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Alexus [3.1K]
3 years ago
11

Find the first, fourth, and tenth terms of the arithmetic sequence described by the given rule.

Mathematics
1 answer:
klasskru [66]3 years ago
4 0
You can just insert 1, 4, and 10 for n.

A(1) = -2 + (1 - 1) * (-2.2) = -2
A(4) = -2 + (4 - 1) * (-2.2) = -8.6
A(10) = -2 + (10 - 1) * (-2.2) = -21.8
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Hlfkejfrrigije8fejfrjhbie 484hrg8vv​
madreJ [45]

Answer:

i am writing it just for points

4 0
3 years ago
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the tigers won twice as many football games as they lost. They played 96 games. How many games did they win?
asambeis [7]
They lost L games.
They won W games.
W = 2L

W + L = 96, but W = 2L, so

2L + L = 96

3L = 96

L = 32

W = 2L = 2 * 32 = 64

They won 64 games.
7 0
3 years ago
please help. use the table below to answer the questions. i just took a picture of the answer and stuff.
Sergeu [11.5K]

Answer:

1) 400

2) 62

3) i want to say 4.5%

Step-by-step explanation:


5 0
4 years ago
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Let U = {q, r, s, t, u, v, w, x, y, z} A = {q, s, u, w, y} B = {q, s, y, z} C = {v, w, x, y, z}. List the elements in the set. (
algol13

Answer:

its correct answer is

{y}

7 0
3 years ago
In a study relating the degree of warping, in mm, of a copper plate (y) to temperature in °C (x), the following summary statisti
kipiarov [429]

Answer:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

Step-by-step explanation:

Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".

Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"

The linear model is given by the following equation Y=mx +b, where y is the dependent variable, x the independent variable, m the slope and b the intercept.

For this case we have the following info given:

\sum_{i=1}^n (x_i -\bar x)^2 =98775

\sum_{i=1}^n (y_i -\bar y)^2 =19.4

\bar x =26.36

\bar y =0.5188

n= 40  represent the sample size

\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y) =796.94

The total sum of squares is given by this formula:

SST= \sum_{i=1}^n (y_i -\bar y)^2 =19.4

And from the formulas for a simple regression, we need to calculate the slope for the regression like this:

m=\frac{\sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)}{\sum_{i=1}^n (x_i -\bar x)^2}

And if we replace the values given we have:

m=\frac{796.94}{98775}=0.008068

We have another useful equation in order to find the sum of squares for the regression, given by:

SSR=m* \sum_{i=1}^n (x_i -\bar x)(y_i -\bar y)=0.008068*796.94=6.430

And we know this equivalence:

SST= SSR+SSE

And solving for the sum of squares for the error we have:

SSE=SST-SSR=19.4-6.430=12.97

So then we have the final solutions:

The error sum of squares is SSE=12.97

The regression sum of squares is SSR=6.430

The total sum of squares is SST=19.4

6 0
3 years ago
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