BESC 3010 Chpt 9 Questions and Answers | Latest Version | 2024/2025 | Rated A+
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Module
BESC 3010
Institution
BESC 3010
BESC 3010 Chpt 9 Questions and
Answers | Latest Version | 2024/2025 |
Rated A+
What is the central limit theorem, and why is it important in statistics?
The central limit theorem states that the distribution of sample means will approximate a
normal distribution as sample size increases, r...
BESC 3010 Chpt 9 Questions and
Answers | Latest Version | 2024/2025 |
Rated A+
What is the central limit theorem, and why is it important in statistics?
✔✔The central limit theorem states that the distribution of sample means will approximate a
normal distribution as sample size increases, regardless of the population’s distribution. This is
essential because it allows statisticians to make inferences about population parameters using
sample data.
Explain the concept of p-value and how it is interpreted in hypothesis testing.
✔✔A p-value indicates the probability of obtaining results as extreme as those observed,
assuming the null hypothesis is true. A low p-value (typically ≤ 0.05) suggests strong evidence
against the null hypothesis, leading to its rejection.
What is a type II error, and how does it differ from a type I error?
✔✔A type II error occurs when we fail to reject a false null hypothesis, missing an effect that
actually exists. This differs from a type I error, where a true null hypothesis is incorrectly
rejected.
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, Describe the purpose and interpretation of a confidence interval.
✔✔A confidence interval provides a range of values within which the population parameter is
likely to fall, with a specified level of confidence (e.g., 95%). It gives an estimate of precision
around a sample statistic.
How does a one-sample t-test differ from an independent samples t-test?
✔✔A one-sample t-test compares the mean of a single sample to a known population mean,
while an independent samples t-test compares the means of two independent groups to see if they
differ significantly.
What is homoscedasticity, and why is it important in regression analysis?
✔✔Homoscedasticity refers to equal variance in the residuals across all levels of an independent
variable. It's crucial in regression because unequal variances (heteroscedasticity) can affect the
reliability of predictions and statistical conclusions.
In what scenario would you use a chi-square test, and what does it assess?
✔✔A chi-square test is used to assess relationships between categorical variables. It evaluates
whether observed frequencies differ significantly from expected frequencies, testing for
independence between variables.
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