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3 years ago

Find the mean of 1,0,10,7,13,2,9,15,0,3

Mathematics

2 answers:

svet-max [94.6K]3 years ago

8 0

1,0,10,7,13,2,9,15,0,3/ 10
=60/10
=6

Zarrin [17]3 years ago

5 0

Answer:

Step-by-step explanation:

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Step-by-step explanation:

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2 years ago

If y-3x=9, 7x+y=25, what is the value of x and y?

lianna [129]

$\bold{\rm{Given}}\text{ : If y - 3x = 9, 7x + y = 25, what is the value of x and y}?$

$\bold{\rm{Solution}} \text{: We'll solve this by substitution method}$

$\dashrightarrow y - 3x = 9 \\ \\ \dashrightarrow \boxed{y = 9 + 3x } \qquad .. \sf eq(1)$

$\text{we got the value of y now getting the value of x}$

$\looparrowright 7x + y = 25 \\ \\ \looparrowright 7x = 25 - y \\ \\ \looparrowright \boxed{x = \frac{25 - y}{7}} \qquad .. \sf eq(2)$

$\text{Now, putting y = 9 + 3x in eq(2)}$

$\hookrightarrow x = \frac{25 - y}{7} \\ \\ \hookrightarrow x = \frac{25 - 9 + 3x}{7} \\ \\ \hookrightarrow 7x = 25 - 9 + 3x \\ \\ \hookrightarrow 7x - 3x = 16 \\ \\ \hookrightarrow 4x = 16 \\ \\ \hookrightarrow x = \frac{16}{4} \\ \\ \star \quad \boxed{ \green{ \frak{ x = 4}}}$

$\text{Now we know the value of x, for getting y we need to put x in eq(1)}$

$: \implies y = 9 + 3x \\ \\ : \implies y = 9 + 3(4) \\ \\ : \implies y = 9 + 12 \\ \\ \star \quad \boxed{\frak{ \green{y = 21}}}$

$\text{Hence, values of x and y are} \: \frak{ \green{4}} \: \text{and} \: \frak{ \green{21}} \: \text{respectively}.$

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2 years ago

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4 years ago

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3 years ago

The following data show the brand, price ($), and the overall score for 6 stereo headphones that were tested by Consumer Reports

Olenka [21]

Complete Question

The complete question is shown on the first uploaded image

Answer:

SST = 1800

SSR = 1512.376

SSE = 287.624

coefficient of determination is $r^2 \approx 0.8402$

What this is telling us is that 84.02% variation in dependent variable y can be fully explained by variation in the independent variable x

The correlation coefficient is $r = 0.917$

Step-by-step explanation:

The table shown the calculated mean is shown on the second uploaded image

Let first define some term

SST (sum of squares total) : This is the difference between the noted dependent variable and the mean of this noted dependent variable

SSR(sum of squared residuals) : this can defined as a predicted shift from the actual observed values of the data

SSE (sum of squared estimate of errors): this can be defined as the sum of the square difference between the observed value and its mean

From the table

$SST = SS_{yy} = 1800$

$SSR = \frac{SS^2_{xy}}{SS_{xx}} = \frac{4755^2}{14950} = 1512.376$

$SSE =SST-SSR$

$=1800 - 1512.376$

$= 287.62$

The coefficient of determination is mathematically represented as

$r^2 = \frac{SSR}{SST}$

$= 1-\frac{SSE}{SST}$

$r^2= 1-\frac{287.6237}{1800}$

$r^2 \approx 0.8402$

The correlation coefficient is mathematically represented as

$r = \pm\sqrt{r^2}$

Substituting values

$r = \sqrt{0.84020}$

$r = 0.917$

this value is + because the value of the coefficient of x in estimated regression equation( $24.9 + 0.301x,$ ) is positive

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3 years ago