Linked Questions

301
votes
10answers
290k views

Difference between logit and probit models

What is the difference between Logit and Probit model? I'm more interested here in knowing when to use logistic regression, and when to use Probit. If there is any literature which defines it using ...
92
votes
1answer
74k views

Interpreting plot.lm()

I had a question about interpreting the graphs generated by plot(lm) in R. I was wondering if you guys could tell me how to interpret the scale-location and leverage-residual plots? Any comments ...
51
votes
4answers
29k views

How are regression, the t-test, and the ANOVA all versions of the general linear model?

How are they all versions of the same basic statistical method?
30
votes
2answers
43k views

Interpretation of plot (glm.model)

Can anyone tell me how to interpret the 'residuals vs fitted', 'normal q-q', 'scale-location', and 'residuals vs leverage' plots? I am fitting a binomial GLM, saving it and then plotting it.
21
votes
2answers
52k views

How do you find weights for weighted least squares regression?

I am a bit lost in the process of WLS regression. I have been given dataset and my task is to test whether there is heteroscedascity, and if so I should run WLS regression. I have carried out the ...
21
votes
1answer
28k views

Why Levene test of equality of variances rather than F ratio?

SPSS uses the Levene test to evaluate homogeneity of variances in the independent group t-test procedure. Why is the Levene test better than a simple F ratio of the ratio of the variances of the ...
11
votes
2answers
21k 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 ...
16
votes
1answer
2k views

Where do the assumptions for linear regression come from? [duplicate]

I'v already known that there are several assumpations when using linear regression model. But I cannot understand why some of them exists. They are: independent errors normal distribution of errors ...
9
votes
2answers
7k views

Simulate linear regression with heteroscedasticity

I am trying to simulate a dataset that matches empirical data that I have, but am unsure how to estimate the errors in the original data. The empirical data includes heteroscedasticity, but I am not ...
-4
votes
2answers
19k views

What are key differences between homoscedasticity and homogeneity?

Homoscedasticity and homogeneity of effect-sizes assumptions are popular with regression analysis and Anova respectively.These assumptions create lot of confusion at least in my mind. I am not clear ...
4
votes
1answer
10k views

Homoscedastic and heteroscedastic data and regression models

How to understand the homoscedasticity and heteroscedasticity in context of regression models? Is there a way to check these properties in R?
3
votes
2answers
3k views

Linear regression with changing variance

I want to perform linear regression on some data. For every value of x, the data values are distributed normally across y, around some mean. However, the variance increases linearly as x increases. I ...
0
votes
1answer
15k views

Testing homoscedasticity for multiple regression in SPSS

I am conducting a multiple regression with 1 DV and 6 IVs. I am trying to test Homoscedasticity on SPSS using a scatterplot since all my variables are scales. I conducted a the residual vs predictor ...
4
votes
2answers
2k views

Efficiency of beta estimates with heteroscedasticity

I need something clarified and that is when you have non-constant variance, estimates won't be biased but will be a problem when it comes to the S.E. formulas and efficiency. Therefore OLS estimates ...
5
votes
1answer
6k views

Why do we say that the variance of the error terms is constant?

I always think about the error term in a linear regression model as a random variable, with some distribution and a variance. So if the error terms come from this random variable, why do we say that ...

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