Final Exam Reviews

explanatory and response variables

In any statistical study, we observe how these two variables are related to one another.

simple random sampling

Consider a sample of size n from a population of size N. This sampling method occurs when every possible sample of size n has an equally likely chance of occurring.

confounding variable

The effects of this type of explanatory variable cannot be distinguished from a second explanatory variable in a given study.

Pareto chart?

This type of bar graph has bars drawn in decreasing order of frequency or relative frequency.

skewed right

This describes a distribution in which the right tail is longer than the left tail

degrees of freedom

When calculating a sample standard deviation, rather than divide by the sample size, we divide by this quantity.

median

The five-number summary of a set of data includes the minimum value, the first quartile, the third quartile, the maximum value, and this quantity.

mean

This measure of central tendency is not resistant.

95%

According to the Empirical Rule, approximately this percentage of data in a normal distribution lies within two standard deviations of the mean.

68%

According to the Empirical Rule, approximately this percentage of data in a normal distribution lies within one standard deviations of the mean.

99.7%

According to the Empirical Rule, approximately this percentage of data in a normal distribution lies within three standard deviations of the mean.

interquartile range

To find outliers in a set of data, multiply this quantity by 1.5 and either subtract from the first quartile or add to the third quartile.

0

The closer the correlation coefficient is to this quantity, the less evidence there is of a linear relation.

residuals

A least-squares regression line minimizes the sum of these values on a scatterplot.

25%

A correlation coefficient of 0.5 means that this percentage of the variation in the given data is explained by the least-squares regression line.

Simpson’s Paradox

Is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.

1

The closer P(E) is to this amount, the more likely an event E is to occur.

E={HT,TH}

When flipping a coin twice in a row, this is the complement of the event E = {HH, TT}.

relative frequency

According to the Empirical Method, we can use this quantity regarding an event E to estimate the probability of E.

independence

Mutual exclusivity refers to events that occur simultaneously, while this refers to events that occur one after another.

expected value

The mean of a random variable is more commonly referred to with this name.

1

In a discrete probability distribution, the sum of the probabilities must equal this amount.

success

A binomial random variable represents the number of times this phenomenon occurs.

normal

As the number of trials in a probability experiment increases, the binomial probability distribution looks more like this type of probability distribution.

standard deviation

This quantity of a binomial probability distribution is found by finding the sample size, probability of success, and probability of failure and calculating the square root of the product

1/2

This is the area under the normal curve to the right of the mean.

z-scores

The probability that a continuous random variable falls in a given interval is determined by the position of these values, unique to a normal distribution.

normal probability plot

This type of scatterplot shows whether a given set of data is normally distributed.

normal distribution

According to the Central Limit Theorem, the sampling distribution of the sample mean becomes closer to this type of distribution as the sample size increases, regardless of the shape of the underlying population.

standard error of the mean

This is another name given to the standard deviation of a sampling distribution of the sample mean, which is calculated as the quotient of the population standard deviation and the square root of the size of each sample.

mean

The population proportion represents this quantity of a sampling distribution of the sample proportion.

spread

To describe any sampling distribution, you must know its shape, its center, and this quality.

PE +- ME

This is the formula for any confidence interval given a point estimate (PE) and margin of error (ME).

Z-score

When performing a hypothesis test to determine a population proportion (or the difference between two population proportions), we use this type of value as our test statistic.

the difference between standard deviations

The advantage to using Welch's formula to calculate degrees of freedom for the difference between two population means is that Welch's formula takes this into account.

T-value

The classical method for testing a hypothesis for a population mean (or the difference between two population means) is based on the position of this type of test statistic.