1. Compare the model you wrote in Part 1 with the regression model you found in Part 2. Which do you think is a better model? Explain. (4 points)

2. Shown is a graph of net generation wind for Texas. The graph appears to take the shape of an
exponential function. Would you recommend using a logistic function, like the model for Kansas, or an
exponential function for this data? (3 points)

3. One logistic equation to model the California data is the equation found using regression. 21,430.4 1 + 7.27265e-0.160197x Which state, Kansas or California, had a greater number of years in which the growth of wind energy was increasing? Explain. (3 points)

hello,

b) 62.47% of the students are between 14 and 18 years old.

step-by-step explanation:

mean = 15 and standard deviation = 2

student between 14 to 18

formula used z-score

for age 14 , z score

using z-table , p(z> -0.5)=0.3085

for age 18 , z score

using z-table , p(z< 1.5)=0.9332

p(-0.5< z< 1.5)=0.9332-0.3085 = 0.6247

thus, 62.47% of the students are between 14 and 18 years old.

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step-by-step explanation:

a orange at the grocery store idk what other product that could be

i don't know

step-by-step explanation:

answer: it actually depends on which is the independent variable and which is the dependent variable. let us say we're looking at a linear equation. y=2x+3. we theoretically could switch the equation to be x=(y-3)/2 and it would be the same line. so either a causes b or it can be that b causes a. so technically the answer is false

step-by-step explanation: