The number of M&Ms in a 6 oz bag is normally distributed with a mean of 18.45 and a standard deviation of 1.95. In order to construct a 95% confidence interval for the true population mean number of M&Ms in a bag with an error of less than 0.5, how many bags of M&Ms am I going to have to eat
The number of M&Ms in a 6 oz bag is normally distributed with a mean of 18.45 and a standard deviation of 1.95. In order to construct a 95% confidence interval for the true population mean number of M&Ms in a bag with an error of less than 0.5, how many bags of M&Ms am I going to have to eat
The number of M&Ms in a 6 oz bag is normally distributed with a mean of 18.45 and a standard deviation of 1.95. In order to construct a 95% confidence interval for the true population mean number of M&Ms in a bag with an error of less than 0.5, how many bags of M&Ms am I going to have to eat
The number of M&Ms in a 6 oz bag is normally distributed with a mean of 18.45 and a standard deviation of 1.95. In order to construct a 95% confidence interval for the true population mean number of M&Ms in a bag with an error of less than 0.5, how many bags of M&Ms am I going to have to eat? (and how sick am I gonna get!?!)
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
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