Population Mean:
The population mean formula, denoted by µ, is used to calculate the average of a given population. It is the sum of all values in the population divided by the total number of values.

Sample Mean:
The sample mean formula, denoted by x̄, is similar to the population mean formula. It calculates the average of a sample, which is a subset of the population. It is calculated by summing up all values in the sample and dividing by the sample size.

Population Variance:
The population variance formula, denoted by σ², measures the spread or dispersion of values in a population. It is calculated by finding the average of the squared differences between each value and the mean of the population.

Sample Variance:
The sample variance formula, denoted by s², is used to estimate the population variance using a sample. It is similar to the population variance formula, but instead of dividing by the population size, it divides by the sample size minus one.

Population Standard Deviation:
The population standard deviation formula, denoted by σ, is the square root of the population variance. It measures the average amount by which values in a population differ from the mean.

Sample Standard Deviation:
The sample standard deviation formula, denoted by s, is the square root of the sample variance. It estimates the average amount by which values in a sample differ from the sample mean.

Correlation Coefficient:
The correlation coefficient formula, denoted by r, measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a perfect negative relationship, 1 indicates a perfect positive relationship, and 0 indicates no relationship.

Regression Line:
The regression line formula, also known as the line of best fit, is used to model the relationship between two variables. It is represented by the equation y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b is the y-intercept.

Probability:
The probability formula calculates the likelihood of an event occurring. It is the ratio of the number of favorable outcomes to the total number of possible outcomes.

Normal Distribution:
The normal distribution formula, also known as the Gaussian distribution, is a probability distribution that is symmetric and bell-shaped. It is characterized by its mean and standard deviation, and many statistical calculations rely on the assumption of a normal distribution.

Hypothesis Testing:
Hypothesis testing involves comparing sample data to a null hypothesis to determine if there is enough evidence to reject the null hypothesis. Various statistical tests, such as t-tests and chi-square tests, have their own formulas and calculations to assess the significance of the results.

Confidence Interval:
A confidence interval formula calculates a range of values within which a population parameter is likely to fall. It is based on the sample data and the desired level of confidence. The formula varies depending on the parameter being estimated, such as the mean or proportion.

Margin of Error:
The margin of error formula determines the range within which the true population parameter is likely to fall. It is calculated based on the sample data and the desired level of confidence. The margin of error is typically represented as a percentage or