As Superpronker mentioned it really depends on what SPSS is doing with the bootstrap. Including your code and the output would help a great deal. Also the bootstrap is a subject with a vast amount of literature. You could see this by simply looking at the bibliography in my 2007 edition of Bootstrap Methods published by Wiley. So I think you really also need at least a basic tutorial on the bootstrap. Sometimes going to wikipedia helps with this sort of thing.

In regression there are various ways to deal with issues like heteroskedasticity and non-normality. If the F test you refer to is from the OLS solution to linear regression where normality and homoskedasticity is ignored and by non-significance you mean that the F test can't tell you that any of the regression coefficients are different from 0, it may be that you should just ignore it and apply a different approach.

The bootstrap can be one approach to deal with the problem. In regression there are two common bootstrap approaches. One is called bootstrapping residuals and the other is called bootstrapping vectors. You should want to find out which one SPSS is using. There is some literature that says bootstrapping vectors is more robust in the sense that it requires fewer assumptions. The vector is the set of observed values of (Y, X1, X2,..., Xk) where Y is the dependent variable and the Xi are the k predictor variables in your model. From your problem description we do not know if k is 1 or >1. For each i there is associated with Xi a regression parameter bi that is estimated.

The bootstrapping residuals method takes the n residuals where n is your sample size and it samples with replacement from this set of residuals. In the computer program this is done by the Monte Carlo method.

The model is Y=b1 X1 + b2 X2 + bk Xk +e where e is an error term. You initially get n residuals by taking yi - b^1 x1i -b^2 x2i-...-b^k xki to be the ith residual. Here b^i denotes the estimate of the regression parameter bi.

We use the notation yi and xki to represent the ith observed value of the dependent variable and the ith observed value of the kth predictor variable respectively.

As this gets complicated, I suggest you look at a reference on bootstrapping residuals The 1993 Chapman and Hall text by Efron and Tibshirani is one possibility. The end results are bootstrap distributions for each regression parameter and one of several possible bootstrap confidence intervals could be used. Efron's percentile method is the most likely possibility. If the confidence interval does not contain 0 the regression parameter is considered significant.

As @Superpronker mentioned it really depends on what SPSS is doing with the bootstrap. Including your code and the output would help a great deal. Also the bootstrap is a subject with a vast amount of literature. You could see this by simply looking at the bibliography in my 2007 edition of Bootstrap Methods published by Wiley. So I think you really also need at least a basic tutorial on the bootstrap. Sometimes going to Wikipedia helps with this sort of thing.

In regression there are various ways to deal with issues like heteroskedasticity and non-normality. If the F test you refer to is from the OLS solution to linear regression where normality and homoskedasticity is ignored and by non-significance you mean that the F test can't tell you that any of the regression coefficients are different from 0, it may be that you should just ignore it and apply a different approach.

The bootstrap can be one approach to deal with the problem. In regression there are two common bootstrap approaches. One is called bootstrapping residuals and the other is called bootstrapping vectors. You should want to find out which one SPSS is using. There is some literature that says bootstrapping vectors is more robust in the sense that it requires fewer assumptions. The vector is the set of observed values of $(Y, X_1, X_2, \ldots, X_k)$ where $Y$ is the dependent variable and the $X_j$ are the $k$ predictor variables in your model. From your problem description we do not know if $k$ is $1$ or $>1$. For each $j$ there is associated with $X_j$ a regression parameter $b_j$ that is estimated.

The bootstrapping residuals method takes the $n$ residuals, where $n$ is your sample size, and it samples with replacement from this set of residuals. In the computer program this is done by the Monte Carlo method.

The model is $Y=b_1 X_1 + b_2 X_2 + \ldots + b_k X_k +e$ where $e$ is an error term. You initially get n residuals by taking $y_i - \hat{b}_1 x_{1i} - \hat{b}_2 x_{2i}- \ldots -\hat{b}_k x_{ki}$ to be the $i$th residual. Here $\hat{b}_j$ denotes the estimate of the regression parameter $b_j$. We use the notation $y_i$ and $x_{ji}$ to represent the $i$th observed value of the dependent variable and the $i$th observed value of the $j$th predictor variable respectively.

As this gets complicated, I suggest you look at a reference on bootstrapping residuals The 1993 Chapman and Hall text by Efron and Tibshirani is one possibility. The end results are bootstrap distributions for each regression parameter and one of several possible bootstrap confidence intervals could be used. Efron's percentile method is the most likely possibility. If the confidence interval does not contain 0 the regression parameter is considered significant.

As Superpronker mentioned it really depends on what SPSS is doing with the bootstrap. Including your code and the output would help a great deal. Also the bootstrap is a subject with a vast amount of literature. You could see this by simply looking at the bibliography in my 2007 edition of Bootstrap Methods published by Wiley. So I think you really also need at least a basic tutorial on the bootstrap. Sometimes going to wikipedia helps with this sort of thing.

In regression there are various ways to deal with issues like heteroskedasticity and non-normality. If the F test you refer to is from the OLS solution to linear regression where normality and homoskedasticity is ignored and by non-significance you mean that the F test can't tell you that any of the regression coefficients are different from 0, it may be that you should just ignore it and apply a different approach.

The bootstrap can be one approach to deal with the problem. In regression there are two common bootstrap approaches. One is called bootstrapping residuals and the other is called bootstrapping vectors. You should want to find out which one SPSS is using. There is some literature that says bootstrapping vectors is more robust in the sense that it requires fewer assumptions. The vector is the set of observed values of (Y,X1,X2,...,Xk) where Y is the dependent variable and the Xi are the k predictor variables in your model. From your problem description we do not know if k is 1 or >1. For each i there is associated with Xi a regression parameter bi that is estimated and the F test for usefulness of the model seeks to determine whether any of the bi are statistically different from 0.

The bootstrapping residuals method takes the n residuals where n is your sample size and it samples with replacement from this set of residuals. In the computer program this is done by the Monte Carlo method.

The model is Y=b1 X1 + b2 X2 + bk Xk +e where e is an error term. You initially get n residuals by taking yi - b^1 x1i -b^2 x2i-...-b^k xki to be the ith residual. Here b^i denotes the estimate of the regression parameter bi.

We use the notation yi and xki to represent the ith observed value of the dependent variable and the ith observed value of the kth predictor variable respectively.

There is then a cycle where the bootstrap residuals are used to get new bootstrap estimates of the bi. The process is repeated and a bootstrap distribution of for each bi is generated. Then bootstrap confidence intervals for the bis are generated (several methods to do this Efron's percentile method is most likely). If the confidence interval for bi doesn't contain 0 it is considered significant.

As Superpronker mentioned it really depends on what SPSS is doing with the bootstrap. Including your code and the output would help a great deal. Also the bootstrap is a subject with a vast amount of literature. You could see this by simply looking at the bibliography in my 2007 edition of Bootstrap Methods published by Wiley. So I think you really also need at least a basic tutorial on the bootstrap. Sometimes going to wikipedia helps with this sort of thing.

In regression there are various ways to deal with issues like heteroskedasticity and non-normality. If the F test you refer to is from the OLS solution to linear regression where normality and homoskedasticity is ignored and by non-significance you mean that the F test can't tell you that any of the regression coefficients are different from 0, it may be that you should just ignore it and apply a different approach.

The bootstrap can be one approach to deal with the problem. In regression there are two common bootstrap approaches. One is called bootstrapping residuals and the other is called bootstrapping vectors. You should want to find out which one SPSS is using. There is some literature that says bootstrapping vectors is more robust in the sense that it requires fewer assumptions. The vector is the set of observed values of (Y, X1, X2,..., Xk) where Y is the dependent variable and the Xi are the k predictor variables in your model. From your problem description we do not know if k is 1 or >1. For each i there is associated with Xi a regression parameter bi that is estimated.

The bootstrapping residuals method takes the n residuals where n is your sample size and it samples with replacement from this set of residuals. In the computer program this is done by the Monte Carlo method.

The model is Y=b1 X1 + b2 X2 + bk Xk +e where e is an error term. You initially get n residuals by taking yi - b^1 x1i -b^2 x2i-...-b^k xki to be the ith residual. Here b^i denotes the estimate of the regression parameter bi.

We use the notation yi and xki to represent the ith observed value of the dependent variable and the ith observed value of the kth predictor variable respectively.

As this gets complicated, I suggest you look at a reference on bootstrapping residuals The 1993 Chapman and Hall text by Efron and Tibshirani is one possibility. The end results are bootstrap distributions for each regression parameter and one of several possible bootstrap confidence intervals could be used. Efron's percentile method is the most likely possibility. If the confidence interval does not contain 0 the regression parameter is considered significant.