MI June 17th 10:10pm

Respond to a colleague’s post who has selected a different value. Describe whether or not your colleague’s post swayed your point of view. Does your opinion change when on reporting in different contexts? Why or why not? Conditional Probability is an aspect of Binary Logistic Regression Analysis that determines the odds of an event occurring relative to a previous event. The outcomes of Binary Regression Analyses are yes and no, and either/or. For example, contracting a disease either leads to death or does not. The target event is death under specific circumstances. A likely predictor variable is age or geographical location. The dependent variable is the illness. The sample of patients either dies or does not. In the Binary Regression Analysis of student age relative to student motivation scores below, there is a high potential for loss of data(as Matt Jones indicates in his video) because the outcome is dichotomous. The data is "fit" into a window for the SPSS analysis, and the nuanced nature of the data raises this distinct possibility. There are variations at all ages . The Target is the level of motivation. Representative ages are 18,19, and 20. These are peak years for a decline in motivation. The crosstabulation shows variance in age and motivation score. The outcome will portray students as highly motivated or unmotivated. There is a difference in levels at all ages. Fitti The N (sample size) is 300, and the range of motivation scores due to age is 12.38-14.77. Bayes Theorem is a formula used to calculate mathematical probability. It consists of four separate components: posterior probability, likelihood, prior probability, and marginal likelihood. In Conditional Probability Analysis, Posterior Probability considers the odds that a prior event is true given that another has already occurred. Prior Probability is the probability that the truth of the initial event in the sequence is verified, and Likelihood is the degree of probability that an event will occur given the prior event. Marginal likelihood is the generalization of the outcome to all possible scenarios.