Answer:
see explanation
Step-by-step explanation:
a
f(0) means find the value of y when x = 0
That is f(0) = 1 ← the point (0, 1) on the graph
b
When f(x) = - 3 means what are the values of x corresponding to y = - 3
From the graph when y = - 3 there are 2 corresponding values of x, that is
x = - 2 or x = + 2
The solution to f(x) = - 3 is x = ± 2
The cost of 0.5 kg of bananas is 393.60 Colones as per the given conversion rates
Conversion rate of 1 USD to Costa Rican Colones = 518 Colones
The conversion rate of kg to pounds given in the question: 1 kg = 2.2025 lbs
Cost of one pound of bananas = $0.69
Bananas required to be purchased = 0.5kg
Converting 0.5kg bananas to pounds = 0.5*2.2025 = 1.10125 pounds
Cost of 1.10125 pound of bananas in dollars = 1.10125*0.69 = 0.7598
Cost of 1.1025 pounds of bananas in Colones = 0.7598*518 = 393.60 Colones
Hence, the cost is 393.60
Therefore, the cost of 0.5 kg bananas in Colones is 393.60 Colones
Learn more about conversion rate:
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50 divided by 8 = 6.25
1oz perfume = $6.25
110 divided by 17 = 6.4705
1oz perfume = $6.4705
Therefore the second option is cheaper
Answer:
A. R2 = 0.6724, meaning 67.24% of the total variation in test scores can be explained by the least‑squares regression line.
Step-by-step explanation:
John is predicting test scores of students on the basis of their home work averages and he get the following regression equation
y=0.2 x +82.
Here, dependent variable y is the test scores and independent variable x is home averages because test scores are predicted on the basis of home work averages.
The coefficient of determination R² indicates the explained variability of dependent variable due to its linear relationship with independent variable.
We are given that correlation coefficient r= 0.82.
coefficient of determination R²=0.82²=0.6724 or 67.24%.
Thus, we can say that 67.24% of total variability in test scores is explained by its linear relationship with homework averages.
Also, we can say that, R2 = 0.6724, meaning 67.24% of the total variation in test scores can be explained by the least‑squares regression line.
