Answer:
See below ~
Step-by-step explanation:
<u>(a) Mean of the data</u>
- 2.4 + 1.6 + 3.2 + 0.3 + 1.5 / 5
- 9/5
- <u>1.8</u>
<u></u>
<u>(b) New mean after each data point increased by 10</u>
- 12.4 + 11.6 + 13.2 + 10.3 + 11.5 / 5
- 59/5
- <u>11.8</u>
<u></u>
<u>(c) New mean after each data point doubled [from (b)]</u>
- 24.8 + 23.2 + 26.4 + 20.6 + 23 / 5
- 118/5
- <u>23.6</u>
Answer: Y= -47/5
Step-by-step explanation:
Isolate the variable by dividing each side by factors that don't contain the variable.
Answer: c) About 16% of the variation in value of the car is explained by a linear relationship with the age of the car.
e) The correlation coefficient, r, is 0.397.
Step-by-step explanation:
Given that:
Coefficient of determination (r²) between two variables, age of car (x) and value of car (y) = 0.158
Correlation of determination (r²) of 0.158 = (0.158 × 100% = 15.8% of the variation between the two variables can be explained by the regression line). Hence, about 16% of the variation between age and value of car can be explained by the linear relationship.
Coefficient of correlation (r) = sqrt(r²) = sqrt(0.158) = 0.397
The numbers of choices in each category are multiplied together. We assume the order of paint choices matters: using color 1 in area A and color 2 in area B is not the same as using color 2 in area A and color 1 in area B.
P(7,2)*4*3*2 = 42*4*3*2 = 1008 ways
_____
P(n, k) = n!/(n-k)!
P(7, 2) = 7*6 = 42
