Applied-Probability-and-Statistics Best Preparation Materials | Applied-Probability-and-Statistics Test Preparation

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WGU Applied-Probability-and-Statistics Exam Syllabus Topics:

SectionObjectives
Regression and Correlation- Relationship analysis
  • 1. Simple linear regression basics
    • 2. Correlation coefficient interpretation
      Descriptive Statistics- Data visualization
      • 1. Histograms and frequency distributions
        • 2. Box plots and interpretation
          - Data summarization
          • 1. Measures of central tendency (mean, median, mode)
            • 2. Measures of variability (range, variance, standard deviation)
              Probability Distributions- Continuous distributions
              • 1. Normal distribution
                • 2. Standard normal and z-scores
                  - Discrete distributions
                  • 1. Poisson distribution (introductory use cases)
                    • 2. Binomial distribution
                      Statistical Inference- Estimation
                      • 1. Confidence intervals for means and proportions
                        - Hypothesis testing
                        • 1. Null and alternative hypotheses
                          • 2. t-tests and z-tests (basic application)
                            Probability- Fundamental probability concepts
                            • 1. Events and sample spaces
                              • 2. Conditional probability and independence
                                - Probability rules
                                • 1. Bayes’ theorem (introductory level)
                                  • 2. Addition and multiplication rules

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q142-Q147):

                                    NEW QUESTION # 142
                                    Type I error # = 0.05 means:

                                    Answer: A

                                    Explanation:
                                    The symbol # represents the significance level of a hypothesis test and is the probability of making a Type I error. A Type I error occurs when the null hypothesis is true but the test rejects it. Therefore, # = 0.05 means there is a 5% risk of rejecting a true null hypothesis under the testing procedure. This is often described as the false positive rate. Option B describes a Type II error, which involves failing to reject a false null hypothesis.
                                    Option C is incorrect because a 5% significance level corresponds to a 95% confidence level in related confidence-interval contexts, not 5% confidence. Option D is unrelated; # is not a sample-size percentage.
                                    The interpretation must connect # directly to the probability of incorrectly rejecting H# when H# is actually true. Study Guide references/topics: hypothesis testing, significance level, Type I error, null hypothesis.


                                    NEW QUESTION # 143
                                    Poisson mean = 4. Probability of exactly 2 events?