Step-by-step explanation:
To find the radius of the sphere we must convert the inches to centimeters
Using the conversation
1 inch = 2.54 cm
If 1 inch = 2.54 cm
4.17 inch = 2.54 × 4.17 = 10.59 cm
<h2>a).</h2>
We can find the radius using the formula
From the question
diameter = 10.59 cm
So we have
<h3>radius = 5.30 cm</h3>
<h2>b).</h2>
Surface area of a sphere= 4πr²
where
r is the radius
Surface area = 4(5.30)²π
= 112.36π
= 352.989
We have the answer as
<h3>Surface area = 353 cm²</h3>
<h2>c).</h2>
Volume of a sphere is given by
r = 5.30
The volume of the sphere is
= 623.61451
We have the answer as
<h2>Volume = 623.6 cm³</h2>
Hope this helps you
Answer:
the answer is c, science CANNOT answer all questions
(-6)(1)∧⁽2/-7⁾= -6 is the answer
Hope it's right
Answer:
The ferris wheel travelled 434.717 ft.
Step-by-step explanation:
Assuming that the ferris wheel is a perfect circle, then it's height is the same as the diameter of the circle, therefore it's radius is:
radius = height/2 = 246/2 = 123 ft
The distance travelled by the ferris wheel is the same as the length of the arc created by a angle of (9pi/8). The length of an arc is given by:
length = r*(angle in radians)
length = 123*(9pi/8)
length = 434.717 ft
The ferris wheel travelled 434.717 ft.
Answer:
A. True
Step-by-step explanation:
Linear regression is "an analysis that assesses whether one or more predictor variables explain the dependent (criterion) variable. The regression has five key assumptions:
1) Linear relationship
: We need to check if the dependnet variable present a linear relationshipThe linearity assumption can best be tested with scatter plots in order to check if we have outliers in the data.
2) Multivariate normality
: "The linear regression analysis requires all variables to be multivariate normal". And we can check this with a histogram or a Q-Q-Plot, usually Normality can be checked with a goodness of fit test like the Kolmogorov-Smirnov test or Shapiro Wilks test.
3) No or little multicollinearity
: "Multicollinearity occurs when the independent variables are too highly correlated with each other". And we can check this with a correlation matrix, variance inflation factor (VIF's), correlation index and other statistics.
4) No auto-correlation
: The "Autocorrelation happens when the residuals are not independent from each other in the data". And usually we can test this with the Durbin-Watson test.
5) Homoscedasticity: MEans that we need constant variance for the linear model. The scatter plot is good way to check whether the data are homoscedastic. And we can interpret this condition as "that variance in the response variable is reasonably consistent across the range of an explanatory factor (otherwise known as homoscedasticity)"
So then the statement is TRUE.
