제일 빠른 시일내에 제일 간단한 방법으로WGU인증 Applied-Probability-and-Statistics시험을 패스하는 방법이 없냐구요? DumpTOP의WGU인증 Applied-Probability-and-Statistics덤프를 공부하시면 가능합니다. DumpTOP의WGU인증 Applied-Probability-and-Statistics덤프는 많은 분들이 검증한 가장 유력한WGU인증 Applied-Probability-and-Statistics시험공부자료입니다. 덤프의 문제만 기억하시면 패스는 문제없기에 제일 빠른 시일내에 시험을 패스하여 자격증 취득이 가능합니다.
| Section | Objectives |
|---|---|
| Topic 1: Descriptive Statistics | - Two Variable Data Analysis
|
| Topic 2: Probability Theory | - Probability Distributions
|
| Topic 3: Regression and Modeling | - Linear Relationships
|
| Topic 4: Statistical Inference | - Estimation and Confidence Intervals
|
>> Applied-Probability-and-Statistics최신 시험기출문제 <<
WGU Applied-Probability-and-Statistics 시험을 한번에 합격할수 없을가봐 두려워 하고 계시나요? 이 글을 보고 계신 분이라면 링크를 클릭하여 저희 사이트를 방문해주세요. 저희 사이트에는WGU Applied-Probability-and-Statistics 시험의 가장 최신 기출문제와 예상문제를 포함하고 있는 WGU Applied-Probability-and-Statistics덤프자료를 제공해드립니다.덤프에 있는 문제와 답을 완벽하게 기억하시면 가장 빠른 시일내에 가장 적은 투자로 자격증 취득이 가능합니다.
질문 # 132
Type II error # is:
정답：D
설명：
A Type II error occurs when the null hypothesis is false but the test fails to reject it. Many introductory answer choices phrase this as "accept H# when false," though the more technically careful wording is "fail to reject H# when false." This is a false negative: the real effect or difference exists, but the test does not detect sufficient evidence. The probability of a Type II error is denoted #. Option B describes a Type I error, which is rejecting a true null hypothesis. Option C is incorrect because 1 # # is not #; # is the Type I error probability, while 1 # # is statistical power. Option D is too vague and does not identify a formal hypothesis- testing error. The defining condition for Type II error is missing a real effect by failing to reject a false null hypothesis. Study Guide references/topics: Type II error, beta, hypothesis testing, statistical power.
질문 # 133
A study recorded the number of hours participants spent on exercise per week: 3 participants exercised for 1 hour, 6 participants exercised for 2 hours, 4 participants exercised for 3 hours, and 2 participants exercised for
4 hours.
Which type of graph would best represent the frequency of exercise hours?
정답：A
설명：
The data describe frequencies for a single variable: weekly exercise duration. Each duration, 1 hour through 4 hours, has an associated count of participants. A bar chart is the most appropriate display because it uses separate bars to show how many observations fall into each category or discrete value. Here, the bar above 1 hour would have height 3, the bar above 2 hours would have height 6, the bar above 3 hours would have height 4, and the bar above 4 hours would have height 2. A line graph is inappropriate because the data are not measuring change over time. A pie chart could show proportions, but the question specifically asks for frequency of exercise hours, making a bar chart clearer and more direct. A scatterplot requires two quantitative variables and is used to study association, not a one-variable frequency distribution. References
/topics from the Study Guide: frequency distributions, bar charts, single-variable data displays, quantitative
/discrete data.
질문 # 134
A biologist is studying the relationship between plant species, species A, species B, and species C, and the average height of the plants.
How should this study be classified?
정답：A
설명：
This study compares two variables: plant species and average plant height. Plant species is categorical because it places each plant into a named group, such as species A, species B, or species C. These categories are labels rather than numerical measurements. Average height, however, is quantitative because it is measured numerically and can be compared using arithmetic operations such as differences, averages, and ranges. Since the explanatory or grouping variable is categorical and the measured outcome is quantitative, the relationship is classified as categorical-to-quantitative. This classification determines which statistical tools are appropriate. For example, side-by-side box plots, group means, or comparative summaries would be suitable because they compare numerical measurements across categories. A categorical-to-categorical study would involve two label-based variables, such as species and color group. A quantitative-to-quantitative study would involve two numerical variables, such as height and growth rate. References/topics from the Study Guide: two-variable data, categorical variables, quantitative variables, comparative statistical summaries.
질문 # 135
In a normal distribution, 95% of data lies within:
정답：C
설명：
For a normal distribution, the empirical rule states that approximately 68% of data fall within one standard deviation of the mean, approximately 95% fall within two standard deviations, and approximately 99.7% fall within three standard deviations. The notation ±2 SD means two standard deviations below the mean to two standard deviations above the mean, or # # 2# to # + 2#. Therefore, the interval containing about 95% of normally distributed observations is ±2 standard deviations. Option B corresponds to approximately 68%, not
95%. Option C corresponds to approximately 99.7%, and option D extends beyond the standard empirical- rule benchmarks. This concept is central when estimating the typical spread of bell-shaped data, such as test scores, biological measurements, or repeated measurement errors. The correct answer is ±2 SD because it matches the 95% portion of the 68-95-99.7 rule. Study Guide references/topics: normal distribution, empirical rule, standard deviation, distribution spread.
질문 # 136
68-95-99.7 rule: % within 1 SD?
정답：B
설명：
The 68-95-99.7 rule, also called the empirical rule, applies to approximately normal distributions. It states that about 68% of observations fall within 1 standard deviation of the mean, about 95% fall within 2 standard deviations, and about 99.7% fall within 3 standard deviations. The question asks specifically for the percentage within 1 standard deviation, so the correct answer is 68%. In notation, this interval is # # # to # +
#, where # is the mean and # is the standard deviation. This rule is useful for interpreting spread in bell- shaped distributions. For example, if scores have mean 80 and standard deviation 5, then approximately 68% of scores fall between 75 and 85. Option B corresponds to 2 standard deviations, and option C corresponds approximately to 3 standard deviations. Option D is not part of the empirical rule. Study Guide references
/topics: empirical rule, normal distribution, standard deviation, spread.
질문 # 137
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