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rosijanka [135]

2 years ago

9

It’s due tomorrow and I don’t have time to to do it now I have to do something for my other classes

1 answer:

ivann1987 [24]2 years ago

4 0

For the top angles you would solve them by setting them equal to each other, so you would do 3x+5=17x-70. You can then add 70 to both sides, getting 3x+75=17x. You can then subtract 3x from both sides, getting 75=14x. You then divide both sides, getting 5 5/14 or around 5.36 as x. You can then add plug that into 3x+5, getting around 21.07. You can then make the equation R=180-21.07. You'd get 158.93.

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Please help me with this, I’m having trouble with the equation.

Lapatulllka [165]

Answer:

Completing the square

Step-by-step explanation:

The next step would be to factor into a perfect square

3 0

2 years ago

If the coefficient of determination is 0.25 and the sum of squares residual is 180, then what is the value of SSY?

Gennadij [26K]

Answer:

And then $SSY=SS_{total}=\sum_{j=1}^n (y_j-\bar y)^2 =240$

C. 240

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"

When we conduct a multiple regression we want to know about the relationship between several independent or predictor variables and a dependent or criterion variable.

If we assume that we have $k$ independent variables and we have $j=1,\dots,j$ individuals, we can define the following formulas of variation:

$SS_{total}=\sum_{j=1}^n (y_j-\bar y)^2$

$SS_{regression}=SS_{model}=\sum_{j=1}^n (\hat y_{j}-\bar y)^2$

$SS_{error}=\sum_{j=1}^n (y_{j}-\hat y_j)^2 =180$

And we have this property

$SST==SSY=SS_{regression}+SS_{error}=SSR+180$

If we solve for SSR we got:

$SSR= SSY-180$ (1)

And we know that the determination coefficient is given by:

$R^2 = \frac{SSR}{SSY}$

We know the value os $R^2= 0.25$ and we can replace SSR in terms of SSY with the equation (1)

$R^2 =0.25= \frac{SSY-180}{SSY}= 1-\frac{180}{SSY}$

And solving SSY we got:

$\frac{180}{SSY}=1-0.25=0.75$

$SSY= \frac{180}{0.75}=240$

And then $SSY=SS_{total}=\sum_{j=1}^n (y_j-\bar y)^2 =240$

C. 240

7 0

3 years ago

Which of the following is the equation of a line in slope-intercept form for a

Arada [10]

The slope intercept formula is:

y = mx + b

m is the slope

b is the y-intercept

In this case the slope is...

1

The y-intercept is...

(0, -4)

Plug these numbers in for m and b respectively:

y = 1x + (-4)

y = x - 4

Hope this helped!

~Just a girl in love with Shawn Mendes

7 0

3 years ago

Read 2 more answers

Please help me, I’ll mark you ask brainliest:-) !!

antoniya [11.8K]

Answer:

enter t= 12

Step-by-step explanation:

I think it helps

4 0

2 years ago

Read 2 more answers

Historically, the proportion of people who trade in their old car to a car dealer when purchasing a new car is 48%. Over the pre

artcher [175]

Answer: The proportion of new car buyers that trade in their old car has statistically significantly decreased.

Step-by-step explanation:

Since we have given that

p = 48% = 0.48

n = 115

x = 46

So, $\hat{p}=\dfrac{46}{115}=0.40$

So, hypothesis would be

$H_0:\ p=\hat{p}\\\\H_a:p$

So, test value would be

$z=\dfrac{p-\hat{p}}{\sqrt{\dfrac{p(1-p)}{n}}}\\\z=\dfrac{0.48-0.40}{\sqrt{\dfrac{0.48\times 0.52}{115}}}\\\\z=\dfrac{0.08}{0.0466}\\\\z=1.72$

At 10% level of significance, critical value would be

z= 1.28

Since 1.28 < 1.72

So, we will reject the null hypothesis.

Hence, the proportion of new car buyers that trade in their old car has statistically significantly decreased.

6 0

3 years ago