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| Section | Weight | Objectives |
|---|---|---|
| Correlation & Regression | 20% | - Correlation coefficient and interpretation - Simple linear regression models - Predictions and limitations of regression - Interpreting slope, intercept, and R-squared |
| Inferential Statistics & Study Design | 20% | - Hypothesis testing framework and interpretation - Sampling methods and bias - Confidence intervals for means/proportions - Observational studies vs experiments |
| Probability Concepts | 20% | - Discrete and continuous probability distributions - Normal distribution and empirical rule - Conditional probability and Venn diagrams - Probability rules, independent and dependent events |
| Descriptive Statistics | 25% | - Measures of spread: range, IQR, variance, standard deviation - Graphical displays: histograms, boxplots, scatterplots - Measures of center: mean, median, mode - Types of data: categorical, discrete, continuous |
| Basic Numeracy & Algebra | 15% | - Exponents, roots, and basic formulas - Arithmetic operations, fractions, decimals, percentages - Linear equations, inequalities, graphing functions |
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NEW QUESTION # 99
Binomial distribution parameters = ?
Answer: D
Explanation:
A binomial distribution is defined by two parameters: n and p. The parameter n is the fixed number of trials, and p is the probability of success on each trial. The model applies when trials are independent, each trial has two possible outcomes, and p remains constant. For example, if X is the number of heads in 10 fair coin flips, then X follows a binomial distribution with n = 10 and p = 0.5. Option B, #, is the parameter of a Poisson distribution, used for counts of events over intervals. Option C, # and #, commonly describes a normal distribution, where # is the mean and # is the standard deviation. Option D is incorrect because the binomial model has a precise parameter structure. Identifying n and p is essential before calculating binomial probabilities or expected value. Study Guide references/topics: binomial distribution, independent trials, success probability, discrete probability.
NEW QUESTION # 100
95% confidence interval for mean = (45, 55). Which statement is correct?
Answer: A
Explanation:
A 95% confidence interval provides a plausible range of values for an unknown population mean based on sample data. The interval (45, 55) means the method used to construct the interval captures the true population mean in approximately 95% of repeated samples under the same conditions. In practical exam language, the correct interpretation is that the population mean is estimated to lie within the interval with 95% confidence. Option B states the midpoint of the interval, 50, but the question asks for the correct confidence interval interpretation, not just the center. Option C is incorrect because the population mean is not always the upper endpoint. Option D is imprecise because once an interval is computed, it is fixed; the confidence level refers to the long-run success rate of the procedure, not a changing probability attached to that exact fixed interval. Study Guide references/topics: confidence intervals, population mean, point estimate, margin of error.
NEW QUESTION # 101
In hypothesis testing, rejecting H# when true = ?
Answer: B
Explanation:
A Type I error occurs when the null hypothesis, H#, is true but the test decision rejects it. In plain statistical language, this is a false positive: the analysis concludes that there is evidence of an effect, difference, or relationship when in reality the null condition is true. The probability of making a Type I error is denoted by
#, the significance level, commonly set at 0.05. A Type II error is different; it occurs when the null hypothesis is false but the test fails to reject it. That is a false negative. Option C is incorrect because rejecting a true null is not a correct decision. Option D describes not rejecting or accepting the null, not the stated event. The critical phrase is "rejecting H# when true," which directly defines a Type I error. Study Guide references
/topics: hypothesis testing, null hypothesis, Type I error, significance level.
NEW QUESTION # 102
Z-score = ?
Answer: C
Explanation:
A z-score standardizes a raw value by expressing its distance from the mean in standard deviation units. The formula is z = (x # #)/#, where x is the observed value, # is the mean, and # is the standard deviation. The numerator x # # measures how far the value is from the mean. Dividing by # converts that distance into standard deviation units. A positive z-score means the value is above the mean, a negative z-score means the value is below the mean, and z = 0 means the value equals the mean. Option B divides the raw value by the mean and does not measure standardized distance. Option C reverses the ratio incorrectly. Option D gives only the raw deviation and fails to standardize by the standard deviation. Z-scores are central in normal distribution calculations and comparisons across different scales. Study Guide references/topics: z-score, standardization, mean, standard deviation.
NEW QUESTION # 103
If y = #4, evaluate the following expression:
(20 # y)/(5 + y)
Answer: C
Explanation:
To evaluate the expression, substitute y = #4 everywhere y appears. The numerator is 20 # y, so replacing y with #4 gives 20 # (#4). Subtracting a negative is equivalent to addition, so the numerator becomes 24. The denominator is 5 + y, so substituting #4 gives 5 + (#4) = 1. The expression therefore becomes 24/1, which equals 24. The correct answer is D. The most common error in this problem is mishandling the negative sign in the numerator. If a student incorrectly computes 20 # (#4) as 16, the resulting value would not match the correct algebraic structure. Another important check is that the denominator is not zero; here it equals 1, so the expression is defined. References/topics from the Study Guide: substitution, evaluating algebraic expressions, integers, order of operations.
NEW QUESTION # 104
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