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

SectionObjectives
Topic 1: Descriptive Statistics- Data visualization
  • 1. Histograms and frequency distributions
    • 2. Box plots and interpretation
      - Data summarization
      • 1. Measures of variability (range, variance, standard deviation)
        • 2. Measures of central tendency (mean, median, mode)
          Topic 2: Regression and Correlation- Relationship analysis
          • 1. Correlation coefficient interpretation
            • 2. Simple linear regression basics
              Topic 3: 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)
                    Topic 4: Probability Distributions- Continuous distributions
                    • 1. Standard normal and z-scores
                      • 2. Normal distribution
                        - Discrete distributions
                        • 1. Binomial distribution
                          • 2. Poisson distribution (introductory use cases)
                            Topic 5: Probability- Probability rules
                            • 1. Addition and multiplication rules
                              • 2. Bayes’ theorem (introductory level)
                                - Fundamental probability concepts
                                • 1. Conditional probability and independence
                                  • 2. Events and sample spaces

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q127-Q132):

                                    NEW QUESTION # 127
                                    Central limit theorem applies when:

                                    Answer: C

                                    Explanation:
                                    The central limit theorem applies when the sample size is sufficiently large and observations are independent.
                                    It states that the sampling distribution of the sample mean becomes approximately normal as sample size increases, even if the original population distribution is not normal. This makes option A correct. Option B is incorrect because the population does not have to be normal for the theorem to operate; large sample size is what permits the normal approximation. Option C is not the condition that defines the theorem. A population parameter may be unknown in inference, but that is not the trigger for the central limit theorem. Option D is incorrect because the classic central limit theorem for means concerns quantitative data, not purely categorical labels. The theorem is foundational because it justifies using normal-based confidence intervals and hypothesis tests for means when sample sizes are large. Study Guide references/topics: central limit theorem, sample size, sampling distribution, normal approximation.


                                    NEW QUESTION # 128
                                    Probability of heads on 3 coin flips exactly twice?

                                    Answer: A

                                    Explanation:
                                    This is a binomial probability problem because there is a fixed number of independent trials, each trial has two outcomes, and the probability of success is constant. Let success be flipping heads. There are 3 coin flips, and the probability of heads on each flip is 1/2. The event "exactly two heads" can occur in three ordered ways: HHT, HTH, and THH. Each sequence has probability (1/2)(1/2)(1/2) = 1/8. Since there are three favorable sequences, the total probability is 3 × 1/8 = 3/8. Equivalently, use the binomial formula: C(3,2)(1/2)