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Results tagged with statistical-significance
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user 1329
Statistical significance is a characteristic of a statistic viewed in light of a null hypothesis and a given significance level. It reflects whether the statistic belongs to the rejection region (is statistically significant) or the acceptance region (is not statistically significant).
6
votes
Is it ever good to increase significance level?
On the one hand it is somewhat artificial to discount a variable because its p value is higher than 0.01 (that's an unusually stringent criterion). How you get there may be more important than what i …
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2
votes
R2, P-value, or accuracy of model?
What is more important is neither R Square nor accuracy as you measure it, but that your model is structured correctly. As Frank pointed out when you transform variables, models are not comparable. …
- 7,882
12
votes
Statistical test for a value being significantly further from the population mean: is it a Z...
You raise an interesting question. First thing first, if you have an observation of 0.35, a mean of 0.25, and a standard deviation of 1/10^7 (that's how I interpret your e^-7 bit) you really don't ne …
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1
vote
What are the differences between parametric and non-parametric statistical tests?
Parametric most often means that a test assumes the tested variables are Normally distributed. Such tests often use regular nominal values, and look at differences in Averages. Non-parametric tests …
- 7,882
1
vote
Significance of the slope of a straight line fit
I would simply use the standard regression output to evaluate the significance of the slope coefficient. I mean by that looking at the coefficient itself, its standard error, t stat (# of standard er …
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2
votes
3
answers
4k
views
How to build a regression model with just 5 datapoints with 5 or more variables?
I am attempting to build a simple multiple regression in order to estimate the value of a home. The 5 datapoints are 5 comparable home sales (similar in size, lot, location, # of bedrooms, etc...).
…
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1
vote
Choosing the Significance Level in Multiple Regression
I would advance that statistical level thresholds are ultimately really arbitrary and driven more by the requirements of the relevant Journal publishers, peer-reviewers, model validators. Thus, in ac …
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2
votes
1
answer
3k
views
Does heteroskedasticity matter if you have a large enough sample?
Let's say you run a regression with over 200 observations. Would this reasonably large sample mitigate the impact of residuals heteroskedasticity as an offshoot of the Central Limit Theorem, or somet …
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1
vote
0
answers
664
views
Can a Linear-Log model be used instead of Robust Standard Errors?
If your regression model has heteroskedastic residuals, one should calculate White Standard Errors that correct for the mentioned heteroskedasticity. If the residuals are also autocorrelated one shou …
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2
votes
1
answer
83
views
What to do if recombination of independent variables cause multicollinearity issue?
Let's say you use a regression that has either: 1) interaction variables or 2) polynomials. When using those features you may run into multicollinearity issues. Do you know how to resolve this issue …
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0
votes
1
answer
191
views
What is the meaning of Confidence Intervals regarding Hold Out samples?
Let's say you have a regression model that estimates GDP. Your model has a Standard Error of 1%. So, you can readily build Confidence Intervals around your regressed estimates. Your 95% CI will be …
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1
vote
Form hypothesis and test it using given data
Let me suggest a different approach that is simpler and relies on fewer assumptions than Heitz Chi Square approach.
Going back to your data set, you essentially have a time series of annual death pen …
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4
votes
Accepted
When to create a control group with paired T test
I think what you are stating is directionally right. But, to clarify you can conduct a paired t test with your one single group and measure how they fare before and after the treatment. You can also …
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31
votes
Are large data sets inappropriate for hypothesis testing?
Hypothesis testing traditionally focused on p values to derive statistical significance when alpha is less than 0.05 has a major weakness. And, that is that with a large enough sample size any experi …
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