Peter Flom’s answer as above is on-point. On your follow up, the confidence interval (CI) gives you two important pieces of information: the estimate of effect and the precision of your estimate.
1) Effect estimate: Your CI has a lower bound of -1.356 and an upper bound of 0.234. A negative number indicates a negative relationship and a positive one indicates a positive one. Note that your CI includes the value 0, which would suggest no relationship.
2) Precision: how “wide” your estimate is, i.e. The distance between upper and lower bounds. If the distance is small or narrow, the estimate is quite precise. A wide estimate including 0 such as yours suggest the data are very scattered, and we can’t get enough information from your sample to indicate that a true relationship exists.
A clue to interpretation exists in the p-value. When we run the statistical test on your data, we are calculating the likelihood we would see the pattern shown by your exact data in “infinite” imaginary samples if there were truly no association between the dependent and independent variables.
Unfortunately your p-value is fairly high, and together with your confidence interval suggest that there is no evidence for a relationship when just comparing these two variables with your data. If anything, there is (very very weak) evidence suggesting there could be a negative relationship, but really we don’t know.
A CI and p-value go hand-in-hand. The narrower a CI and the farther it is from 0, the lower the p-value.
Your intercept tests give a very different set of stats, but unfortunately in a linear regression this isn’t the main item of interest: the value of x1 is the key item, and if anything the data suggest your independent y variable may clustered around a certain value for reasons unrelated to the dependent variable.