Write an expression with four like terms. your expression should have three different variables and a constant.

Polynomial comes from poly- (meaning "many") and -nomial (in this case meaning "term") ... so it says "many terms"

A polynomial can have:

constants (like 3, −20, or ½)
variables (like x and y)
exponents (like the 2 in y2), but only 0, 1, 2, 3, ... etc are allowed
that can be combined using addition, subtraction, multiplication and division ...

... except ...

... not division by a variable (so something like 2/x is right out)

So:

A polynomial can have constants, variables and exponents,
but never division by a variable.

Also they can have one or more terms, but not an infinite number of terms.

These are polynomials:

3x
x − 2
−6y2 − ( 79 )x
3xyz + 3xy2z − 0.1xz − 200y + 0.5
512v5 + 99w5
5
(Yes, "5" is a polynomial, one term is allowed, and it can be just a constant!)

These are not polynomials

3xy-2 is not, because the exponent is "-2" (exponents can only be 0,1,2,...)
2/(x+2) is not, because dividing by a variable is not allowed
1/x is not either
√x is not, because the exponent is "½" (see fractional exponents)

But these are allowed:

x/2 is allowed, because you can divide by a constant
also 3x/8 for the same reason
√2 is allowed, because it is a constant (= 1.4142...etc)

7 0

2 years ago

d)An insurance company will only sell its Select policy to people for whom the probability of a stroke in the next 10 years is l

NARA [144]

Answer:

Check the explanation

Step-by-step explanation:

The First action is to enter the data in Excel and then save it as a csv file. Then call the data set in R using your saved path, I have made use of my computer path here in my code. please change it for yours.

Here is the code That you need to use for finding the multiple limear regression model and summary of it.

code:

data=read. csv("C:\\Users\\HP\\Desktop\\chegg. csv")

data

attach(data)

model=lm(Risk~Age+Blood.Pressure+Smoker)

summary(model)

And Here is the output:

Call:

lm(formula = Risk ~ Age + Blood.Pressure + Smoker)

Residuals:

Min 1Q Median 3Q Max

-13.1064 -1.5715 0.4225 3.4855 8.5561

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -91.75950 15.22276 -6.028 1.76e-05 ***

Age 1.07674 0.16596 6.488 7.49e-06 ***

Blood.Pressure 0.25181 0.04523 5.568 4.24e-05 ***

Smoker 8.73987 3.00082 2.912 0.0102 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.757 on 16 degrees of freedom

Multiple R-squared: 0.8735, Adjusted R-squared: 0.8498

F-statistic: 36.82 on 3 and 16 DF, p-value: 2.064e-07

The answer is now written in the notebook which i am uploading..

6 0

2 years ago