Means
Which of the following is a method for collecting data that could be used to determine the average height of students in a high school?
Calculating a z-score
Conducting a survey
Computing the standard deviation
Performing a hypothesis test
Why is considering variability important when interpreting a correlation coefficient between two variables?
High variability can weaken apparent relationships shown by correlation coefficients.
Variability ensures that all data pairs are equally weighted in correlation calculations.
Correlation coefficients automatically adjust for differences in variability.
Variability impacts correlation coefficient values differently for each variable.
What is the most appropriate measure of center for a symmetric, bell-shaped distribution?
Range
Mode
Median
Mean
When analyzing paired data for two quantitative variables, which of the following would provide information on the strength and direction of a linear relationship between them?
Stacked bar graph displaying distributions
Two pie charts with percentages
Bar graph showing frequency counts
Scatterplot with a correlation coefficient
What does it mean when data points on a residual plot are randomly scattered around ?
Data points show clear patterns suggesting strong autocorrelation among residuals.
There exists an exponential trend in our dataset requiring transformation.
The model's assumptions may be valid and residuals appear without pattern.
Residuals are showing signs of increasing variability indicating non-constant variance.
When comparing the variability of two distributions, which of the following measures would be most appropriate to use?
Mean
Median
Standard deviation
Mode
If a factory produces bulbs with a defect rate of %, what is the probability that in a sample of bulbs at least one will have a defect?
A common error would be to multiply the defect rate directly by the sample size.
The probability of one defective bulb does not equate to the probability of at least one.
The complementary probability method means calculating and subtracting it from 1.
Although the events are dependent, simply multiplying the initial defect rate does not yield correct results.

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When selecting a procedure for inferential statistics, what is generally the first question you should ask?
Can I use a chi-square test?
Are the groups related or independent?
Do I have one or two samples?
Am I dealing with means or proportions?
If a linear regression model has a high R-squared value on the training data but performs poorly on new, unobserved data, what could be the reason for this discrepancy?
Independence of observations.
Lack of multicollinearity in predictor variables.
Homoscedasticity of residuals.
Overfitting to the training data.
When comparing two regression models using residual plots from different datasets, which pattern best indicates that one model may be more robust than another?
The model with higher variance in residuals at extreme values of predictors.
The model with randomly scattered residuals around zero without any systematic pattern.
The model with clusters of residuals far away from zero.
The model with residuals that show a clear linear trend.