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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Descriptive Statistics | 25% | - Types of data: categorical, discrete, continuous - Measures of center: mean, median, mode - Measures of spread: range, IQR, variance, standard deviation - Graphical displays: histograms, boxplots, scatterplots |
| Topic 2: Basic Numeracy & Algebra | 15% | - Linear equations, inequalities, graphing functions - Exponents, roots, and basic formulas - Arithmetic operations, fractions, decimals, percentages |
| Topic 3: Probability Concepts | 20% | - Normal distribution and empirical rule - Conditional probability and Venn diagrams - Discrete and continuous probability distributions - Probability rules, independent and dependent events |
| Topic 4: Correlation & Regression | 20% | - Correlation coefficient and interpretation - Interpreting slope, intercept, and R-squared - Predictions and limitations of regression - Simple linear regression models |
| Topic 5: Inferential Statistics & Study Design | 20% | - Observational studies vs experiments - Hypothesis testing framework and interpretation - Sampling methods and bias - Confidence intervals for means/proportions |
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NEW QUESTION # 121
In a class of 40 students, the following data were collected on their favorite sport:
* Soccer: 10 students
* Basketball: 15 students
* Tennis: 5 students
* Swimming: 10 students
What is the relative frequency of students whose favorite sport is not basketball?
Answer: D
Explanation:
Relative frequency is the proportion of observations in a category compared with the total number of observations. The class has 40 students. Basketball accounts for 15 students, so the number of students whose favorite sport is not basketball is 40 # 15 = 25. The relative frequency is therefore 25/40. Dividing gives
0.625. This means 62.5% of the class selected a sport other than basketball. The same result can be found by adding the non-basketball categories directly: soccer 10, tennis 5, and swimming 10, giving 10 + 5 + 10 = 25 students. Option A, 0.25, corresponds to 10 out of 40 and would represent soccer or swimming alone. Option B, 0.50, corresponds to 20 out of 40. Option D, 0.75, would imply 30 students, which is too high. References
/topics from the Study Guide: relative frequency, categorical data, frequency tables, proportions.
NEW QUESTION # 122
Binomial expected value = ?
Answer: A
Explanation:
For a binomial random variable, the expected value is E(X) = np, where n is the number of trials and p is the probability of success on each trial. The binomial model applies when there is a fixed number of independent trials, each trial has two outcomes, and the probability of success remains constant. The expected value represents the long-run average number of successes across repeated binomial experiments. For example, if a test has 20 independent questions and the probability of success on each is 0.75, the expected number correct is 20 × 0.75 = 15. Option B, n/p, and option C, p/n, do not represent the binomial mean. Option D, n + p, incorrectly adds the number of trials and success probability, which are not comparable units. The formula np captures both how many opportunities there are and how likely success is on each opportunity. Study Guide references/topics: binomial distribution, expected value, success probability, discrete random variables.
NEW QUESTION # 123
Sample correlation r = 0 #
Answer: B
Explanation:
The correlation coefficient r measures the strength and direction of a linear relationship between two quantitative variables. Values close to 1 indicate strong positive linear association, values close to #1 indicate strong negative linear association, and values near 0 indicate no linear relationship. Therefore, r = 0 means there is no linear association detected by the correlation coefficient. It is important to interpret this precisely: r
= 0 does not guarantee there is no relationship of any kind. A nonlinear relationship may still exist, but correlation measures only linear pattern. Option B and option C are incorrect because strong relationships require r to be close to 1 or #1. Option D is incorrect because perfect correlation occurs at r = 1 or r = #1, not at 0. The correct interpretation is no linear relationship. Study Guide references/topics: correlation coefficient, linear association, scatterplots, correlation interpretation.
NEW QUESTION # 124
In a game, a coin is tossed, and a spinner with 8 equal spaces numbered 1 through 8 is spun.
What is the probability of getting heads on the coin and a number less than 3 on the spinner?
Answer: A
NEW QUESTION # 125
Random variable X = number of heads in 4 coin flips #
Answer: D
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 # 126
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