Quiz WGU - Applied-Probability-and-Statistics–Latest Valid Test Testking

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

SectionObjectives
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- Probability rules
        • 1. Bayes’ theorem (introductory level)
          • 2. Addition and multiplication rules
            - Fundamental probability concepts
            • 1. Conditional probability and independence
              • 2. Events and sample spaces
                Regression and Correlation- Relationship analysis
                • 1. Correlation coefficient interpretation
                  • 2. Simple linear regression basics
                    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. Standard normal and z-scores
                              • 2. Normal distribution
                                - Discrete distributions
                                • 1. Poisson distribution (introductory use cases)
                                  • 2. Binomial distribution

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q151-Q156):

                                    NEW QUESTION # 151
                                    Two independent events: P(A) = 0.3, P(B) = 0.2. P(A or B) = ?

                                    Answer: B

                                    Explanation:
                                    For two events, the general addition rule is P(A or B) = P(A) + P(B) # P(A and B). Because A and B are independent, their joint probability is P(A and B) = P(A)P(B). Here, P(A) = 0.3 and P(B) = 0.2, so P(A and B)
                                    = 0.3 × 0.2 = 0.06. Then P(A or B) = 0.3 + 0.2 # 0.06 = 0.44. The subtraction is necessary because the overlap, where both A and B occur, is counted twice when adding P(A) and P(B). Option A, 0.5, incorrectly adds the two probabilities without correcting for overlap. Option B gives the probability of both events, not either event. Option D is unrelated to the addition rule. Study Guide references/topics: independent events, addition rule, multiplication rule, union of events.


                                    NEW QUESTION # 152
                                    Poisson distribution mean = ?

                                    Answer: B

                                    Explanation:
                                    For a Poisson distribution, the mean is #, pronounced "lambda." The parameter # represents the expected number of events occurring in a fixed interval, such as calls per hour, defects per batch, or arrivals per minute.
                                    A defining feature of the Poisson distribution is that both the mean and variance are equal to #. Therefore, if X follows a Poisson distribution with # = 4, then the expected value, or mean, is 4, and the variance is also 4.
                                    Option B and option C are only correct in special cases where # happens to equal 0 or 1, not generally. Option D is conceptually related because the Poisson variance equals #, but the question asks for the mean, and the parameter name is #. The Poisson distribution is used for discrete counts of events over a fixed interval under a constant average rate. Study Guide references/topics: Poisson distribution, expected value, variance, # parameter.


                                    NEW QUESTION # 153
                                    Correlation coefficient r = #0.5 #

                                    Answer: C

                                    Explanation:
                                    The correlation coefficient r describes the direction and strength of a linear relationship between two quantitative variables. Its values range from #1 to +1. A negative value indicates that as one variable increases, the other tends to decrease. Since r = #0.5, the direction is negative. Its magnitude, 0.5, is not close enough to zero to indicate no relationship, but it is also not close enough to #1 to represent an extremely strong relationship. Therefore, the best description is a moderate negative linear relationship. Option B is incorrect because the sign is negative, not positive. Option C is incorrect for both direction and strength.
                                    Option D would be appropriate only when r is near 0, indicating little or no linear association. A scatterplot with r = #0.5 would generally show a downward trend, but with noticeable scatter around the trend. Study Guide references/topics: correlation coefficient, negative association, scatterplots, linear relationship strength.


                                    NEW QUESTION # 154
                                    Sum of probabilities in sample space = ?

                                    Answer: B

                                    Explanation:
                                    The probabilities of all outcomes in a complete sample space must sum to 1. A sample space contains every possible outcome of a probability experiment, and one of those outcomes must occur. For example, when rolling a fair six-sided die, the outcomes are 1, 2, 3, 4, 5, and 6. Each has probability 1/6, and the sum is 1/6 +
                                    1/6 + 1/6 + 1/6 + 1/6 + 1/6 = 1. A total probability of 0 would mean no outcome can occur, which is impossible for a valid experiment. A total greater than 1 violates probability rules because probabilities cannot exceed certainty. "Cannot exceed 2" is too broad and mathematically invalid, since the exact total must equal
                                    1. This principle is foundational for checking probability distributions and validating whether assigned probabilities are coherent. Study Guide references/topics: sample space, probability axioms, total probability, theoretical probability.


                                    NEW QUESTION # 155
                                    Sample mean = best point estimate of population mean?

                                    Answer: A

                                    Explanation:
                                    The sample mean is the standard point estimate for the population mean. A point estimate is a single statistic calculated from sample data and used to estimate an unknown population parameter. If the parameter of interest is the population mean #, the corresponding sample statistic is x#. Therefore, the statement is true.
                                    This does not mean the sample mean is guaranteed to equal the population mean exactly; sampling variability can cause sample means to differ from the true population mean. However, the sample mean is unbiased under random sampling and becomes more stable as sample size increases. Confidence intervals expand this idea by placing a margin of error around the sample mean. Option B is incorrect because the sample mean is precisely the conventional point estimator for #. Options C and D impose invalid restrictions. Study Guide references/topics: sample mean, population mean, point estimate, sampling variability.


                                    NEW QUESTION # 156
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