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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Descriptive Statistics | 25% | - Measures of spread: range, IQR, variance, standard deviation - Types of data: categorical, discrete, continuous - Graphical displays: histograms, boxplots, scatterplots - Measures of center: mean, median, mode |
| Topic 2: Correlation & Regression | 20% | - Simple linear regression models - Interpreting slope, intercept, and R-squared - Correlation coefficient and interpretation - Predictions and limitations of regression |
| Topic 3: Basic Numeracy & Algebra | 15% | - Exponents, roots, and basic formulas - Linear equations, inequalities, graphing functions - Arithmetic operations, fractions, decimals, percentages |
| Topic 4: Inferential Statistics & Study Design | 20% | - Hypothesis testing framework and interpretation - Confidence intervals for means/proportions - Sampling methods and bias - Observational studies vs experiments |
| Topic 5: Probability Concepts | 20% | - Probability rules, independent and dependent events - Conditional probability and Venn diagrams - Discrete and continuous probability distributions - Normal distribution and empirical rule |
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NEW QUESTION # 36
A sociologist is investigating the relationship between educational level, high school, bachelor's, master's, and doctorate, and annual income.
How should this study be classified?
Answer: B
Explanation:
This study contains two variables: educational level and annual income. Educational level is categorical because it places individuals into named groups, such as high school, bachelor's, master's, and doctorate.
Although these categories have a natural order, they are still categories rather than measured numerical quantities. Annual income is quantitative because it is measured numerically and can be analyzed using arithmetic summaries such as mean, median, range, and standard deviation. Therefore, the study is classified as categorical-to-quantitative. This classification is important because it determines appropriate statistical displays and summaries. For example, side-by-side box plots or group means could be used to compare annual income across education levels. A categorical-to-categorical study would involve two label-based variables, such as education level and employment sector. A quantitative-to-quantitative study would involve two numerical variables, such as income and years of experience. References/topics from the Study Guide:
categorical variables, quantitative variables, two-variable data classification, comparative summaries.
NEW QUESTION # 37
Sample mean = 40, SD = 8, n = 16. Standard error = ?
Answer: C
Explanation:
The standard error of the sample mean is calculated as SE = s/#n, where s is the sample standard deviation and n is the sample size. Here, the standard deviation is 8 and the sample size is 16. The square root of 16 is 4, so SE = 8/4 = 2. The sample mean of 40 is not used directly in the standard error formula; it identifies the center of the sample, not the variability of the sample mean across repeated samples. Option B, 8, is the standard deviation, not the standard error. Option D, 4, is the square root of the sample size. Option C, 0.5, would result from an incorrect calculation. Standard error decreases as sample size increases because larger samples provide more precise estimates. Study Guide references/topics: standard error, standard deviation, sample size, sampling distribution.
NEW QUESTION # 38
Probability of rolling sum = 12 = ?
Answer: D
Explanation:
Rolling two six-sided dice produces 36 equally likely ordered outcomes. A sum of 12 can occur only when both dice show 6, giving the single ordered pair (6, 6). Since there is exactly 1 favorable outcome out of 36 total outcomes, the probability is 1/36. This is structurally similar to rolling a sum of 2, where only (1, 1) works. Option B, 1/6, is much too large and would apply to one specified face on one die. Option C, 2/36, would require two different ordered outcomes, but no pair other than (6, 6) sums to 12. Option D, 1/12, also overstates the probability. Extreme sums such as 2 and 12 are least likely because they have only one possible ordered combination. Study Guide references/topics: sample spaces, dice sums, theoretical probability, equally likely outcomes.
NEW QUESTION # 39
In a standard normal distribution, z-score for the mean = ?
Answer: A
Explanation:
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.
NEW QUESTION # 40
Variance formula = ?
Answer: C
Explanation:
The sample variance formula is s² = #(x# # x#)² / (n # 1). In this formula, x# represents each data value, x# is the sample mean, and n is the sample size. The expression x# # x# gives each value's deviation from the mean. Squaring the deviations prevents negative and positive deviations from canceling and emphasizes larger departures. Dividing by n # 1 gives the sample variance, using degrees of freedom to correct bias when estimating population variance from a sample. Option B is the formula for the sample mean, not variance.
Option C squares the mean and does not measure spread. Option D resembles part of the standard deviation process but omits division by n # 1 and is not the variance formula. Variance is foundational because standard deviation is the square root of variance. Study Guide references/topics: variance, sample variance formula, mean deviations, measures of spread.
NEW QUESTION # 41
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