Exam Dumps Applied-Probability-and-Statistics Collection, Certification Applied-Probability-and-Statistics Exam Cost

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

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q116-Q121):

                                    NEW QUESTION # 116
                                    In a game, a coin is tossed, and a spinner with 8 equal spaces numbered 1 through 8 is spun.
                                    What is the probability of getting heads on the coin and a number less than 3 on the spinner?

                                    Answer: B


                                    NEW QUESTION # 117
                                    Central limit theorem applies when:

                                    Answer: D

                                    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 # 118
                                    Uniform distribution 0-5: P(X < 3) = ?

                                    Answer: D

                                    Explanation:
                                    For a continuous uniform distribution on the interval from 0 to 5, probability is proportional to interval length.
                                    The total interval length is 5 # 0 = 5. The event X < 3 corresponds to the interval from 0 to 3, which has length 3. Therefore, P(X < 3) = 3/5 = 0.6. This works because the uniform distribution assigns constant density across the entire interval, so any subinterval's probability equals its length divided by the total length.
                                    Option B would correspond to half the interval, such as X < 2.5. Option C would correspond to length 2 out of
                                    5. Option D incorrectly treats the cutoff value 3 as if it were a percentage rather than an interval boundary.
                                    The correct answer is 0.6, meaning 60% of the uniform distribution lies below 3. Study Guide references
                                    /topics: uniform distribution, continuous probability, interval length, probability density.


                                    NEW QUESTION # 119
                                    Sum of probabilities in sample space = ?

                                    Answer: C

                                    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 # 120
                                    What is true about the correlation between the variables?

                                    Answer: B

                                    Explanation:
                                    The scatterplot shows a clear downward pattern: as x increases, y tends to decrease. This establishes a negative correlation. The strength of a correlation is judged by how closely the points follow a general linear pattern. Here, although the points are not perfectly aligned, they form a visible descending band from upper left to lower right. That pattern is much stronger than a weak association, where the points would appear widely scattered with only a slight trend. Therefore, the best description is a strong negative correlation.
                                    Options C and D are incorrect because they describe positive correlation, which would require y to increase as x increases. Option A correctly identifies the negative direction but understates the strength of the pattern. In correlation terminology, a strong negative value would have r substantially below 0 and closer to #1 than to 0.
                                    References/topics from the Study Guide: scatterplots, negative correlation, correlation strength, bivariate quantitative data.


                                    NEW QUESTION # 121
                                    ......

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