The length is 19.5 feet. this is because when you add the yards, it equals 15 feet. Next, add 1 + 2 feet from the first and second jump. 10+8 inches equal 1.5 feet. Next, add 15 feet+3 feet+1.5 feet to get 19.5 feet.
Answer:
The proportion of infants with birth weights between 125 oz and 140 oz is 0.1359 = 13.59%.
Step-by-step explanation:
When the distribution is normal, we use the z-score formula.
In a set with mean
and standard deviation
, the zscore of a measure X is given by:

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
In this question, we have that:

The proportion of infants with birth weights between 125 oz and 140 oz is
This is the pvalue of Z when X = 140 subtracted by the pvalue of Z when X = 125. So
X = 140



has a pvalue of 0.9772
X = 125



has a pvalue of 0.8413
0.9772 - 0.8413 = 0.1359
The proportion of infants with birth weights between 125 oz and 140 oz is 0.1359 = 13.59%.
Answer:
Cost to rent one tank=226.08 Dollars
Step-by-step explanation:
Radius of the cylindrical tank=3 feet
Height of the tank= 2 feet
Cost to rent one tank= Volume of one tank * cost to rent one cubic feet of volume
Volume of the cylindrical tank:
=
As, 
Putting the values in the formula:
Volume of the tank:

Cost to rent one cubic feet= 4 Dollars
Cost to rent 56.52 Cubic feet of one tank;
= 4*56.52
=226.08 Dollars
Answer:
Answer = d. Chi-Square Goodness of Fit
Step-by-step explanation:
A decision maker may need to understand whether an actual sample distribution matches with a known theoretical probability distribution such as Normal distribution and so on. The Goodness-of-fit Test is a type of Chi-Square test that can be used to determine if a data set follows a Normal distribution and how well it fits the distribution. The Chi-Square test for Goodness-of-fit enables us to determine the extent to which theoretical probability distributions coincide with empirical sample distribution. To apply the test, a particular theoretical distribution is first hypothesized for a given population and then the test is carried out to determine whether or not the sample data could have come from the population of interest with hypothesized theoretical distribution. The observed frequencies or values come from the sample and the expected frequencies or values come from the theoretical hypothesized probability distribution. The Goodness-of-fit now focuses on the differences between the observed values and the expected values. Large differences between the two distributions throw doubt on the assumption that the hypothesized theoretical distribution is correct and small differences between the two distributions may be assumed to be resulting from sampling error.