Applied-Probability-and-Statistics유효한인증덤프최신시험대비덤프공부자료

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

SectionObjectives
Regression and Modeling- Linear Relationships
  • 1. Simple linear regression
    • 2. Slope and intercept interpretation
      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
                Descriptive Statistics- Two Variable Data Analysis
                • 1. Outliers and relationships
                  • 2. Scatter plots interpretation
                    • 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- Estimation and Confidence Intervals
                            • 1. Confidence interval interpretation
                              • 2. Point estimates
                                - Hypothesis Testing (Introductory Level)
                                • 1. Null vs alternative hypothesis
                                  • 2. p-values interpretation

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                                    Applied-Probability-and-Statistics최신 시험덤프자료 - Applied-Probability-and-Statistics참고덤프

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                                    최신 Courses and Certificates Applied-Probability-and-Statistics 무료샘플문제 (Q79-Q84):

                                    질문 # 79
                                    A marketing analyst is studying the relationship between customer age groups, teens, adults, and seniors, and their preferred shopping methods, online and in-store.
                                    Which numerical measure is most appropriate for this study?

                                    정답：C

                                    설명：
                                    This study involves two categorical variables: customer age group and preferred shopping method. Age group is categorical because customers are classified into groups such as teens, adults, and seniors. Shopping method is also categorical because customers are classified as preferring either online or in-store shopping.
                                    When two categorical variables are analyzed together, conditional percentages are the most appropriate numerical summary because they describe how the distribution of one categorical variable changes within levels of another categorical variable. For example, the analyst could compute the percentage of teens who prefer online shopping, the percentage of adults who prefer online shopping, and the percentage of seniors who prefer online shopping. A five-number summary is used for quantitative data, not categorical data. Mean difference requires numerical measurements. A correlation coefficient measures the strength and direction of a linear relationship between two quantitative variables, so it is not appropriate here. References/topics from the Study Guide: two-variable categorical data, conditional percentages, categorical association, numerical summaries.


                                    질문 # 80
                                    Correlation coefficient r = #0.9 indicates:

                                    정답：A

                                    설명：
                                    The correlation coefficient r measures the direction and strength of a linear relationship between two quantitative variables. It ranges from #1 to 1. A value close to #1 indicates a strong negative linear relationship, meaning that as one variable increases, the other tends to decrease in a highly consistent linear pattern. Since r = #0.9 is very close to #1, the relationship is strong and negative. Option B is incorrect because the sign is negative, not positive. Option C identifies the direction correctly but understates the strength; #0.9 is not weak. Option D would correspond to r near 0, where there is little or no linear association. A scatterplot associated with r = #0.9 would show points clustered closely around a downward- sloping line. This does not prove causation; it describes linear association only. Study Guide references
                                    /topics: correlation coefficient, negative association, strength of relationship, scatterplots.


                                    질문 # 81
                                    Central limit theorem applies when:

                                    정답：D

                                    설명：
                                    The central limit theorem applies when the sample size is sufficiently large and observations are independent.
                                    It states that the sampling distribution of the sample mean becomes approximately normal as sample size increases, even if the original population distribution is not normal. This makes option A correct. Option B is incorrect because the population does not have to be normal for the theorem to operate; large sample size is what permits the normal approximation. Option C is not the condition that defines the theorem. A population parameter may be unknown in inference, but that is not the trigger for the central limit theorem. Option D is incorrect because the classic central limit theorem for means concerns quantitative data, not purely categorical labels. The theorem is foundational because it justifies using normal-based confidence intervals and hypothesis tests for means when sample sizes are large. Study Guide references/topics: central limit theorem, sample size, sampling distribution, normal approximation.


                                    질문 # 82
                                    A t-distribution is used when:

                                    정답：D

                                    설명：
                                    A t-distribution is used for inference about a population mean when the population standard deviation # is unknown, especially with a small sample. In that situation, the sample standard deviation s must estimate #, creating extra uncertainty. The t-distribution accounts for this added uncertainty with heavier tails than the standard normal distribution. As sample size increases, the t-distribution becomes closer to the normal distribution. Option B describes a z-procedure setting, where # is known and the normal distribution may be used. Option C is incorrect because categorical data are usually analyzed with proportions or chi-square procedures, not t-distributions for means. Option D is incomplete: t-procedures typically require quantitative continuous or approximately continuous data, but the decisive condition in the answer set is unknown # with a small sample. Study Guide references/topics: t-distribution, unknown standard deviation, small samples, inference for means.


                                    질문 # 83
                                    Ten teachers each had a jar of jelly beans. The jars had this number of jelly beans in each respective jar: 50,
                                    60, 80, 80, 100, 100, 100, 120, 120, and 140.
                                    What is the mean of these data?

                                    정답：D

                                    설명：
                                    The mean is the arithmetic average of a quantitative data set. To calculate it, add all observed values and divide by the number of observations. The jelly bean counts are 50, 60, 80, 80, 100, 100, 100, 120, 120, and
                                    140. Their sum is 950. There are 10 jars, so the mean is 950 ÷ 10 = 95. This means the average number of jelly beans per jar is 95. Option A, 100, may seem plausible because 100 appears frequently and is near the center of the data, but frequency alone determines the mode, not the mean. Option C, 80, is one of the repeated values but not the average. Option B, 120, is too high because several observations fall well below
                                    120. The mean uses every value in the data set, including the low values 50 and 60 and the high value 140.
                                    References/topics from the Study Guide: mean, measures of center, quantitative data, arithmetic average.


                                    질문 # 84
                                    ......

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                                    Applied-Probability-and-Statistics최신 시험덤프자료: https://www.itcertkr.com/Applied-Probability-and-Statistics_exam.html