Answer:
is valid for all values of x ≥ 0
Step-by-step explanation:
Maria has 50 plants in her garden, and 40% of them are tomato plants. How many plants are tomato plants? (thank you for helping
2 answers:
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check the first picture below.
those are the table of values we get for both, at 1 through 5 seconds.
why are both functions equal between the 4th and 5th second?
h(t) is the distance from the ground, check the second picture below, that's pretty much the graph of h(t) usually.
g(t) is the height of the object from the ground.
whilst g(t) is ever increasing, h(t) goes up, reaches a peak point, the vertex, and then goes back down, on its way down, it drops to 69 and then to 0 at the 5th second, however, between 69 and 0, it dropped first to 31 at some time, and then 30, then 29, then 27, then 25.8 and so on till it got to 0.
one of those values between 69 and 0, will match a value between 25.8 and 31.
what does all that mean in this context?
it means that at some point the baseball was at a height predicted g(t) as well as by h(t).
Answer:
0.0268 = 2.68% probability that the sample average is greater than $229,500
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal probability distribution
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.
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
Single house:
The mean appraised value of a house in this subdivision for all houses is $228,000, with a standard deviation of $8,500, which means that
Sample:
Sample of 120, so, by the central limit theorem,
What is the probability that the sample average is greater than $229,500?
This is 1 subtracted by the pvalue of Z when X = 229.5. So
By the Central Limit Theorem
has a pvalue of 0.9732
1 - 0.9732 = 0.0268
0.0268 = 2.68% probability that the sample average is greater than $229,500