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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic Numeracy & Algebra | 15% | - Arithmetic operations, fractions, decimals, percentages - Linear equations, inequalities, graphing functions - Exponents, roots, and basic formulas |
| Topic 2: Correlation & Regression | 20% | - Correlation coefficient and interpretation - Interpreting slope, intercept, and R-squared - Simple linear regression models - Predictions and limitations of regression |
| Topic 3: Inferential Statistics & Study Design | 20% | - Confidence intervals for means/proportions - Observational studies vs experiments - Hypothesis testing framework and interpretation - Sampling methods and bias |
| Topic 4: Probability Concepts | 20% | - Normal distribution and empirical rule - Probability rules, independent and dependent events - Discrete and continuous probability distributions - Conditional probability and Venn diagrams |
| Topic 5: Descriptive Statistics | 25% | - Graphical displays: histograms, boxplots, scatterplots - Measures of spread: range, IQR, variance, standard deviation - Measures of center: mean, median, mode - Types of data: categorical, discrete, continuous |
>> Applied-Probability-and-Statistics問題トレーリング <<
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質問 # 143
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?
正解：D
解説：
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.
質問 # 144
A single ball is drawn from an opaque bag that contains red, blue, and green balls. The probability of drawing a red ball is .3, and the probability of drawing a blue ball is .6.
What is the probability of drawing a green ball?
正解：C
解説：
The probabilities of all possible outcomes in a sample space must sum to 1. In this experiment, the bag contains only red, blue, and green balls, so these three outcomes exhaust the sample space. The probability of drawing a red ball is 0.3, and the probability of drawing a blue ball is 0.6. Their combined probability is 0.3 +
0.6 = 0.9. The remaining probability must belong to drawing a green ball. Therefore, P(green) = 1 # 0.9 = 0.1.
This can also be viewed as a complement problem: green is the complement of drawing either red or blue when those are the only other colors available. Option B repeats the red probability, option C would make the total probability exceed 1, and option D is the combined probability of red or blue rather than green.
References/topics from the Study Guide: probability sum rule, complements, sample space, mutually exclusive outcomes.
質問 # 145
A researcher has collected information on students from a school. The researcher wants to display the ethnic groups represented in the school.
Which pair of graphs both display data in this manner?
正解：C
解説：
Ethnic group is a categorical variable because it classifies students into named groups rather than measuring a numerical quantity. Categorical data are commonly displayed with bar graphs or pie charts. A bar graph shows the frequency or relative frequency of each category using separate bars, making it easy to compare group sizes. A pie chart shows how the whole student population is divided among categories, making it useful for displaying proportions or percentages. Therefore, the correct pair is bar graph and pie chart. A box plot is used for quantitative data and summarizes values using quartiles and a median, so it is not appropriate for ethnic categories. A line graph is typically used to show change over time or ordered numerical trends. A histogram is used for distributions of quantitative data grouped into intervals, not named categories. Since the goal is to display category membership, both selected graphs must support categorical frequency or percentage displays. References/topics from the Study Guide: categorical data displays, bar graphs, pie charts, relative frequency.
質問 # 146
Z-test used when:
正解：C
解説：
A z-test for a population mean is used when the population standard deviation # is known and the sampling distribution of the test statistic can be treated as normal. The test statistic has the form z = (sample statistic # hypothesized parameter) divided by the standard error. Knowing # allows the standard error to be computed using #/#n rather than estimating it with the sample standard deviation. Option B describes the common setting for a t-test, not a z-test. Option C is not sufficient by itself; small samples generally require stronger normality assumptions and often favor t-procedures when # is unknown. Option D is unrelated to the classic z- test for a mean, though z-tests can also be used for proportions under appropriate large-sample conditions. In the provided answer set, the defining condition is that the population standard deviation is known. Study Guide references/topics: z-test, population standard deviation, standard error, hypothesis testing.
質問 # 147
Regression intercept represents:
正解：D
解説：
In a linear regression equation, usually written as # = b# + b#x, the intercept b# represents the predicted value of Y when X equals 0. It is the point where the regression line crosses the vertical axis. For example, if a model is # = 12 + 3x, the intercept 12 means the predicted response is 12 when x = 0. The slope, b#, is different; it represents the predicted change in Y for each one-unit increase in X. Correlation measures the strength and direction of a linear association, not the intercept. The mean is a measure of center for a variable and is not the same as a regression intercept. In applied settings, the intercept is meaningful only when X = 0 is within a realistic or relevant range of the data. Study Guide references/topics: linear regression, intercept interpretation, slope-intercept form, response prediction.
質問 # 148
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