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Results tagged with regression
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user 78807
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
2
votes
What violates the assumptions of regression analysis?
Consider the regression equation $\hat y = \beta_0 + \beta_1x_1 + \beta_2x_2 + ... + \beta_px_p + \epsilon_i$ and below are the common OLS regression assumptions:
Linearity: relationship between dependent …
- 1,085
0
votes
0
answers
200
views
pooled, fixed or random effects
Which of the regression models - pooled, fixed effects or random effects - should be used here? According to vignette (page 2), it seems pooled model should be used in this case. …
- 1,085
5
votes
Exponential Regression vs Exponential smoothing
In linear regression, we try to find $y = b + mx$ that fits best data. So, exponential regression is non-linear. … In short, to predict future, you use past predictions and actual data for exponential smoothing whereas you use only past data for regression. …
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0
votes
Performance of Logistic Regression with time
Every time you conduct the analysis, you could use the latest obtained data points, for instance, data collected in the last 180 days. I suppose the applicants' data is stored and updated on database …
- 1,085
1
vote
Setting up a mediation model
Path A and Path B are fine. Below are all the steps to test mediation effect:
mdlA <- lm(exercise ~ sleep, data=data)
mdlB <- lm(score ~ exercise, data=data)
mdlC <- lm(exercise ~ sleep, data=data)
m …
- 1,085
4
votes
What is the best book about generalized linear models for novices?
As a complete beginner myself, I found Foundations of Linear and Generalized Linear Models by the celebrated author of Categorical Data Analysis Alan Agresti to be helpful. Language is fluid, though s …
4
votes
2
answers
17k
views
Lagged independent variables in economic analysis
I am trying to study the effects of foreign direct investment (FDI) in growth of gross domestic product (GDP). It's considered that FDI positively impacts GDP growth and it makes sense to assume that …
- 1,085
0
votes
How do I remove outliers in dataset?
Instead of analyzing and omitting outliers, an easy approach to deal with extreme values is to use the rlm() function available in MASSpackage. rlm() fit a linear model by robust regression. lm() have …
- 1,085
4
votes
What is the difference in how $\mathrm{R}^2$ and $\mathrm{R}$ values are interpreted?
Notice that the $r$ value is does not tell anything about the slope of the regression line. …
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2
votes
Standard error of residuals and standard error of regression
Consider the regression equation $\hat y_i = \beta_0 + \beta_1x_i + \epsilon_i$ and to compute standardized residuals,
Find the mean of residual, $\bar \epsilon = \frac{\sum_{i=1}^{n} \epsilon_i}{n}$ …
- 1,085
2
votes
Confidence Interval for Regression Line (simple linear regression)
It tells about the population parameter (as opposed to sample statistic) which in your case is regression coefficient of the entire population. … A confidence interval is a range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter such as mean and regression coefficient. …
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