Which expressions are equivalent to 35x + 3. Select the TWO that apply.

Who knows how to solve these? I need help!!

Elina [12.6K]

1= 0.25
2=1.12
3= -075

4 0

3 years ago

A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The relationshi

maks197457 [2]

Answer:

58.6% of the variation in length (in cm) of new born boys and their weight (in kg) is explained by the line of best fit.

Step-by-step explanation:

Given the following :

R² value = 58.6% comparing the length (cm) of new born boys to their weight (kg)

The R² value is called the Coefficient of determination. It is obtained by taking the square of the correlation Coefficient (R). The value gives the proportion of Variation between the independent and dependent variables which is explained by regression line. In the scenario above, the R² value means that 58.6% of the variation in length in centimeter of new born boys and their weight (in kg) is explained by the line of best fit. While (100% - 58.6% = 41.4%) is due to other factors.

8 0

3 years ago

the polynomial ky^3+3y^2-3 and 2y^3-5y+k when divided by (y-5) leave the same remainder in each case. Find the value of k.

marshall27 [118]

Answer:

$k=\frac{153}{124}$

Step-by-step explanation:

According to the Remainder Theorem; when P(y) is divided by y-a, the remainder is p(a).

The first polynomial is :

$p(y)=ky^3+3y^2-3$

When p(y) is divided by y-5, the remainder is

$p(5)=k(5)^3+3(5)^2-3$

$p(5)=125k+75-3$

$p(5)=125k+72$

When the second polynomial:

$m(y)= 2y^3-5y+k$ is divided by y-5.

The remainder is;

$m(5)= 2(5)^3-5(5)+k$

$m(5)= 225+k$

The two remainders are equal;

$\implies 125k+72=225+k$

$\implies 125k-k=225-72$

$\implies 124k=153$

$k=\frac{153}{124}$

3 0

3 years ago

The distribution of the test statistic for analysis of variance is the F-distribution. true or false

lubasha [3.4K]

Answer:

True. See explanation below

Step-by-step explanation:

Previous concepts

Analysis of variance (ANOVA) "is used to analyze the differences among group means in a sample".

The sum of squares "is the sum of the square of variation, where variation is defined as the spread between each individual value and the grand mean"

If we assume that we have $k$ groups and on each group from $j=1,\dots,k$ we have $k$ individuals on each group we can define the following formulas of variation: