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Hi,

Is there a way to run a regression by setting an if condition on only a few variables in the regression, but not others?

submitted by Top-Jaguar-4412 to stata [link] [comments]
Is there a way to run a regression by setting an if condition on only a few variables in the regression, but not others?

I am interested in how Stata calculates regression coefficients when you use the ‘reg’ command.

I am assuming they don’t just do:

b = (X’X)^{[-1](X’y)}

as I have heard this is numerically unstable.

Could anyone point me to documentation describing their algorithm?

submitted by sadfasn to stata [link] [comments]
I am assuming they don’t just do:

b = (X’X)

as I have heard this is numerically unstable.

Could anyone point me to documentation describing their algorithm?

Hello! I am not sure if this is the right sub to post this but please feel free to delete if it's not..

Is it possible to run regressions on Stata using a dataset located in an external SSD? If yes, what are the pros and cons? Sorry I have not tried this yet and I'm yet to purchase an external SSD if this is possible..

Thanks in advance!

submitted by Electronic_Jalapeno to stata [link] [comments]
Is it possible to run regressions on Stata using a dataset located in an external SSD? If yes, what are the pros and cons? Sorry I have not tried this yet and I'm yet to purchase an external SSD if this is possible..

Thanks in advance!

I'm trying to do a regression kink analysis for a dataset with Stata. I'm not very familiar with Stata but I've been watching tutorials and I got a template for the RKD from the Worldbank website. I've attached pictures of the .do file and my data. Can anyone explain how i can adapt the template to my dataset?

Program to get RKD estimates

Dataset

submitted by Doctor_When_ to stata [link] [comments]
Program to get RKD estimates

Dataset

Hello, I wanted to use Poisson regression and see if the level of education and work status have an effect on how many emails an individual sends per week. The dataset I have has a variable "usemail" answering the question with yes or no. Another variable "emailhrs" hours of e-mail per week for internet users. Can I use either of these as dependent variables? I'm not sure if they qualify as counts

submitted by Adventurous_Jaguar33 to stata [link] [comments]
submitted by whyamianoob to AskStatistics [link] [comments]

My main issue is that my regression results do not match my graphical results, even when I use companion commands as rdrobust and rdplot ( https://sites.google.com/site/rdpackages/rdrobust ).

Do you have any suggestion in that sense? Code snippets or else is very much welcomed!

submitted by EconGuru93 to stata [link] [comments]
Do you have any suggestion in that sense? Code snippets or else is very much welcomed!

Hi reddit stata,

I recently wanted to simulate data for logistic regression. Any suggestions are welcome. I want to note this experience down so that it may help myself and others in future. I tried to generate a dataset for a hypothetical disease affected by BMI and mean arterial pressure. I also wanted a categorical variable (socio-economic status) to have some effect.

Starting the process, I used corr2data to generate a dataset of independent variables autocorrelated to each other (as found in nature).

I used the following commands

matrix input mean = (170 70 120 80)

matrix input sd = (7 8 8 5.6)

matrix input corr = ( 1 0.7 0.2 0.2\0.7 1 0.2 0.2\0.2 0.2 1 0.4\ 0.2 0.2 0.4 1)

corr2data ht wt sbp dbp, n(100000) mean(mean) sd(sd) corr(corr)

gen bmi = wt/(ht/100)^2 \\ generate body mass index

gen mbp = (2*dbp + sbp)/3 \\ generate mean arterial pressure

gen ses = runiformint(1,5) \\generate socio-economic status

tab ses, gen(ses_cat) \\create dummy variables for ses

gen hypothetical_disease = uniform() < invlogit(ln(2.5)*(bmi-24) + ln(1.5)*(mbp-94) + ln(1)*ses_cat1 + ln(0.97)*ses_cat2 + ln(0.96)*ses_cat3 + ln(0.95)*ses_cat4 + ln(0.94)*ses_cat5 + - 3.5 + rnormal(0,1))

logistic hypothetical_disease bmi mbp i.ses

You can play with the constant (in this case -3.5) and random error(in this case, rnormal(0,1)). For each independent variable (eg. BMI), I used the term (IV - mean(IV)).

For example, the first term inside the brackets of invlogit is ln(2.5)*(bmi-24). This is because I wanted the odds ratio of BMI to be 2.5. instead of bmi, I used bmi - 24 because the mean of bmi was close to 24.

Any suggestions for improving this simulation will be helpful.

submitted by luxatioerecta to stata [link] [comments]
I recently wanted to simulate data for logistic regression. Any suggestions are welcome. I want to note this experience down so that it may help myself and others in future. I tried to generate a dataset for a hypothetical disease affected by BMI and mean arterial pressure. I also wanted a categorical variable (socio-economic status) to have some effect.

Starting the process, I used corr2data to generate a dataset of independent variables autocorrelated to each other (as found in nature).

I used the following commands

matrix input mean = (170 70 120 80)

matrix input sd = (7 8 8 5.6)

matrix input corr = ( 1 0.7 0.2 0.2\0.7 1 0.2 0.2\0.2 0.2 1 0.4\ 0.2 0.2 0.4 1)

corr2data ht wt sbp dbp, n(100000) mean(mean) sd(sd) corr(corr)

gen bmi = wt/(ht/100)^2 \\ generate body mass index

gen mbp = (2*dbp + sbp)/3 \\ generate mean arterial pressure

gen ses = runiformint(1,5) \\generate socio-economic status

tab ses, gen(ses_cat) \\create dummy variables for ses

gen hypothetical_disease = uniform() < invlogit(ln(2.5)*(bmi-24) + ln(1.5)*(mbp-94) + ln(1)*ses_cat1 + ln(0.97)*ses_cat2 + ln(0.96)*ses_cat3 + ln(0.95)*ses_cat4 + ln(0.94)*ses_cat5 + - 3.5 + rnormal(0,1))

logistic hypothetical_disease bmi mbp i.ses

You can play with the constant (in this case -3.5) and random error(in this case, rnormal(0,1)). For each independent variable (eg. BMI), I used the term (IV - mean(IV)).

For example, the first term inside the brackets of invlogit is ln(2.5)*(bmi-24). This is because I wanted the odds ratio of BMI to be 2.5. instead of bmi, I used bmi - 24 because the mean of bmi was close to 24.

Any suggestions for improving this simulation will be helpful.

Assuming it's possible...

submitted by Caterina1124 to stata [link] [comments]
Hey guys,

I've been told to choose one regression over the other and I'm pretty sure I need to use the BIC and AIC function to do this. I was just wandering how I would go about doing this on Stata?

Thanks guys!

submitted by sammy7918 to stata [link] [comments]
I've been told to choose one regression over the other and I'm pretty sure I need to use the BIC and AIC function to do this. I was just wandering how I would go about doing this on Stata?

Thanks guys!

I am working on my thesis. I am using the Random Effects Regression method in Stata. In this project, there are multiple independent variables. I also have to insert some control variables in the program but I am unsure how to do that. Could anyone please explain how to do that?

Thank you in advance.

submitted by agni1694 to stata [link] [comments]
Thank you in advance.

submitted by Timely-Drawing-5084 to stata [link] [comments]

I need to find out if the expansion of the affordable care act and the subsequent utilization from the expansion impacted employment in the healthcare sector. I have a variable (health_occ) that includes several healthcare occupations and the percentage of people employed in each occupation. I have variables for whether or not the state expanded medicare (expan), and a variable that breaks down the percentage of states that adopted in a given year (expan_yr, 9999 if state did not adopt), if at all. i also have a variable for all the years (2013-2020). I have several other variables for education, sex (sex), race (race), marital status (mar), education level (edu_lvl) and hours worked per week (hpw). not all of these variables need to be used, however. I have to create regression models to answer the question. I have been given some guidance to create a difference in difference model. my outcome is supposed to be a "total employment" variable that I generate which would describe the number of people employed in each health occupation in each state in each year. I need to create an after change (treatment) variable to measure the after effect of adoption. the problem is, different states adopted in different years, and I am not supposed to run regression for each state. additionally I need either a variable for the control or treatment group which is just the dummy variable that says whether or not the state adopted the expansion. because this is either a 1 or a 0, I am also supposed to include dummy variables for each year and each state. I think the baseline difference in difference model should look something like this where m interaction term is the effect of the adoption the states that adopted

ln total employment healthcare = B0 + AdoptedExpansion(x1) + AfterChange(x2) + (AdoptedExpansion*AfterChange)(x3) + state dummy + year dummy + error term

I am also supposed to show in simple statistics how my argument can be proven so for example doing the sum of the total employment variable when the medicaid expansion is 1 and when it is 0.

While I understand all of this conceptually, I do not know how to code it in stata. How do I code:

-create a total employment healthcare variable that tells me how many people where employed in the healthcare occupation (health_occ) in each state (state) in each (year) -take the natural log of that total employment healthcare variable and make it the y variable for my baseline difference in difference model -create the baseline difference in difference model where the control is the states who didn't adopt at all and the group of interest is the states that did -create an afterchange variable for this model that takes into account before and after the treatment (the time trend), and how would this even work given that different states adopted in didfferent years and I'm not supposed to run a regression for each state -how do I generate a state and year dummy variable and how do I use them in my baseline difference in difference model -how do I incorporate the other variables (sex, race, broad employment industry, years of education, hours worked per week, marital status) to create secondary models after this baseline -what other regressions can I potentially run to help answer this question as best as possible -what would the code for the simple statistic that i mentioned earlier look like. I imagine it would be a regression of the total employment healthcare variable run on the expansion variable and seeing what the outcome is when expansion is one and when expansion is zero, I'm just not sure how to code it.

for the after change variable this is all I could come up with: Gen afterchange = 1 if expan_yr > year but I'm not totally sure how this would work

Any help or guidance would be greatly appreciated

submitted by 1Sentaler to econometrics [link] [comments]
ln total employment healthcare = B0 + AdoptedExpansion(x1) + AfterChange(x2) + (AdoptedExpansion*AfterChange)(x3) + state dummy + year dummy + error term

I am also supposed to show in simple statistics how my argument can be proven so for example doing the sum of the total employment variable when the medicaid expansion is 1 and when it is 0.

While I understand all of this conceptually, I do not know how to code it in stata. How do I code:

-create a total employment healthcare variable that tells me how many people where employed in the healthcare occupation (health_occ) in each state (state) in each (year) -take the natural log of that total employment healthcare variable and make it the y variable for my baseline difference in difference model -create the baseline difference in difference model where the control is the states who didn't adopt at all and the group of interest is the states that did -create an afterchange variable for this model that takes into account before and after the treatment (the time trend), and how would this even work given that different states adopted in didfferent years and I'm not supposed to run a regression for each state -how do I generate a state and year dummy variable and how do I use them in my baseline difference in difference model -how do I incorporate the other variables (sex, race, broad employment industry, years of education, hours worked per week, marital status) to create secondary models after this baseline -what other regressions can I potentially run to help answer this question as best as possible -what would the code for the simple statistic that i mentioned earlier look like. I imagine it would be a regression of the total employment healthcare variable run on the expansion variable and seeing what the outcome is when expansion is one and when expansion is zero, I'm just not sure how to code it.

for the after change variable this is all I could come up with: Gen afterchange = 1 if expan_yr > year but I'm not totally sure how this would work

Any help or guidance would be greatly appreciated

Hello,

This is my first time using Stata for a class assignment. I am a beginner in statistics in general so needless to say I am struggling. Could someone explain to me how I can run a regression using a dataset that has missing values for ten independent variables? Basic command definitions and examples would be appreciated.

We received one lecture on how to use Stata which wasn't very helpful for me because they didn't address what to do when there are missing values.

Thank you in advance!

EDIT: I keep getting an error message: "no observations r(2000);"

submitted by oceanofflavor to stata [link] [comments]
This is my first time using Stata for a class assignment. I am a beginner in statistics in general so needless to say I am struggling. Could someone explain to me how I can run a regression using a dataset that has missing values for ten independent variables? Basic command definitions and examples would be appreciated.

We received one lecture on how to use Stata which wasn't very helpful for me because they didn't address what to do when there are missing values.

Thank you in advance!

EDIT: I keep getting an error message: "no observations r(2000);"

submitted by Awesomemanu to stata [link] [comments]

How do I control for gender differences across logistic regressions in stata? I have been computing everything in a "Do-file" and would like to know if I can control for gender across all of the variables that I run in my Do-File? For example, if my DV is general misconduct and my IVs are age, race, etc. and my command looks like this "logit General_Misconduct v0005 RaceRecode" what can I do to look at how these variables impact misconduct and how it differs for males and females?

I was originally using SPSS for my thesis, but one of my committee members just suggested that I switch to stata and I have never used this before so any help/advice is greatly appreciated!!

submitted by OptimisticThought7 to statistics [link] [comments]
I was originally using SPSS for my thesis, but one of my committee members just suggested that I switch to stata and I have never used this before so any help/advice is greatly appreciated!!

Hello everyone,

let's say I have the following data

what I did

submitted by Zahrawi to rstats [link] [comments]
let's say I have the following data

year <- 1900:2015 age <- 5:15 seq_age <- rep(age, length(year)) seq_year <- sort(rep(year, length(age))) y <- rnorm(length(seq_year), 100, 10) sim_data <- data.frame(year = seq_year, age = seq_age, y = y)I want to run linear regression using age as an independent variable and the variable y as a dependent variable while subsetting for each year. then I want to extract the coefficients and store them in a list for each year.

what I did

regdata <- list() for(i in unique(sim_data$year)){ regressiondata <- sim_data %>% filter(year == i) regdata[[i]] <- lm(data = regressiondata, y ~ age) }I will get a list full of NULL values only, what is the problem?

submitted by neilddd to Cricket [link] [comments] |

submitted by Luck1492 to Colts [link] [comments] |

Good day everyone,

The title says most of it. When you test if two coefficients in linear regressions are equal I use: test b1 = b2. When I use this I get the output of an F-test, but I was wondering which formula is used for this F-test. Hopefully, anyone can help me.

Kind regards

submitted by kwark-beertje to stata [link] [comments]
The title says most of it. When you test if two coefficients in linear regressions are equal I use: test b1 = b2. When I use this I get the output of an F-test, but I was wondering which formula is used for this F-test. Hopefully, anyone can help me.

Kind regards

I’ve been running some regression models using proc glm and have entered school fixed effects into the model (currently without an intercept). The model, from memory looks something like this: Proc glm data; Class vars; Model graduating = race age etc. school_ID / solution noint ; Run;

Is there an easy way to get the average school fixed effect (i.e., the coefficient x the proportion of students represented by that coefficient)? This is equivalent to how Stata would display the intercept. I can write the program to do it by hand, but I just want to make sure there isn’t a simple solution first.

submitted by NearbyConcept to sas [link] [comments]
Is there an easy way to get the average school fixed effect (i.e., the coefficient x the proportion of students represented by that coefficient)? This is equivalent to how Stata would display the intercept. I can write the program to do it by hand, but I just want to make sure there isn’t a simple solution first.

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