AP Statistics Symbols

Directions

• For this assignment you will need to memorize symbols and what they represent. You will NOT need to memorize the definition of the symbol, only what it represents.
• During the first week of school, there will be a retakable quiz. In the quiz, you will be given either the symbol or what it represents and you will be expected to produce the other.

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• The quiz will not be on the first day of school. So, if you have questions about the quiz, you can ask them then.
• THE NEXT SLIDE CONTAINS AN EXAMPLE OF HOW THE SLIDES ARE SET UP.

(What the symbol represents)

(symbol)

(definition)

Sample Mean

The arithmetic average. The mean of a sample. To find the mean of a set of observations, add their values and divide them by the number of observations or the size of the sample (n)

Population Mean

The average of a variable for the entire population NOT just a sample. In statistics, it’s actually rare that you can calculate the population mean. That’s because asking an entire population about something is cost prohibitive or too time consuming.

Sample Standard Deviation

Dd OR

Statistics that measures the typical distance of the values in a sample from the mean. It is calculated by finding the “average” of the squared distances and then taking the square root.

Population Standard Deviation

The standard deviation of a variable for the entire population NOT a just a sample. In statistics, it’s actually rare that you can calculate the population standard deviation. That’s because asking an entire population about something is cost prohibitive or too time consuming.

Sample Variance

Sample Variance measures how far sample data set is spread out. The technical definition is “The average of the squared differences from the mean.” but all it really does is to give you a very general idea of the spread of your data. A value of zero means that there is no variability; All the numbers in the data set are the same.

Population Variance

Population Variance measures how far all values in the population are spread out. In statistics, it’s actually rare that you can calculate the population variance. That’s because asking an entire population about something is cost prohibitive or too time consuming.

Sample Size

The size of the sample that is selected. The number of pieces of data collected.

Population Size

The number of individuals in the entire group we want information about. This number value is not always known because it is difficult and time consuming to get an accurate count.

Sample Proportion (of success)

The proportion of people that give a positive response from the the sample you are investigating/ collecting data from.

Population Proportion (of success)

The proportion of people that give a positive response from the entire population. In statistics, it’s actually rare that you can calculate the population proportion. That’s because asking an entire population about something is cost prohibitive or too time consuming.

Sample Proportion of Failure

The proportion of people that give a negative response from the the sample you are investigating/ collecting data from.

Population Proportion of Failure

The proportion of people that give a negative response from the entire population. In statistics, it’s actually rare that you can calculate the population proportion of failure. That’s because asking an entire population about something is cost prohibitive or too time consuming.

Sigma (sum of)

To sum or add the following values.

Level of Significance

OR Probability of Making a Type I Error

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(alpha)

The probability of rejecting a null hypothesis when the null hypothesis is actually true.

Probability of Making a Type II Error

The probability of failing to reject a null hypothesis when it is actually false.

Correlation Coefficient

Measure of the direction and strength of a relationship between two quantitative variables.

Coefficient of Determination

The fraction of the variation in the values o y that is accounted for by the least-squares regression line (linear relationship) of y on x.

Predicted y

The value predicted by the least-square regression line.

y-intercept

The value of the predicted-y from the least square regression line when the value for x is equal to zero.

Slope

The amount by which y is predicted to change when x increase by a single or 1 unit.

Least-Squares Regression Line

The line that makes the sum of the squared vertical distances of the data points from the line as small as possible. Also known as “the line of best fit”.