EXST 2201 Exam 2 Questions and
Answers Graded A+
What is the purpose of hypothesis testing in statistics?
The purpose of hypothesis testing is to determine whether there is enough evidence in a
sample to support a specific claim or hypothesis about a population.
What does a Type I error ...
EXST 2201 Exam 2 Questions and
Answers Graded A+
What is the purpose of hypothesis testing in statistics?
✔✔The purpose of hypothesis testing is to determine whether there is enough evidence in a
sample to support a specific claim or hypothesis about a population.
What does a Type I error represent in hypothesis testing?
✔✔A Type I error occurs when the null hypothesis is incorrectly rejected when it is actually
true, leading to a false positive conclusion.
What is a Type II error?
✔✔A Type II error occurs when the null hypothesis is incorrectly accepted when it is false,
resulting in a false negative conclusion.
How can the significance level (alpha) be set in a study?
✔✔The significance level can be set based on the research context, commonly using 0.05, 0.01,
or 0.10, which reflects the acceptable probability of making a Type I error.
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,What role does the p-value play in hypothesis testing?
✔✔The p-value indicates the probability of observing the data, or something more extreme,
assuming the null hypothesis is true, helping to determine statistical significance.
What is the difference between one-tailed and two-tailed tests?
✔✔A one-tailed test assesses the possibility of an effect in one direction, while a two-tailed test
evaluates the possibility of effects in both directions.
What is the significance of the confidence interval in statistics?
✔✔A confidence interval provides a range of values within which we can be confident that the
true population parameter lies, reflecting the uncertainty around our estimate.
How is the margin of error calculated in a confidence interval?
✔✔The margin of error is calculated by multiplying the critical value (based on the desired
confidence level) by the standard error of the sample statistic.
What does it mean for a sample to be independent in statistical testing?
✔✔Independence in a sample means that the selection of one individual does not influence the
selection of another, ensuring that the data points are not related.
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,What is the purpose of ANOVA (Analysis of Variance)?
✔✔ANOVA is used to compare means among three or more groups to determine if at least one
group mean is statistically different from the others.
What assumptions must be met for ANOVA to be valid?
✔✔ANOVA assumes that the samples are independent, the populations have equal variances,
and the data is normally distributed.
How can the results of ANOVA be interpreted?
✔✔Results of ANOVA can be interpreted by examining the F-statistic and associated p-value; a
significant p-value indicates that at least one group mean is different.
What is a post hoc test, and when is it used?
✔✔A post hoc test is used after ANOVA to identify which specific group means are
significantly different from each other when the overall test shows significant results.
What is the purpose of regression analysis in statistics?
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, ✔✔Regression analysis aims to examine the relationship between one or more independent
variables and a dependent variable, allowing predictions and insights about relationships.
What is the difference between simple linear regression and multiple linear regression?
✔✔Simple linear regression involves one independent variable predicting a dependent variable,
while multiple linear regression involves two or more independent variables.
What does the coefficient of determination (R-squared) represent?
✔✔R-squared represents the proportion of variance in the dependent variable that can be
explained by the independent variables in the model.
What are the assumptions of linear regression analysis?
✔✔Assumptions include linearity, independence of errors, homoscedasticity (constant variance
of errors), and normality of error distribution.
How can outliers affect regression analysis?
✔✔Outliers can disproportionately influence the slope of the regression line, leading to
inaccurate estimates and potentially skewed results.
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