POINT AND INTERVAL ESTIMATES OF POPULATION PARAMETERSA point estimate is a single number that is our best guess for the parameter (e.g: 25% of all Dutch people are above 1,80m). An interval estimate is an interval of numbers within which the parameter value is believed to fall (e.g: between 20% and 30% of the Dutch people are above 1,80m). The margin of error gives the lower border and the upper border of the margin. A good estimator of a parameter has two properties:Unbiased A good estimator has a sampling distribution that is centred at the parameter. A mean from a random sample should fall around the population parameter and this is especially the case with multiple samples and thus a sampling distribution.Small standard deviationA good estimator has a small standard deviation compared to other estimators. The sample mean is preferred over the sample median, even in a normal distribution, because the sample mean has a smaller standard deviation. An interval estimate is designed to contain the parameter with some chosen probability, such as 0.95. Confidence intervals are interval estimates that contain the parameter with a certain degree of confidence. A confidence interval is an interval containing the most believable values for a parameter. The probability that this method produces an interval that contains the parameter is called the confidence level. A sampling distribution of a sample proportion gives the possible values for the sample proportion and their probabilities and is a normal distribution if np is larger than 15 and n(1-p) is larger than 15. The margin of error measures how accurate the point estimate is likely to be in estimating a parameter. CONSTRUCTING A CONFIDENCE INTERVAL TO ESTIMATE A POPULATION PROPORTIONThe point estimate of the population proportion is the sample proportion. The standard error is the estimated standard deviation of a sampling distribution. The formula for the standard error is:The greater...