WGU Applied-Probability-and-Statistics Study Test, Applied-Probability-and-Statistics Valid Exam Cram

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

SectionObjectives
Topic 1: Statistical Inference- Hypothesis Testing (Introductory Level)
  • 1. p-values interpretation
    • 2. Null vs alternative hypothesis
      - Estimation and Confidence Intervals
      • 1. Point estimates
        • 2. Confidence interval interpretation
          Topic 2: Probability Theory- Probability Distributions
          • 1. Normal distribution
            • 2. Binomial distribution basics
              - Fundamental Probability Concepts
              • 1. Conditional probability
                • 2. Basic probability rules
                  • 3. Independent vs dependent events
                    Topic 3: Regression and Modeling- Linear Relationships
                    • 1. Slope and intercept interpretation
                      • 2. Simple linear regression
                        Topic 4: 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. Measures of dispersion (variance, standard deviation, range)
                                  • 3. Data visualization (histograms, box plots)

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

                                    NEW QUESTION # 62
                                    Probability sum for all outcomes = ?

                                    Answer: D

                                    Explanation:
                                    In any valid probability model, the probabilities of all outcomes in the sample space must sum to 1. The sample space is the complete set of possible outcomes for an experiment, and exactly one outcome from that set must occur. A total probability of 1 represents certainty. For example, when rolling a fair six-sided die, the six outcomes each have probability 1/6, and their sum is 6 × 1/6 = 1. A total of 0 would imply no possible outcome can occur, which is not a valid probability model. A total greater than 1 violates the maximum boundary of probability, and a total less than 1 would mean the sample space is incomplete or some probability has been omitted. This rule is one of the axioms of probability and is used to verify distributions.
                                    Study Guide references/topics: sample space, probability axioms, total probability, probability distributions.


                                    NEW QUESTION # 63
                                    Dataset: 3, 6, 9. Median?

                                    Answer: C

                                    Explanation:
                                    The median is the middle value of a data set after the values are placed in ascending order. The dataset is 3, 6,
                                    9, which is already ordered from least to greatest. Since there are three observations, the median is the second value, because one value lies below it and one value lies above it. Therefore, the median is 6. The median is a measure of center and is especially useful when data may be skewed or contain outliers because it depends on position rather than the magnitude of every value. The mean for this dataset is also 6, since (3 + 6 + 9) ÷ 3 =
                                    6, but the question specifically asks for the median. Options B and C are not values in the dataset, and option D is the minimum value, not the middle. Study Guide references/topics: median, ordered data, measures of central tendency, descriptive statistics.


                                    NEW QUESTION # 64
                                    There are 12 cell phone charging cords available at a store for purchase. The length of each charging cord in inches is reflected as 3, 3, 4, 6, 6, 6, 6, 7, 10, 10, 11, and 12, respectively.
                                    What are the mean of these data?

                                    Answer: C

                                    Explanation:
                                    The mean is the arithmetic average of a data set. To find it, add all values and divide by the number of values.
                                    The cord lengths are 3, 3, 4, 6, 6, 6, 6, 7, 10, 10, 11, and 12. Their sum is 84. Since there are 12 charging cords, divide 84 by 12 to obtain 7. Therefore, the mean cord length is 7 inches. This value represents the balance point of the distribution, not necessarily a value that must occur most often. In this data set, 6 appears most frequently, so 6 is the mode, but the question asks for the mean. Option B, 8, and option C, 9, are too large because several values are below 7. Option D, 6, identifies the most common value rather than the average. References/topics from the Study Guide: mean, measures of center, quantitative data, arithmetic average.


                                    NEW QUESTION # 65
                                    Variance formula = ?

                                    Answer: B

                                    Explanation:
                                    The sample variance formula is s² = #(x# # x#)² / (n # 1). In this formula, x# represents each data value, x# is the sample mean, and n is the sample size. The expression x# # x# gives each value's deviation from the mean. Squaring the deviations prevents negative and positive deviations from canceling and emphasizes larger departures. Dividing by n # 1 gives the sample variance, using degrees of freedom to correct bias when estimating population variance from a sample. Option B is the formula for the sample mean, not variance.
                                    Option C squares the mean and does not measure spread. Option D resembles part of the standard deviation process but omits division by n # 1 and is not the variance formula. Variance is foundational because standard deviation is the square root of variance. Study Guide references/topics: variance, sample variance formula, mean deviations, measures of spread.


                                    NEW QUESTION # 66
                                    Random variable X = number of heads in 4 coin flips #

                                    Answer: B

                                    Explanation:
                                    The random variable X counts the number of heads obtained in 4 coin flips. Since it is a count, it can take only specific whole-number values: 0, 1, 2, 3, or 4. It cannot take fractional values such as 2.5 heads. A random variable with countable possible outcomes is classified as discrete. This situation also fits a binomial framework because there is a fixed number of independent trials, each trial has two outcomes, and the probability of heads remains constant for a fair coin. A continuous random variable, by contrast, can take any value over an interval, such as time, weight, or height. The number of heads is not measured on a continuum; it is counted. Therefore, the correct classification is discrete. Study Guide references/topics: discrete random variables, binomial setting, coin-flip outcomes, probability distributions.


                                    NEW QUESTION # 67
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