Applied-Probability-and-Statistics試験情報: Applied Probability and Statistics (FZO1 C955)無料サービスを提供するApplied-Probability-and-Statistics参考書勉強

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

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

                                    >> Applied-Probability-and-Statistics試験情報 <<

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                                    WGU Applied Probability and Statistics (FZO1 C955) 認定 Applied-Probability-and-Statistics 試験問題 (Q139-Q144):

                                    質問 # 139
                                    Z-score formula = ?

                                    正解:A

                                    解説:
                                    The z-score formula is z = (x # #)/#, where x is the observed value, # is the population mean, and # is the population standard deviation. A z-score standardizes a raw value by measuring how many standard deviations it lies above or below the mean. The numerator, x # #, gives the deviation from the mean. Dividing by # converts that deviation into standard deviation units. If z is positive, the value is above the mean. If z is negative, the value is below the mean. If z = 0, the value equals the mean. Option B incorrectly adds the value and mean, which does not measure deviation. Option C ignores the mean, and option D ignores the observed value entirely. The z-score is central for normal distribution calculations because it allows values from different scales to be compared using the standard normal distribution. Study Guide references/topics: z- scores, standard normal distribution, mean, standard deviation.


                                    質問 # 140
                                    Uniform distribution 0-10: P(X < 4) = ?

                                    正解:C

                                    解説:
                                    For a continuous uniform distribution from 0 to 10, probability is proportional to interval length because every subinterval of equal length has equal probability density. The total interval length is 10 # 0 = 10. The event X < 4 corresponds to the interval from 0 to 4, which has length 4. Therefore, P(X < 4) = 4/10 = 0.4.
                                    This means 40% of the distribution lies below 4. Option B, 0.6, would correspond to P(X > 4), since the interval from 4 to 10 has length 6. Option C would represent half the interval, such as P(X < 5). Option D does not match the interval proportion. The essential uniform-distribution principle is area equals probability, and with constant density, area reduces to interval length divided by total length. Study Guide references
                                    /topics: uniform distribution, continuous probability, interval length, probability density.


                                    質問 # 141
                                    A cafe recorded the total ice cream sales and the average maximum temperature of the day. The scatterplot shows the recorded data. The regression equation y = 13x # 598 estimates the total ice cream sales, y, of a day and the average maximum temperature, x, on that day.

                                    What does the slope represent in this context?

                                    正解:B

                                    解説:
                                    In a linear regression equation of the form y = mx + b, the slope m gives the predicted change in the response variable y for a one-unit increase in the explanatory variable x. Here, y represents total ice cream sales and x represents average maximum temperature. The equation y = 13x # 598 has slope 13, so the model predicts that ice cream sales increase by about $13 for every 1°F increase in temperature. This is not the sales value at
                                    0°F; that would be the intercept, #598, which is generally not meaningful in this practical context. It is also not the predicted sales at 100°F, because that would require substituting x = 100 into the regression equation.
                                    The positive slope matches the upward trend in the scatterplot: higher temperatures are associated with higher ice cream sales. References/topics from the Study Guide: correlation and regression, slope interpretation, explanatory and response variables.


                                    質問 # 142
                                    Probability cannot be:

                                    正解:B

                                    解説:
                                    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.


                                    質問 # 143
                                    A dataset: 2, 4, 6, 8, 10. Variance = ?

                                    正解:B

                                    解説:
                                    Variance measures the average squared distance of data values from the mean. For the dataset 2, 4, 6, 8, 10, the mean is (2 + 4 + 6 + 8 + 10) ÷ 5 = 30 ÷ 5 = 6. The deviations from the mean are #4, #2, 0, 2, and 4.
                                    Squaring these deviations gives 16, 4, 0, 4, and 16. The sum of squared deviations is 40. Using the sample variance formula, divide by n # 1, where n = 5. Thus, sample variance = 40 ÷ 4 = 10. Option A is correct.
                                    Option C, 6.25, does not result from the standard sample variance computation for these values. The important distinction is whether a problem is using sample variance or population variance; the provided answer set and calculation use the sample variance formula. Study Guide references/topics: variance, mean, squared deviations, sample variance.


                                    質問 # 144
                                    ......

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