I am puzzled by this. If, as you appear to be saying, youare not interested in learning HOW to solve problems like this, why would you care about "the answer and solutions"?Cute aq said:1. The weight of goats at a farm is normally distributed with a mean of 60 kg and a standard deviation of 10 kg. A truck used to transport goats can only accommodate not more than 650 kg. If 10 goats are selected at random from the population, what is the probability that the total weight exceeds the maximum weight?
PS: I JUST NEED THE ANSWER AND SOLUTIONS
Country Boy said:I am puzzled by this. If, as you appear to be saying, youare not interested in learning HOW to solve problems like this, why would you care about "the answer and solutions"?
A sampling distribution is a theoretical distribution that represents the possible values of a sample statistic, such as the mean or standard deviation, if an infinite number of random samples were taken from a population. It is used to make inferences about the population based on the sample data.
Sampling distribution is important because it allows us to estimate population parameters and make inferences about the population based on sample data. It also helps us understand the variability of sample statistics and the accuracy of our estimates.
The population distribution represents the distribution of values for a specific variable in the entire population, while the sampling distribution represents the distribution of values for a sample statistic, such as the mean or standard deviation, in all possible samples of a given size from the population.
The shape of a sampling distribution can be affected by the sample size, the variability of the population, and the sampling method used. Generally, as the sample size increases, the sampling distribution becomes more normal. A larger variability in the population can result in a wider and more spread out sampling distribution. Different sampling methods, such as random sampling or stratified sampling, can also lead to different shapes of the sampling distribution.
A sampling distribution can be calculated by taking multiple random samples from a population and calculating the sample statistic of interest, such as the mean or standard deviation, for each sample. These sample statistics are then plotted on a graph to create the sampling distribution. Alternatively, mathematical formulas can also be used to calculate a sampling distribution based on the population parameters and sample size.