Statistics Unit 1 & 2 Quiz

A study where researchers observe and collect data without manipulating any variables. It aims to describe and understand existing relationships but cannot establish cause and effect.

A study where researchers manipulate one or more variables (called treatments) to see their effect on another variable. It allows for determining cause and effect relationships.

Each member of the population has an equal chance of being selected. It provides a representative sample.

The population is divided into subgroups (strata) based on a characteristic. A random sample is then taken from each stratum.

The population is divided into clusters (natural groupings). A random sample of clusters is selected, and all individuals within the selected clusters are included in the sample.

A multistage sample involves multiple stages of random sampling. For example, you might randomly select states, then counties within those states, and finally schools within those counties.

A sample that is convenient to access. It is often biased and not representative of the population.

The mean of the sum of two random variables is simply the sum of their individual means.

A graphical representation of data where each data point is represented by a dot above a numerical scale. Useful for visualizing the distribution of data points and identifying outliers.

The value that divides the data set in half when ordered from least to greatest.

A numerical value that describes the spread of data around the mean. It measures the average distance of each data point from the mean.

A line that best represents the relationship between two variables in a scatterplot. It is calculated using the least squares regression method, minimizing the sum of squared residuals.

A statistical measure that describes the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, with 1 indicating a perfect positive correlation, -1 indicating a perfect negative correlation, and 0 indicating no correlation.

A plot of the residuals (the difference between predicted and actual values) against the independent variable. It helps assess the quality of the linear regression model. A good fit shows a random scatter of residuals, while a bad fit shows patterns.

Data points that fall far from the trend of the majority of data points in a scatterplot. These points can have a significant impact on the LSRL and correlation.

Points in a scatterplot that have an extreme value on the independent variable (x-axis). High leverage points can have a large influence on the LSRL, especially if they are far from the central trend of the data.

A random variable that counts the number of successes in a fixed number of independent trials, where each trial has the same probability of success.

A variable that can be measured numerically.

A variable that classifies data into categories.

A design where all subjects are randomly assigned to different treatment groups.

A design where subjects with similar characteristics are grouped together and then randomly assigned to treatment groups.