Consider the following output from a regression predicting Oscar Best Picture winners based on the number of categories in which the film was nominated (1976-2015) Because the p-value given is [ Select ] ["extremely small", "moderate", "extremely large"] there is
[ Select ] ["very weak", "almost no", "borderline", "very strong"]
evidence of a relationship between the number of categories in which a film is nominated and its likelihood of winning Best Picture.

Logistic Regression - winners
Dependent variable: 1=won 0= loss
Factors: number of categories nominated
Estimated Regression Model (Maximum Likelihood)
Standard Estimated
Parameter Estimate Error Odds Ratio
CONSTANT -5.17048 0.737865
number of categories nominated 0.462202 0.0835516 1.58757
Analysis of Deviance
Source Deviance Df P-Value
Model 38.9351 1 0.0000
Residual 172.834 226 0.9965
Total (corr.) 211.769 227
Percentage of deviance explained by model = 18.3857
Adjusted percentage = 16.4968
Likelihood Ratio Tests
Factor Chi-Square Df P-Value
number of categories nominated 38.9351 1 0.0000