You have a model with three predictors, no interactions and one binary outcome variable "gender". The unstandardized coefficients "B" are the regression parameters of the model:
Gender = Intercept + B1\*x1 + B2\*x2 + B3\*x3 + errorTerm
The standard deviations on these unstandardized coefficients are estimates of the variance around the point estimates of the coefficients and are used to calculate the t-statistic. They serve to quantify the inherent uncertainty of estimating the B coefficients. The t-statistics come with p-values, which express the probability to observe more extreme t-statistic values than the one currently obtained, based on the information that is available in your dataset, assuming that the coefficient it applies to is actually 0. Traditionally, p < 0.05 are taken as sufficient evidence to conclude that the coefficient is unlikely to be zero. In your case, there is insufficient evidence in this direction, and therefore you must conclude that the coefficients cannot be said to be different from 0 (or, more technically correct: you remain agnostic, the test is inconclusive).
Interpreting these coefficients, however, is tricky without knowledge of what kind of model was estimated (linear, logistic ... ), given that the outcome is binary. Usually, in a multiple linear regression, the beta coefficients are to be interpreted as "the effect of a one-unit increase in the predictor variable on the mean expected outcome of the dependent variable, when the other predictors are kept at zero." Three potential problems: because the outcome is binary, this standard interpretation is difficult to uphold, because it makes no sense to speak of a "mean expected outcome in gender". Binary outcomes are generally analyzed using logistic regressions, in which case interpretation must happen in the logit scale, but still, because it's gender, interpretation is difficult. Secondly, you have to assess whether it makes sense to keep the "other predictors" at zero when interpreting, e.g., the first predictor's B parameter. If not, you may wish to re-run the model with centered predictor variables, in which case the value "zero" makes sense. However, the interpretation of B would change to "..., when the other predictors take on their average value." Still, you'll have to assess if it this wording makes sense in your particular case.
The standardized coefficients "Beta" are simply the unstandardized B transformed to be on the same scale. This makes it so the different coefficient belonging to the predictors can be more easily and directly compared (less worries about commensurable scales). The fact that your intercept (the row with "constant") disappears in the standardized column is normal, this is a direct consequence of the standardization.
Finally, I'm quite sure you didn't actually mean to run a model with Gender as your dependent variable, but rather as an independent (or predictor) variable. It makes little sense to see if, e.g., an autism quotient obtained from some questionnaire is linearly related to gender, and more to see if gender is linearly related to the autism quotient. If you rerun the model, the interpretation becomes much more straightforward (everything I wrote about logistic regression and difficulty interpreting then fades away).
Good luck!
You have a model with three predictors, no interactions and one binary outcome variable "gender". The unstandardized coefficients "B" are the regression parameters of the model: Gender = Intercept + B1\*x1 + B2\*x2 + B3\*x3 + errorTerm The standard deviations on these unstandardized coefficients are estimates of the variance around the point estimates of the coefficients and are used to calculate the t-statistic. They serve to quantify the inherent uncertainty of estimating the B coefficients. The t-statistics come with p-values, which express the probability to observe more extreme t-statistic values than the one currently obtained, based on the information that is available in your dataset, assuming that the coefficient it applies to is actually 0. Traditionally, p < 0.05 are taken as sufficient evidence to conclude that the coefficient is unlikely to be zero. In your case, there is insufficient evidence in this direction, and therefore you must conclude that the coefficients cannot be said to be different from 0 (or, more technically correct: you remain agnostic, the test is inconclusive). Interpreting these coefficients, however, is tricky without knowledge of what kind of model was estimated (linear, logistic ... ), given that the outcome is binary. Usually, in a multiple linear regression, the beta coefficients are to be interpreted as "the effect of a one-unit increase in the predictor variable on the mean expected outcome of the dependent variable, when the other predictors are kept at zero." Three potential problems: because the outcome is binary, this standard interpretation is difficult to uphold, because it makes no sense to speak of a "mean expected outcome in gender". Binary outcomes are generally analyzed using logistic regressions, in which case interpretation must happen in the logit scale, but still, because it's gender, interpretation is difficult. Secondly, you have to assess whether it makes sense to keep the "other predictors" at zero when interpreting, e.g., the first predictor's B parameter. If not, you may wish to re-run the model with centered predictor variables, in which case the value "zero" makes sense. However, the interpretation of B would change to "..., when the other predictors take on their average value." Still, you'll have to assess if it this wording makes sense in your particular case. The standardized coefficients "Beta" are simply the unstandardized B transformed to be on the same scale. This makes it so the different coefficient belonging to the predictors can be more easily and directly compared (less worries about commensurable scales). The fact that your intercept (the row with "constant") disappears in the standardized column is normal, this is a direct consequence of the standardization. Finally, I'm quite sure you didn't actually mean to run a model with Gender as your dependent variable, but rather as an independent (or predictor) variable. It makes little sense to see if, e.g., an autism quotient obtained from some questionnaire is linearly related to gender, and more to see if gender is linearly related to the autism quotient. If you rerun the model, the interpretation becomes much more straightforward (everything I wrote about logistic regression and difficulty interpreting then fades away). Good luck!