Step 1

Point Estimator :

Point estimators are functions that are used to find an approximate value of a population parameter from random samples of the population. They use the sample data of a population to calculate a point estimate or a statistic that serves as the best estimate of an unknown parameter of a population.

Most often, the existing methods of finding the parameters of large populations are unrealistic. For example, when finding the average age of kids attending kindergarten, it will be impossible to collect the exact age of every kindergarten kid in the world. Instead, a statistician can use the point estimator to make an estimate of the population parameter.

Step 2

\(\displaystyle\mu_{{1}}−\mu_{{2}}-\mu_{{1}}\) : mean of Manufacturer 1

\(\displaystyle\mu_{{2}}\) : mean of Manufacturer 2

\(\displaystyle\mu_{{1}}-\mu_{{2}}\) : mean difference

Point Estimator :

Point estimators are functions that are used to find an approximate value of a population parameter from random samples of the population. They use the sample data of a population to calculate a point estimate or a statistic that serves as the best estimate of an unknown parameter of a population.

Most often, the existing methods of finding the parameters of large populations are unrealistic. For example, when finding the average age of kids attending kindergarten, it will be impossible to collect the exact age of every kindergarten kid in the world. Instead, a statistician can use the point estimator to make an estimate of the population parameter.

Step 2

\(\displaystyle\mu_{{1}}−\mu_{{2}}-\mu_{{1}}\) : mean of Manufacturer 1

\(\displaystyle\mu_{{2}}\) : mean of Manufacturer 2

\(\displaystyle\mu_{{1}}-\mu_{{2}}\) : mean difference