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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Inferential Statistics & Study Design | 20% | - Hypothesis testing framework and interpretation - Sampling methods and bias - Observational studies vs experiments - Confidence intervals for means/proportions |
| Topic 2: Correlation & Regression | 20% | - Correlation coefficient and interpretation - Simple linear regression models - Interpreting slope, intercept, and R-squared - Predictions and limitations of regression |
| Topic 3: Descriptive Statistics | 25% | - Graphical displays: histograms, boxplots, scatterplots - Measures of spread: range, IQR, variance, standard deviation - Types of data: categorical, discrete, continuous - Measures of center: mean, median, mode |
| 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: Basic Numeracy & Algebra | 15% | - Exponents, roots, and basic formulas - Linear equations, inequalities, graphing functions - Arithmetic operations, fractions, decimals, percentages |
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NEW QUESTION # 64
Histogram vs bar chart:
Answer: C
Explanation:
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.
NEW QUESTION # 65
Sample mean = 60; point estimate of population mean = ?
Answer: D
Explanation:
A point estimate is a single sample statistic used to estimate an unknown population parameter. When estimating a population mean, the standard point estimate is the sample mean. In this question, the sample mean is given as 60. Therefore, the point estimate of the population mean is also 60. This does not mean the true population mean is guaranteed to equal 60; rather, 60 is the best single numerical estimate based on the available sample. Confidence intervals build on this point estimate by adding and subtracting a margin of error to reflect sampling uncertainty. Option B is incorrect because although the true population mean is unknown, the point estimate is known. Option C and option D do not correspond to the sample statistic provided. The logic is central to inferential statistics: use sample evidence to estimate population characteristics. Study Guide references/topics: sample mean, population mean, point estimation, inferential statistics.
NEW QUESTION # 66
Random variable X = number of heads in 4 coin flips #
Answer: A
Explanation:
The random variable X counts the number of heads obtained in 4 coin flips. Since it is a count, it can take only specific whole-number values: 0, 1, 2, 3, or 4. It cannot take fractional values such as 2.5 heads. A random variable with countable possible outcomes is classified as discrete. This situation also fits a binomial framework because there is a fixed number of independent trials, each trial has two outcomes, and the probability of heads remains constant for a fair coin. A continuous random variable, by contrast, can take any value over an interval, such as time, weight, or height. The number of heads is not measured on a continuum; it is counted. Therefore, the correct classification is discrete. Study Guide references/topics: discrete random variables, binomial setting, coin-flip outcomes, probability distributions.
NEW QUESTION # 67
Mean = 50, SD = 5, n = 25. Standard error = ?
Answer: C
Explanation:
The standard error of the mean measures the expected sampling variability of the sample mean. It is calculated by dividing the sample standard deviation by the square root of the sample size: SE = s / #n. In this question, the standard deviation is 5 and the sample size is 25. Therefore, SE = 5 / #25 = 5 / 5 = 1. The mean of 50 identifies the center of the sample distribution but is not directly used in the standard error calculation.
Option B, 5, is the standard deviation, not the standard error. Option C, 25, is the sample size. Option D, 0.2, would result from incorrectly dividing 5 by 25 rather than by the square root of 25. The distinction between standard deviation and standard error is important: standard deviation describes spread among individual observations, while standard error describes spread among sample means. Study Guide references/topics:
standard error, standard deviation, sample size, sampling distributions.
NEW QUESTION # 68
Type I error = ?
Answer: D
Explanation:
A Type I error occurs when a statistical test rejects the null hypothesis even though the null hypothesis is actually true. This is often described as a false positive. In practical terms, the test concludes that there is an effect, difference, or relationship when none truly exists under the null condition. The probability of committing a Type I error is denoted by #, the significance level, often set at 0.05. Option B describes a Type II error, where the test fails to reject a false null hypothesis. Option C is not an error at all. Option D is too general; Type I error is not a data-entry issue but a formal decision error in hypothesis testing. The defining phrase is "reject H# when true." Study Guide references/topics: hypothesis testing, Type I error, null hypothesis, significance level.
NEW QUESTION # 69
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