The response value of 1 on the y-axis represents a success. In these results, the equation is written as the probability of a success. Use the fitted line plot to examine the relationship between the response variable and the predictor variable. The odds ratio is approximately 38, which indicates that for every 1 mg increase in the dosage level, the likelihood that no bacteria is present increases by approximately 38 times. In these results, the model uses the dosage level of a medicine to predict the presence of absence of bacteria in adults. If the p-value is greater than the significance level, you cannot conclude that there is a statistically significant association between the response variable and the predictor. P-value > α: The association is not statistically significant
P-value ≤ α: The association is statistically significant If the p-value is less than or equal to the significance level, you can conclude that there is a statistically significant association between the response variable and the predictor. A significance level of 0.05 indicates a 5% risk of concluding that an association exists when there is no actual association. Usually, a significance level (denoted as α or alpha) of 0.05 works well.
The null hypothesis is that the predictor's coefficient is equal to zero, which indicates that there is no association between the predictor and the response. To determine whether the association between the response variable and the predictor variable in the model is statistically significant, compare the p-value for the predictor to your significance level to assess the null hypothesis.
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