最新Applied-Probability-and-Statistics題庫資訊 - Applied-Probability-and-Statistics題庫資料

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

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                                    最新的 Courses and Certificates Applied-Probability-and-Statistics 免費考試真題 (Q74-Q79):

                                    問題 #74
                                    Histogram vs bar chart:

                                    答案：B

                                    解題說明：
                                    A histogram and a bar chart both use bars, but they display different types of data. A histogram is used for quantitative numerical data grouped into intervals, such as ages, weights, test scores, or commute times. The bars represent continuous or ordered numeric ranges, and the bars usually touch to show that the scale is continuous. A bar chart is used for categorical data, such as favorite sport, political party, product type, or survey response category. The bars are separated because the categories are distinct labels rather than continuous intervals. Option B reverses the correct uses. Option C is incorrect because the graphs are not the same even though both contain bars. Option D is invalid because option A states the standard distinction.
                                    Correct graph selection depends on identifying whether the variable is categorical or quantitative. Study Guide references/topics: histograms, bar charts, categorical data, quantitative data displays.


                                    問題 #75
                                    Type I error # = 0.05 means:

                                    答案：B

                                    解題說明：
                                    The symbol # represents the significance level of a hypothesis test and is the probability of making a Type I error. A Type I error occurs when the null hypothesis is true but the test rejects it. Therefore, # = 0.05 means there is a 5% risk of rejecting a true null hypothesis under the testing procedure. This is often described as the false positive rate. Option B describes a Type II error, which involves failing to reject a false null hypothesis.
                                    Option C is incorrect because a 5% significance level corresponds to a 95% confidence level in related confidence-interval contexts, not 5% confidence. Option D is unrelated; # is not a sample-size percentage.
                                    The interpretation must connect # directly to the probability of incorrectly rejecting H# when H# is actually true. Study Guide references/topics: hypothesis testing, significance level, Type I error, null hypothesis.


                                    問題 #76
                                    A university surveys faculty and students to determine support for a new campus recycling initiative.
                                    The results of the survey are shown in the following 2 × 2 contingency table:

                                    Which statement is true?

                                    答案：C

                                    解題說明：
                                    This problem compares conditional percentages across two groups. For faculty members, 40 out of 50 support the recycling initiative, so the faculty support rate is 40/50 = 0.80, or 80%. For students, 200 out of 500 support the initiative, so the student support rate is 200/500 = 0.40, or 40%. Comparing 80% and 40% shows that faculty members are twice as likely as students to support the initiative. This is not a slight difference; it is a large difference of 40 percentage points. Therefore, the correct statement is that faculty members are much more likely than students to support the recycling initiative. The table contains two categorical variables: group type and recycling opinion. Conditional percentages are the appropriate numerical tool because the goal is to compare support within each group. References/topics from the Study Guide:
                                    contingency tables, two-way categorical data, conditional percentages, comparative proportions.


                                    問題 #77
                                    A dataset: 4, 8, 12, 16, 20. Mean = ?

                                    答案：A

                                    解題說明：
                                    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.


                                    問題 #78
                                    In a standard normal distribution, z-score for the mean = ?

                                    答案：C

                                    解題說明：
                                    A z-score measures how many standard deviations a value is above or below the mean. The formula is z = (x
                                    # #) / #, where x is the observed value, # is the mean, and # is the standard deviation. If the observed value equals the mean, then x = #. Substituting into the formula gives z = (# # #) / # = 0 / # = 0. Therefore, the z- score corresponding to the mean is always 0 in any normal distribution, including the standard normal distribution. In the standard normal distribution specifically, the mean is 0 and the standard deviation is 1, so the mean lies exactly at z = 0. A z-score of 1 would indicate one standard deviation above the mean, and #1 would indicate one standard deviation below the mean. Study Guide references/topics: standard normal distribution, z-scores, mean, standard deviation.


                                    問題 #79
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