I am new to regression analysis and I am trying to figure out how to interpret my results. I basically did a logit regression in Python and I am wondering how I can interpret the "coef" and "z-value" for example.

My analysis is about how the number of tweets, promos, fb_updates etc. affect whether a business ends up being successful (e.g. the final outcome is either 1 for success, or 0 for failure)

>>> print result.summary()

Logit Regression Results

=============================================

Dep. Variable: success No. Observations: 2780

Model: Logit Df Residuals: 2776

Method: MLE Df Model: 5

Date: Tue, 23 May 2017 Pseudo R-squ.: 0.1952

Time: 21:48:54 Log-Likelihood: -1665.6

converged: True LL-Null: -2069.5

LLR p-value: 2.202e-172

===========================================

----------------coef-------std err------z---------P>|z|------[0.025--- 0.975]

---------------------------------------------------------------------------------

tweets -------0.0022-----0.000-----4.903----- 0.000-----0.001-----0.003

fb_updates --0.2344 ----0.014-----16.492-----0.000-----0.207-----0.262

faq------------0.0798-----0.017------4.704------0.000-----0.047-----0.113

promos-------0.0116-----0.010-----1.178------0.239-----0.031-----0.008

images-------0.0171-----0.005------3.232------0.001-----0.007-----0.027

==============================================

I did some research on what this means:

1. The minus under "coef" means there is an inverse relationship. The coef predicts the dependent variable from the independent variable. However, all my "coef" values are very low except for fb_updates - what does this mean?

2. The std error is low for all variables, so the parameters are statistically different from 0?? Not sure how to interpret this.

3. No idea what "z" means

4. My P-values are all under 0.01 except for promos...so they are all statistically significant except for Promos?

5. [0.025 0.975] --> confidence interval for coefficient?

I also did the "odds ratio"

>>> print np.exp(result.params)

tweets 1.002240

fb_updates 1.264199

faq 1.083017

promos 0.988451

images 1.017253

dtype: float64

I read a bunch of websites trying to figure out how to interpret the results, but I am still lost...any help would be appreciated!