Pretest for Probability and Statistics

A random variable is a variable that only takes on values from a specific set of outcomes.

A random variable is a constant that represents a specific event in a process.

A random variable is a fixed number that does not change.

A random variable is a variable that takes on numerical values determined by the outcomes of a random process.

The sample space is the most likely outcome of an experiment.

The sample space is the average of all outcomes.

The sample space is the set of all possible outcomes of a probability experiment.

The sample space is the sum of all possible outcomes.

A probability distribution is a fixed set of outcomes that cannot change.

A probability distribution is a method to calculate the average of a dataset.

A probability distribution is a graphical representation of data points.

A probability distribution is a function that describes the likelihood of different outcomes for a random variable.

The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases.

Sample means will always be normally distributed regardless of sample size.

The Central Limit Theorem applies only to populations with a normal distribution.

The Central Limit Theorem states that sample means are always equal to the population mean.

The standard deviation indicates the spread of data points around the mean in a normal distribution.

The standard deviation indicates the maximum value in a normal distribution.

The standard deviation measures the total number of data points in a distribution.

The standard deviation is the average of all data points in a normal distribution.

The mean population might be higher than PHP 180.

The true average of the population of all workbooks might indeed be PHP 180.

No, there is insufficient information. The population might be lower than PHP 180.

No, there is insufficient information. The population might be larger than PHP 180.

one-sample t-test

one-sample z-test

Pearson r

. Linear Regression

one-sample t-test

one-sample z-test

Pearson r

Linear Regression

Yes, since n = 25 the central limit theorem applies.

Yes, since n = 0.05 the central limit theorem applies.

No, since n = 25 the central limit theorem cannot be applied.

No, since the n = 0.05 the central limit theorem cannot be applied.

Yes, because n ≤ 30

Yes, because n ≥ 30.

No, because n ≤ 30.

No, because n ≥ 30