Simplifying √196 before doing the multiplication:
( √196 ÷ 7 ) × √48
= ( (√4 × √49) ÷ 7 ) × √48
= ( (2 × 7) ÷ 7 ) × √48
= 2√48
Simplifying √48:
= 2 × √16 × √3
= 2 × 4 × √3
= 8√3
which is irrational because it's a square root
Answer:
y=4
Step-by-step explanation:
4✖️4+3=19
Answer: The correct answer is option C: Both events are equally likely to occur
Step-by-step explanation: For the first experiment, Corrine has a six-sided die, which means there is a total of six possible outcomes altogether. In her experiment, Corrine rolls a number greater than three. The number of events that satisfies this condition in her experiment are the numbers four, five and six (that is, 3 events). Hence the probability can be calculated as follows;
P(>3) = Number of required outcomes/Number of possible outcomes
P(>3) = 3/6
P(>3) = 1/2 or 0.5
Therefore the probability of rolling a number greater than three is 0.5 or 50%.
For the second experiment, Pablo notes heads on the first flip of a coin and then tails on the second flip. for a coin there are two outcomes in total, so the probability of the coin landing on a head is equal to the probability of the coin landing on a tail. Hence the probability can be calculated as follows;
P(Head) = Number of required outcomes/Number of all possible outcomes
P(Head) = 1/2
P(Head) = 0.5
Therefore the probability of landing on a head is 0.5 or 50%. (Note that the probability of landing on a tail is equally 0.5 or 50%)
From these results we can conclude that in both experiments , both events are equally likely to occur.
Answer:
At a level of 95%, it is expected that the interval [0.45; 11.59] contains the value of the ductility in steel when its carbon content is 0.5%.
Step-by-step explanation:
Hello!
Considering the dependent variable:
Y: Ductility in steel.
And the independent variable:
X: Carbon content of the steel.
The linear regression was estimated and a prediction interval was calculated.
The prediction interval is calculated to predict a value that the variable Y (response variable) can take for a given value of the variable X (predictor variable) in the definition range of the linear regression line. Symbolically [Y/X=
]
In this case 95% prediction interval for Y/X=0.5
At a level of 95%, it is expected that the interval [0.45; 11.59] contains the value of the ductility in steel when its carbon content is 0.5%.
I hope it helps!