AP Statistics Unit 1 Review

What is a stemplot?

• A method of displaying quantitative data in a graphical format to retain the original data values while showing their distribution.

• A type of chart that only displays categorical data without any numerical values.

• A graphical representation of data that uses bars to show frequencies of categories.

• A plot that only shows the mean and median of a dataset without any distribution information.

What does it mean if the mean is greater than the median in a distribution?

• The distribution is typically left-skewed, meaning there are a few low values that pull the mean down.

• The distribution is typically normal, meaning the mean and median are equal.

• The distribution is typically right-skewed, meaning there are a few high values that pull the mean up.

• The distribution is uniform, meaning all values are equally likely.

How do you interpret a 75th percentile?

• A 75th percentile means that 75% of the data points fall below this value, indicating it is higher than the majority of the data.

• A 75th percentile indicates that 75% of the data points are above this value, showing it is lower than most of the data.

• A 75th percentile means that 75% of the data points are equal to this value, indicating it is an average value.

• A 75th percentile indicates that 75% of the data points are irrelevant to the analysis.

What is a box plot?

• A graphical representation of data using bars to show frequency.

• A standardized way of displaying the distribution of data based on a five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum.

• A type of chart that shows the relationship between two variables.

• A method for calculating the average of a dataset.

What is the central limit theorem?

• The central limit theorem states that the sampling distribution of the sample mean will approach a normal distribution as the sample size increases, regardless of the shape of the population distribution.

• The central limit theorem states that all samples will have the same mean regardless of the population distribution.

• The central limit theorem states that larger samples will always yield more accurate results than smaller samples.

• The central limit theorem states that the mean of the population will always equal the mean of the sample.

What does it mean for a distribution to be skewed?

• A skewed distribution has data points symmetrically distributed around the mean.

• A skewed distribution is one where the data points are not symmetrically distributed around the mean, leading to a longer tail on one side.

• A skewed distribution has equal data points on both sides of the mean.

• A skewed distribution is always a normal distribution with a slight variation.

What is the purpose of using percentiles in statistics?

• To calculate the mean of a data set.

• To understand the relative standing of a value within a data set, indicating how a particular score compares to the rest of the data.

• To determine the mode of a data set.

• To find the range of a data set.

How do you calculate percentile rank?

• Percentile rank is calculated by determining the percentage of scores in a distribution that are equal to or below a particular score. The formula is: (Number of values below the score + 0.5 * Number of values equal to the score) / Total number of values * 100.

• Percentile rank is calculated by taking the average of all scores in a distribution and dividing it by the highest score.

• Percentile rank is determined by counting the number of scores above a particular score and dividing it by the total number of scores.

• Percentile rank is calculated by subtracting the lowest score from the highest score and dividing by the total number of scores.

What is the relationship between mean and median in a symmetrical distribution?

• The mean is always greater than the median.

• The mean and median are equal.

• The mean is always less than the median.

• The mean and median are unrelated.

What does a normal distribution look like?

• A normal distribution is bell-shaped and symmetrical, with most of the data points clustering around the mean and fewer points as you move away from the mean.

• A normal distribution is skewed to the left, with more data points on the right side.

• A normal distribution has a uniform shape, with data points evenly distributed across all values.

• A normal distribution is a straight line, indicating no variation in data points.