1. For x<3, h(x)=x+2
now x+2 is an increasing line (a practical way to check, h(1)=3, h(2)=4)
the largest value it takes is at 3, not inclusive, so h(3)=5 not inclusive.
Range_1= (-∞, 5)
2. for x> or equal to 3, h(x)=-x+8, which is a decreasing line (check h(4)=4, h(5)=3 )
so this line takes its maximal value at x=3, f(3)=5 and then takes any other value to -∞.
Range_2=(-∞,5]
3. Range(h)=Range1∪Range2=(-∞, 5)∪(-∞,5]=(-∞,5] (B)
Answer: 93.6 hrs
Step-by-step explanation: 8hr ÷ 6yards = 1.3 yards per hr.
72 yards × 1.3 yards per hr = 93.6 hrs
An equation that models the proportional relationship between t and s.
t = 12.5s
<h3>What is proportional relationship?</h3>
Relationships between two variables where their ratios are equal are known as proportional relationships. The fact that one variable is always a constant value multiplied by the other in a proportionate connection is another way to conceive of them. This parameter is referred to as the "constant of proportionality."
<h3>According to the given information :</h3>
1) Ticket for an art museum costs $12.50
2) In other words, the price will increase by $12.50 for each ticket purchased (or tickets).
In other words, the total expense is always 12.5 times the quantity of tickets sold.
3) This indicates that we can create this ---> t = 12.5s.
An equation that models the proportional relationship between t and s.
t = 12.5s
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Answer:
Area= 32 (formula is: length x width)
Perimeter= 8 + 4 + 8 + 4= 24
Step-by-step explanation:
The statement that 99% of all confidence intervals with a 99% confidence level should contain the population parameter of interest is false.
A confidence interval (CI) is essentially a range of estimates for an unknown parameter in frequentist statistics. The most frequent confidence level is 95%, but other levels, such 90% or 99%, are infrequently used for generating confidence intervals.
The confidence level is a measurement of the proportion of long-term associated CIs that include the parameter's true value. This is closely related to the moment-based estimate approach.
In a straightforward illustration, when the population mean is the quantity that needs to be estimated, the sample mean is a straightforward estimate. The population variance can also be calculated using the sample variance. Using the sample mean and the true mean's probability.
Hence we can generally infer that the given statement is false.
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