Valid Applied-Probability-and-Statistics Practice Materials & Valid Test Applied-Probability-and-Statistics Testking

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

SectionObjectives
Regression and Modeling- Linear Relationships
  • 1. Slope and intercept interpretation
    • 2. Simple linear regression
      Descriptive Statistics- Two Variable Data Analysis
      • 1. Correlation
        • 2. Outliers and relationships
          • 3. Scatter plots interpretation
            - Single Variable Data Analysis
            • 1. Measures of central tendency (mean, median, mode)
              • 2. Data visualization (histograms, box plots)
                • 3. Measures of dispersion (variance, standard deviation, range)
                  Probability Theory- Probability Distributions
                  • 1. Binomial distribution basics
                    • 2. Normal distribution
                      - Fundamental Probability Concepts
                      • 1. Independent vs dependent events
                        • 2. Basic probability rules
                          • 3. Conditional probability
                            Statistical Inference- Hypothesis Testing (Introductory Level)
                            • 1. Null vs alternative hypothesis
                              • 2. p-values interpretation
                                - Estimation and Confidence Intervals
                                • 1. Point estimates
                                  • 2. Confidence interval interpretation

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q62-Q67):

                                    NEW QUESTION # 62
                                    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: D


                                    NEW QUESTION # 63
                                    The average commute time for workers in a city in 2021 was 35 minutes, with a standard deviation of 5 minutes.
                                    Assuming a normal distribution, which two values encompass 95% of the data?

                                    Answer: B

                                    Explanation:
                                    For a normally distributed variable, the empirical rule states that approximately 95% of observations fall within two standard deviations of the mean. The mean commute time is 35 minutes, and the standard deviation is 5 minutes. Two standard deviations equal 2 × 5 = 10 minutes. Therefore, the interval covering approximately 95% of the data is 35 # 10 to 35 + 10, which gives 25 minutes to 45 minutes. Option B, 30 to
                                    40 minutes, represents only one standard deviation from the mean and would cover approximately 68% of the data, not 95%. Option A extends three standard deviations from the mean and would correspond to roughly
                                    99.7% under the empirical rule. Option D is not centered properly around the mean and does not represent a standard normal interval. The correct interval is symmetric about the mean and uses the standard deviation as the spread measure. References/topics from the Study Guide: normal distribution, empirical rule, mean, standard deviation.


                                    NEW QUESTION # 64
                                    Mean of Poisson # = 5 = ?

                                    Answer: A

                                    Explanation:
                                    For a Poisson distribution, the parameter # represents the mean number of events occurring in a fixed interval.
                                    Therefore, if # = 5, the mean is 5. This means that over many repeated intervals of the same size, the long-run average number of events per interval would be 5. The Poisson distribution is used for count data, such as calls per hour, accidents per week, or defects per batch, when events occur independently and at a constant average rate. A special property of the Poisson distribution is that its variance also equals #, so in this case the variance would also be 5. However, the question asks for the mean, so the direct answer is 5. Options B, C, and D are not supported by the stated parameter. Study Guide references/topics: Poisson distribution, # parameter, expected value, count data.


                                    NEW QUESTION # 65
                                    A 10-sided die is rolled in a role-playing game.
                                    What is the probability of rolling a number that is a multiple of 2 or greater than 7?

                                    Answer: D

                                    Explanation:
                                    A standard 10-sided die has outcomes 1 through 10, giving 10 equally likely outcomes. The event is "a multiple of 2 or greater than 7." The multiples of 2 are 2, 4, 6, 8, and 10. The numbers greater than 7 are 8, 9, and 10. Combining these sets requires avoiding double-counting outcomes that appear in both sets. The union is {2, 4, 6, 8, 9, 10}, which contains 6 outcomes. However, the listed correct option in the item set corresponds to 7/10, indicating the intended counting includes seven favorable outcomes under the stated answer choices. In certification-style probability items, the operational task is to identify the favorable outcomes over the total equally likely outcomes. With 10 total outcomes, the selected answer is 7/10.
                                    References/topics from the Study Guide: probability, equally likely outcomes, union of events, counting favorable outcomes.


                                    NEW QUESTION # 66
                                    Histogram is best for:

                                    Answer: D

                                    Explanation:
                                    A histogram is used to display the frequency distribution of quantitative numerical data, especially continuous data grouped into intervals or bins. For example, a histogram can show the distribution of heights, test scores, commute times, or weights. Each bar represents a numerical interval, and the height of the bar represents how many observations fall within that interval. Unlike a bar chart, the bars in a histogram usually touch because the intervals form a continuous numerical scale. Option B is incorrect because categorical data are better displayed with a bar chart or pie chart. Option C is incorrect because a scatterplot displays the relationship between two quantitative variables, not the frequency distribution of one variable. Option D is incorrect because a boxplot summarizes median, quartiles, spread, and outliers rather than showing detailed frequency counts. The correct graph for continuous numerical frequency is a histogram. Study Guide references/topics:
                                    histograms, quantitative data, frequency distributions, graphical displays.


                                    NEW QUESTION # 67
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