Does bootstrapped regression allow for inference?Obtaining and interpreting bootstrapped confidence intervals from hierarchical dataDistribution of reciprocal of regression coefficientNon-technical conditions for validity of nonparametric bootstrap confidence intervalsBootstrapped coefficients for ordinal logistic regression with Rbootstrapped standard error to make inference about difference of meansBootstrapping multivariate multiple variable regressionBootstrapping to measure the significance of the skewnessBootstrapped confidence interval for the difference in means for paired dataAre observations independent in bootstrapped resamples?Bootstrapping Prediction Errors
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Does bootstrapped regression allow for inference?
Obtaining and interpreting bootstrapped confidence intervals from hierarchical dataDistribution of reciprocal of regression coefficientNon-technical conditions for validity of nonparametric bootstrap confidence intervalsBootstrapped coefficients for ordinal logistic regression with Rbootstrapped standard error to make inference about difference of meansBootstrapping multivariate multiple variable regressionBootstrapping to measure the significance of the skewnessBootstrapped confidence interval for the difference in means for paired dataAre observations independent in bootstrapped resamples?Bootstrapping Prediction Errors
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$begingroup$
Assuming a linear model of
$$ y = beta_0+beta_1x+epsilon $$
I can construct a bootstrapped confidence interval for the estimate of $beta_1$ by sampling with replacement from all of the $(x_i, y_i)$ pairs from the data.
Is it possible for me to tell if the value of $beta_1$ is significant, without preforming a t-test the data? Or is the bootstrap used more less for inference but more for trying to find the most accurate predictor in this case?
bootstrap
asked 4 hours ago
VastingVasting
305
$endgroup$
add a comment |
$begingroup$
Assuming a linear model of
$$ y = beta_0+beta_1x+epsilon $$
I can construct a bootstrapped confidence interval for the estimate of $beta_1$ by sampling with replacement from all of the $(x_i, y_i)$ pairs from the data.
Is it possible for me to tell if the value of $beta_1$ is significant, without preforming a t-test the data? Or is the bootstrap used more less for inference but more for trying to find the most accurate predictor in this case?
bootstrap
asked 4 hours ago
VastingVasting
305
$endgroup$
add a comment |
$begingroup$
Assuming a linear model of
$$ y = beta_0+beta_1x+epsilon $$
I can construct a bootstrapped confidence interval for the estimate of $beta_1$ by sampling with replacement from all of the $(x_i, y_i)$ pairs from the data.
Is it possible for me to tell if the value of $beta_1$ is significant, without preforming a t-test the data? Or is the bootstrap used more less for inference but more for trying to find the most accurate predictor in this case?
bootstrap
asked 4 hours ago
VastingVasting
305
$endgroup$
Assuming a linear model of
$$ y = beta_0+beta_1x+epsilon $$
I can construct a bootstrapped confidence interval for the estimate of $beta_1$ by sampling with replacement from all of the $(x_i, y_i)$ pairs from the data.
Is it possible for me to tell if the value of $beta_1$ is significant, without preforming a t-test the data? Or is the bootstrap used more less for inference but more for trying to find the most accurate predictor in this case?
bootstrap
bootstrap
asked 4 hours ago
VastingVasting
305
asked 4 hours ago
VastingVasting
305
asked 4 hours ago
VastingVasting
305
asked 4 hours ago
VastingVasting
305
asked 4 hours ago
VastingVasting
305
305
add a comment |
add a comment |
1 Answer
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$begingroup$
Is it possible for me to tell if the value of $beta_1$ is
significant, without preforming a t-test the data?
Yes. You could use bootstrap to calculate confidence intervals. Another thing that you could do, is you could take many bootstrap samples from your data, then calculate regression parameters for each of the samples and check how often the parameters calculated on bootstrap samples are greater then zero, where the empirical fraction from bootstrap samples would be equivalent of testing that $beta>0$.
answered 4 hours ago
Tim♦Tim
59.9k9132225
$endgroup$
add a comment |
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1 Answer
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$begingroup$
Is it possible for me to tell if the value of $beta_1$ is
significant, without preforming a t-test the data?
Yes. You could use bootstrap to calculate confidence intervals. Another thing that you could do, is you could take many bootstrap samples from your data, then calculate regression parameters for each of the samples and check how often the parameters calculated on bootstrap samples are greater then zero, where the empirical fraction from bootstrap samples would be equivalent of testing that $beta>0$.
answered 4 hours ago
Tim♦Tim
59.9k9132225
$endgroup$
add a comment |
$begingroup$
Is it possible for me to tell if the value of $beta_1$ is
significant, without preforming a t-test the data?
Yes. You could use bootstrap to calculate confidence intervals. Another thing that you could do, is you could take many bootstrap samples from your data, then calculate regression parameters for each of the samples and check how often the parameters calculated on bootstrap samples are greater then zero, where the empirical fraction from bootstrap samples would be equivalent of testing that $beta>0$.
answered 4 hours ago
Tim♦Tim
59.9k9132225
$endgroup$
add a comment |
$begingroup$
Is it possible for me to tell if the value of $beta_1$ is
significant, without preforming a t-test the data?
Yes. You could use bootstrap to calculate confidence intervals. Another thing that you could do, is you could take many bootstrap samples from your data, then calculate regression parameters for each of the samples and check how often the parameters calculated on bootstrap samples are greater then zero, where the empirical fraction from bootstrap samples would be equivalent of testing that $beta>0$.
answered 4 hours ago
Tim♦Tim
59.9k9132225
$endgroup$
Is it possible for me to tell if the value of $beta_1$ is
significant, without preforming a t-test the data?
Yes. You could use bootstrap to calculate confidence intervals. Another thing that you could do, is you could take many bootstrap samples from your data, then calculate regression parameters for each of the samples and check how often the parameters calculated on bootstrap samples are greater then zero, where the empirical fraction from bootstrap samples would be equivalent of testing that $beta>0$.
answered 4 hours ago
Tim♦Tim
59.9k9132225
edited 4 hours ago
answered 4 hours ago
Tim♦Tim
59.9k9132225
answered 4 hours ago
Tim♦Tim
59.9k9132225
answered 4 hours ago
Tim♦Tim
59.9k9132225
59.9k9132225
add a comment |
add a comment |
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