Independent Variables Puns

Hi everyone!

I am studying the scientific method in my research class. I have been tasked with determining the Independent Variable and it's factors from the following information:

Oranges: California and Florida

It should be noted that the independent variable is latent (not clearly stated). I personally am completely baffled. Any ideas?

Hi all, I'm seeking your advice. I am performing a glmm where my response variable is binary and my 5 independent variables are continuous. The independent variables are scaled and centered. Two of the 5 independent variables are right skewed. The other 3 are normally distributed. I am running the glmm with all 5 of these covariates (both fixed and random effects for each). A couple questions:

1. the estimates for these 2 skewed variables do not seem correct because they are ~15x as large as the variables that are not skewed. So my thought is that the heavy skew is causing this. Is this a correct conclusion? If so, how do I transform these variables?

2. does the "family" argument in R refer to the independent or dependent variable? I thought dependent, but is this an avenue for transforming the skewed variables?

Maybe I'm down the wrong rabbit hole on this. Any advice would be great! Thanks!

Context: In a magical mystery land, there are a list of 20 laws that fairies can apply whenever they encounter a law breaking incident. Each time they enforce the law they can apply multiple laws if applicable, which can lead to a few outcomes (eg., get fined/not get fined, get on record/not get on record, banishment/no banishment etc). Since there are 20 laws, the deity of the land would like to use metrics to inform them what laws are redundant and how to eliminate or combine different laws.

Specific Problem: Would like to examine the relationship between: (IV) whether or not a law co-occurs with another law and the degree to which co-occurrences lead to, specifically, (DV) banishment or no banishment (interested in that one outcome). Since multiple laws can be applied at the same time (not-mutually exclusive), I'm assuming that a logistical regression may not be applicable in this scenario despite the outcome variable being binary. Was wondering it there are alternative models that can examine this relationship?

Thank you thank you!

EDIT: I've taken university level statistics courses as a background and have applied things like ANOVAs and basic regression models, so only have some understanding of basic concepts

Hello guys. I have to finish my Extended Essay from Physics very soon and I am panicking. I have conducted my experiment and I have 4 independent variables (the ones that I am changing). After looking at example essays I am thinking that it might be too much. If I reduce it to 3 would it be okay?

Hi everyone,

I’m wanting to test if how many football games someone attends per season (the categorical independent variable) has an impact on the answer they give to a yes/no/I’m not sure question but I’m struggling to find the right test to run - please help!

Hello guys, i need to run a linear model where the independent variable is a stock (ETF precisely) price during a period of 6 months, daily. I need to put all the variables that, according to economics theory, are revelant to explain movement of stock prices. Could you suggest me what are these?

Thank you so much

SPSS module was taught in 2 days 5 months ago which really didn't help much and i'm really confused so help a sis out. THANK YOU T.T

1. there is a relationship between work-life balance and job satisfaction

2. Work-life balance and job satisfaction decreases with work experience

3. There are differences of work-life balance and job satisfaction between the public and private x industry

The independent variable falls in an ordinal scale with percents. 1=30% 2=30 to 60% 3=69% or higher and the dependent variable is a yes or no nominal scale

D is the treatment variable (veteran status)

Z is the instrument (draft eligibility, assigned with a random lottery)

Y is the outcome (earnings)

There are heterogeneous effects (not all drafted become veterans, some non-drafted become veterans, some always become veterans regardless of draft eligibility and some never become veteran)

What does it mean that the instrument is randomly assigned with respect to the potential outcomes and **the treatment** ?? (formally: [see slide 7, assumption 1](https://econ.lse.ac.uk/staff/spischke/ec533/The%20LATE%20theorem.pdf) . Doesn't this violate the First Stage assumption ( Cov(Z,D) different from 0 ) ??? Thanks!

My professor and text find this to be a statement that is false . I, on the other hand think it to be true . Can someone explain how it’s a false statement.

I was hoping someone could point me in the right direction on what non parametric test I should use.

I have a dependent variable on attitudes towards the economy/environment. It's on a scale of 0 (Prioritise the enconomy) - 10 (Prioritise the environment). I assumed this was an interval variable, but I'm not sure if this variable uses the Likert scale - which would make it ordinal.

I want to do a non parametric test using the dependent variable and a categorical independent variable, but as I'm not sure whether or not the DV is ordinal or interval, I can't figure out which test is appropriate. I only know that I can't use one way ANOVA or Kruskal–Wallis test, as DV and IV don't have the same distribution shape.

I’m about to calculate the expectation of Z = X(t)exp(X(t)) where X(t) is a brownish motion with drift.

But how do I calculate an expectation of a product of 2 random variables if they are not independent?

I have often heard people use different terms for Dependent variable and Independent Variables in context of Regression or Time series forecasting such as:

Dependent variable : Regressand, Predicted, Endogenous, target, response

Independent variable: predictor, covariate, regressor, exogenous, features

I was wondering under what contexts should one use different terms for dependent and independent variables? Is their any etymological or pedantic reasons for why we have so many terms describing DV and IDV ?

I'm trying to see the influence of some factors (ex. peer influence, high salary) to the career choice of my respondents.

I'm not sure if the factors are categorical or not since I'm also thinking whether to let them rank the factors from 1-10 or not. I think ANOVA can be used but I'm not so sure since what I'm finding is relationship.

Please let me know if there's an actual test for this or should I just change my direction?

In my experiment subjects (of both sexes) performed a task twice under two treatment conditions. The order of the two treatment sessions is counterbalanced in my sample. The task consists of pushing buttons in response to visual stimuli, each stimulus-response pair is a trial. The inter-trial-interval is not precisely constant, but it is near-constant. During the experiment an event occurs and the response variable y was recorded in the four trials after each event. It appears that there is a gradual post-event increase in y but only during treatment 1, see data chart. I am primarily interesting in testing this hypothesis and secondarily interested if subjects' sex has an effect.

I set a mixed effects GLM in Matlab GLM = fitglme(tbl,'y ~ sex + treatment*trialNo + (trialNo|treatment) + (treatment|subject)') with subject sex as a fixed factor and treatment being a random factor nested in subject (i.e. repeated measures) and trialNo similarly nested in treatment. This returned:
Fixed effects coefficients (95% CIs):

Which I interpret as indicating that there are no overall effects of trial # (i.e. longitudinal effect) but a marginally significant interaction between trial # and treatment. As well as a marginal effect of sex (not shown in figure).

Is fitting mixed-effects generalized linear model a valid strategy for testing the stated hypothesis? If so, is my Wilkinson notation correct for the mixed-effect repeated measures design of my data? Are there better ways to test the hypothesis, perhaps that more fully capture the ordinality of the trial# grouping variable? Thank you for your help.

I have survey data using which I am trying to run a regression. I am trying to study the impact of race, education level, and income level on the tendency to experience job loss.

Dependent Variable:

• Job Loss: can have either 'Yes' or 'No' as values

Independent Variables:

• Race: can have either of the 4 races from Black, Asian/Indian, American Indian, White. These are not d ordered categories
• Education Level: can have either of 'High School or Below', 'Some College' and 'College and Beyond'. These are ordered categories
• Income Level: can have either of 'Low', 'Medium' or 'High'. These are ordered categories

What would be the best model to use in this case? I am thinking about a logit or a probit model. Would these models sufficiently answer my original research question pertaining to the effect on the tendency to experience job loss?

Any suggestion or direction to appropriate resources would be appreciated.

Let’s say a continuous variable M (scale 1 to 500) is how the dependent variable X is defined. That is: if M is =/<50 then variable X = 1 and if M >50 then X = 0.

I am running a (multivariable) binary logistic regression to figure out the associations between potential predictors and the increase/decrease % of X happening (i.e., X = 1).

I understand that it is self-explanatory that if M is lower, then X is more likely to happen (will happen). And vice versa if M is higher. But I would still like to include M in the model because I want to report the % decrease/increase of X happening if someone has M = 80 rather than M = 350. Is this stupid? Redundant? Inappropriate?

I understand that there a several assumptions that have to be met before you can perform a binary logistic regression. I know that correlation between continuous independent variables (multicollinearity) can be a problem, but in this case the independent variable and the dependent variable are strongly associated and not two or more independent variables. I could not find any information on this particular scenario anywhere else, hence, the question.

Say I have one independent variable X1 that I suspect has a value on the absolute value of Y. All of the other Xs are more 'important' in determining Y's direction, but the higher X1 is, the more positive or more negative Y is.

Surely regressing Y on X1 and the other Xs isn't appropriate.

I'm also not really interested in regressing |Y| on all Xs because I am interested in how the other Xs determine the direction of Y.

How would I deal with this? I imagine X1 is an important control variable so I wouldn't want to omit it.

I think it’s right but it’s just easier to memorize for me

What is the term in math when you are trying to determine if there is enough data to solve a problem?

As in you need to solve for X (in real numbers), in X=B+C/D But you only have C and B

I can't remember if this is part of dimensionless analysis.

Hello everyone,

I am planning on doing several ordinal logistic regressions on my Likert-scale dataset concerning assessments of different aspects of digitalization.

Which problems may I generally run into when im using too many independent variables to explain a dependent variable? Can this lead to „overfitting“ and how can I find out how many/which variables make sense to use together?

Hi all!

I have correlated two independent variables, displayed value of 0.9. (High correlation)

Running a VIF on the auxiliary regression with the same independent variables yield a VIF = 1.

Auxiliary regression R^2 = 0.8

Why is this occurring? Is there a possible fix?

(No calculations - this is done using Stata)

Helping little sis with some homework and need some confirmation(or correction)

So question is how many independent variables are there in this study

Study is roughly Participants perform an activity, their performance is rated out of ten. In the

first group there is a low level of pressure applied, as such they are performing the tasks alone, with nobody watching.

Group two has moderate amount of pressure applied and as such they have a crowd around them but they are silent

Group 3 has high level of pressure applied and as such there is a crowd who are all shouting at them

Is the IV the level of pressure applied or is there two IVs, one being if there is a crowd or not and one being if they are shouting or not?

Many thanks in advance

Basically I am looking at qualitative data, a number of psychological assessments for a group of people.

Unfortunately the data is not particularly well structured but it has the headings for each category. So for example "anxiety issues" heading and underneath there's an outline of client complaints relating to anxiety.

Then there's the sub tab of "suggested intervention" where a professional writes out a recommended course of action for the individual.

So far I am familiar with some of the standard NLP procecessed. I could prep input (and output?) data using tools such as CountVectoriser to get the frequency matrix of terms. I'm not really sure where to go from there though?

I've not yet attempted any problems with multiple dependent variables, and I'm not sure if this is the correct path to go down even.

Has anyone here dealt with a similar problem and could advice on ideas etc.

Edit: Did people seriously down vote this? For what possible reason?