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

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

SectionObjectives
Statistical Inference- Hypothesis testing
  • 1. Null and alternative hypotheses
    • 2. t-tests and z-tests (basic application)
      - Estimation
      • 1. Confidence intervals for means and proportions
        Regression and Correlation- Relationship analysis
        • 1. Correlation coefficient interpretation
          • 2. Simple linear regression basics
            Descriptive Statistics- Data summarization
            • 1. Measures of variability (range, variance, standard deviation)
              • 2. Measures of central tendency (mean, median, mode)
                - Data visualization
                • 1. Histograms and frequency distributions
                  • 2. Box plots and interpretation
                    Probability Distributions- Continuous distributions
                    • 1. Normal distribution
                      • 2. Standard normal and z-scores
                        - Discrete distributions
                        • 1. Poisson distribution (introductory use cases)
                          • 2. Binomial distribution
                            Probability- Probability rules
                            • 1. Addition and multiplication rules
                              • 2. Bayesโ€™ theorem (introductory level)
                                - Fundamental probability concepts
                                • 1. Events and sample spaces
                                  • 2. Conditional probability and independence

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                                    Valid Applied-Probability-and-Statistics Exam Pdf & Applied-Probability-and-Statistics Practice Tests

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q138-Q143):

                                    NEW QUESTION # 138
                                    A standard deck of 52 playing cards, 13 with hearts, 13 with diamonds, 13 with clovers, and 13 with spades, is shuffled, and one card is drawn at random.
                                    What is the probability of drawing a heart?

                                    Answer: B

                                    Explanation:
                                    The probability of a simple event is calculated by dividing the number of favorable outcomes by the total number of equally likely outcomes. A standard deck contains 52 cards. The favorable outcomes are the hearts, and the question states that there are 13 hearts in the deck. Therefore, the probability of drawing a heart is 13
                                    /52. This fraction simplifies by dividing both numerator and denominator by 13, giving 1/4. Option A, 1/13, would incorrectly treat one card as favorable out of 13 rather than 13 favorable hearts out of 52 total cards.
                                    Option B, 1/2, would imply that half the deck consists of hearts, which is false. Option D, 1/3, does not correspond to the four equally sized suits in a standard deck. Since the four suits are equally represented, each suit has probability one out of four. References/topics from the Study Guide: theoretical probability, equally likely outcomes, fractions, sample spaces.


                                    NEW QUESTION # 139
                                    A die is rolled twice. Probability of rolling two sixes?

                                    Answer: D

                                    Explanation:
                                    Rolling a standard six-sided die twice creates two independent events. The probability of rolling a six on the first roll is 1/6 because there is one favorable outcome, six, out of six equally likely outcomes. The probability of rolling a six on the second roll is also 1/6. Because the outcome of the first roll does not affect the outcome of the second roll, the multiplication rule for independent events applies. Thus, P(six and six) = 1/6 ร— 1/6 = 1
                                    /36. Another way to verify the result is to list the sample space: two die rolls produce 6 ร— 6 = 36 equally likely ordered outcomes. Only one outcome, (6, 6), satisfies the condition of two sixes. Therefore, the probability is
                                    1 favorable outcome out of 36 total outcomes. Study Guide references/topics: independent events, multiplication rule, sample space, theoretical probability.


                                    NEW QUESTION # 140
                                    Random variable X = number of heads in 4 coin flips #

                                    Answer: C

                                    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 # 141
                                    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: A


                                    NEW QUESTION # 142
                                    Probability cannot be:

                                    Answer: C

                                    Explanation:
                                    Probability values must lie between 0 and 1, inclusive. A probability of 0 means an event is impossible, while a probability of 1 means an event is certain. Values between 0 and 1 represent events with varying degrees of likelihood. A negative probability has no valid interpretation in standard probability theory because probability measures relative likelihood or long-run frequency, and neither can be less than zero. Option B is allowed because impossible events have probability 0. Option C is allowed because certain events have probability 1. Option D is allowed because ordinary uncertain events have probabilities strictly between 0 and
                                    1. For example, the probability of rolling a 3 on a fair die is 1/6, which lies between 0 and 1. The only invalid option listed is negative probability. Study Guide references/topics: probability axioms, probability range, impossible events, certain events.


                                    NEW QUESTION # 143
                                    ......

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