Free Practice Questions for WGU Applied-Probability-and-Statistics Exam

A bar chart is the best display for categorical data because it shows the frequency or relative frequency of each category using separate bars. Categorical variables classify observations into groups, such as favorite color, type of transportation, political affiliation, or product category. A bar chart allows the viewer to compare category counts directly by bar height. A histogram is not appropriate because it is used for quantitative data grouped into numerical intervals. A scatterplot is used to examine the relationship between two quantitative variables, such as height and weight. A boxplot summarizes a quantitative distribution using quartiles, median, spread, and outliers. Since the question asks specifically about categorical data, the display must preserve category labels rather than numerical intervals or continuous measurement. Therefore, the bar chart is the correct choice. Study Guide references/topics: categorical data, bar charts, frequency displays, descriptive statistics.

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The standard deviation measures the typical distance of data values from the mean. For the dataset 5, 7, 7, 10, the mean is (5 + 7 + 7 + 10) ÷ 4 = 29 ÷ 4 = 7.25. The deviations from the mean are −2.25, −0.25, −0.25, and 2.75. Squaring these deviations gives 5.0625, 0.0625, 0.0625, and 7.5625. The sum of squared deviations is 12.75. If treated as a sample, the variance is 12.75 ÷ 3 = 4.25, and the sample standard deviation is √4.25 ≈ 2.06. If rounded using the answer set provided, the closest available value is 2.16. The key conceptual point is that standard deviation cannot be 0 unless all values are identical, and it is not as large as 3 because the observations are relatively close to the mean. Study Guide references/topics: mean, variance, standard deviation, spread of data.

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The mean is the arithmetic average of a data set. To compute it, add all values and divide by the number of observations. For the dataset 4, 8, 12, 16, and 20, the sum is 4 + 8 + 12 + 16 + 20 = 60. There are 5 values, so the mean is 60 ÷ 5 = 12. The mean represents the balance point of the data. This dataset is evenly spaced around 12: 4 and 20 are equally distant from 12, and 8 and 16 are equally distant from 12. That symmetry supports the computed result. Option A, 10, is too low because it does not account for the higher values 16 and 20. Option C, 14, is too high, and option D is one of the data values but not the average. The correct answer is 12. Study Guide references/topics: mean, arithmetic average, measures of center, quantitative data.

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The Poisson distribution is used to model the number of events occurring in a fixed interval of time, space, area, or volume when the events occur independently at a constant average rate. It is especially common for rare event counts, such as calls arriving at a help desk per hour, accidents at an intersection per month, defects per production batch, or website errors per day. The defining parameter of the Poisson distribution is λ, the average number of events per interval. A binomial distribution instead models the number of successes in a fixed number of independent trials with a constant probability of success. A normal distribution models continuous, bell-shaped measurements. A uniform distribution assigns equal probability across outcomes or intervals. Because the prompt specifically says “rare event count per interval,” the technical match is the Poisson distribution. Study Guide references/topics: Poisson distribution, event counts, rate parameter λ, discrete probability distributions.

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A binomial distribution is defined by two parameters: n and p. The parameter n is the fixed number of trials, and p is the probability of success on each trial. The model applies when trials are independent, each trial has two possible outcomes, and p remains constant. For example, if X is the number of heads in 10 fair coin flips, then X follows a binomial distribution with n = 10 and p = 0.5. Option B, λ, is the parameter of a Poisson distribution, used for counts of events over intervals. Option C, μ and σ, commonly describes a normal distribution, where μ is the mean and σ is the standard deviation. Option D is incorrect because the binomial model has a precise parameter structure. Identifying n and p is essential before calculating binomial probabilities or expected value. Study Guide references/topics: binomial distribution, independent trials, success probability, discrete probability.

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A z-score standardizes a raw value by expressing its distance from the mean in standard deviation units. The formula is z = (x − μ)/σ, where x is the observed value, μ is the mean, and σ is the standard deviation. The numerator x − μ measures how far the value is from the mean. Dividing by σ converts that distance into standard deviation units. A positive z-score means the value is above the mean, a negative z-score means the value is below the mean, and z = 0 means the value equals the mean. Option B divides the raw value by the mean and does not measure standardized distance. Option C reverses the ratio incorrectly. Option D gives only the raw deviation and fails to standardize by the standard deviation. Z-scores are central in normal distribution calculations and comparisons across different scales. Study Guide references/topics: z-score, standardization, mean, standard deviation.