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Art [367]
3 years ago
11

Explain how to use basic facts and number patterns to find 5,600 divide by 7

Mathematics
2 answers:
Charra [1.4K]3 years ago
5 0
5600 can be simplified. Say you were to just take the two zeros off the end and you would have 56. Now divide 56 by 7 and you get 8. Add the zeros back to the 8 and your answer is 7 x 800. Hope this helps.
Tems11 [23]3 years ago
3 0

Answer:

<h2>600.</h2>

Step-by-step explanation:

Well, to divide these numbers, we can use certain strategy that fits with out logic. For example, we know that 56 divided by 7 can be done mentally, because it's a simple division that can be immediately solved.

So, 56 divided by 7 is 8, then we add the two zeros to the quotient, because we isolated the first to digit to make the division easier and then we have to add back those zeros.

Therefore the division results in 600.

There are several operations that can be done by using certain strategy, like separating the number in easier parts like we did this time.

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A researcher collected data on the hours of TV watched per day from a sample of five people of different ages. Here are the resu
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Answer:

1. The least squares regression is y = -0.1015·x + 6.51

2. The independent variable is b) age

Please see attached table

Step-by-step explanation:

The least squares regression formula is given as follows;

\dfrac{\sum_{i = 1}^{n}(x_{i} - \bar{x})\times \left (y_{i} - \bar{y}  \right ) }{\sum_{i = 1}^{n}(x_{i} - \bar{x})^{2}}

We have;

\bar x = 24

\bar y = 4

\Sigma (x_i - \bar x) (y_i - \bar y) = -79

\Sigma (x_i - \bar x)^2 = 778

\therefore \hat \beta =\dfrac{\sum_{i = 1}^{n}(x_{i} - \bar{x})\times \left (y_{i} - \bar{y}  \right ) }{\sum_{i = 1}^{n}(x_{i} - \bar{x})^{2}} = \frac{-79}{778} = -0.1015

The least squares regression is y = -0.1015·x + α

∴ α = y  -0.1015·x = 6 - (-0.1015 × 5) = 6.51

The least squares regression is thus;

y = -0.1015·x + 6.51

2. The independent variable is the age b)

3. Steps to create an ANOVA table with α = 0.05

The overall mean = (43  + 30  + 22  + 20  + 5  + 1  + 6  + 4  + 3  + 6 )/10 = 14

There are 2 different treatment = df_{treat} = 2 - 1 = 1

There are 10 different treatment measurement = df_{tot} = 10 - 1 = 9

df_{res} = 9 - 1 = 8

df_{treat} + df_{res} = df_{tot}

The estimated effects are;

\hat A_1 = 24 - 14 = 10

\hat A_2 = 4 - 14 = -10

SS_{treat} = 10^2 \times 5 + (-10)^2 \times 5 =1000

\sum_{i}\SS_{row}_i = \sum_{i}\sum_{j} (y_{ij} - \bar y)= [(1 - 4)^2 + (6 - 4)^2 + (4 - 4)^2 + (3 - 4)^2 + (6 - 4)^2] = 18

\sum_{i} S S_{row}_i = \sum_{i}\sum_{j} (y_{ij} - \bar y) ^2= [(43 - 24)^2 + (30 - 24)^2 + (22 - 24)^2 + (20 - 24)^2 + (5 - 24)^2] = 778

S S_{res} = \sum_{i} S S_{row}_i = 778 + 18 = 796

SS_{tot} = (43  - 14)² + (30  - 14)² + (22  - 14)² + (20  - 14)² + (5  - 14)² + (1 - 14)1² + (6  - 4 )² + (3  - 14)² + (6  - 14)² = 1796

MS_{treat} = \dfrac{SS_{treat} }{df_{treat} } = \dfrac{1000}{1} = 1000

MS_{res} = \dfrac{SS_{res} }{df_{res} } = \dfrac{796}{8} = 99.5

F- value is given by the relation;

F = \dfrac{MS_{treat} }{MS_{res} } = \frac{1000}{99.5} = 10.05

We then look for the critical values at degrees of freedom 1 and 8 at α = 0.05 on the F-distribution tables 5.3177

Hence; F = 10.05 > F_{1,8}^{Krit}(5\%) = 5.3177, we reject the null hypothesis.

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