relate to the coefficients in the reverse regression (Regress x on y and controls Z)?
Simple Linear ...
1
vote
1
answer
23
views
In a multioutput deep learning model, is there a benefit to normalizing the output dimensions if they are of different magnitudes?
I am building a multi output deep learning model where the output consists of five dimensions (the specific architecture is a modification of YOLO). These have different magnitudes (ranges: [0, 1.2], [...
- 13
1
vote
1
answer
50
views
Dealing with multiple regression [closed]
details of my dataset
summary(lm(visits ~ health1 + age, data = Medicaid1986))
But it gives this output:
health1:The first principal component (divided by 1000) ...
- 11
3
votes
0
answers
49
views
A faster way of finding unbiased estimators for this linear model
No access to computers or calculators is available for this problem.
Consider the following linear model
$$Y_1 =\theta_1 + \theta_2 + \theta_3 + \theta_4 + \theta_5 + \theta_6 + \epsilon_1\\
Y_2 =\...
- 155
1
vote
0
answers
20
views
Time series regression with multiple independent variables
it's been a while since i studied statistics and im a bit rusty on the topic but im looking for some advice on times series regression.
I'm trying to generate a time series regression model where i ...
- 11