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lions [1.4K]
3 years ago
15

I need an answer quick. I am trying to finish this fast.

Mathematics
2 answers:
inessss [21]3 years ago
8 0

Answer:

D, m<4 is 137°

Step-by-step explanation:

The answer would be D, because m<4 would be supplementary with the angle that's 43 degrees. Supplementary angles add up to 180, so this could be found through the following equation.

x + 43 = 180

x would represent <4

You would now subtract 43 from both sides.

x = 137

makvit [3.9K]3 years ago
7 0

Answer:

D. The m<4 is 137°

Step-by-step explanation:

The rest of the angles are not correct because if you add 142+56 (adjacent angles) or the unknown angles (angle 1 + angle 2), it doesn't equal 180. You can picture it as angle 3 and its adjacent angle (56°) equal to 180 sience there sharing one straight line thus being 180°

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Hi!

Remember that an x-intercept is a point in which the line touches the x-axis (the horizontal line). And, the y-intercept is a point in which the line touches the y-axis (the vertical/up and down line)

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2 years ago
An article reports the following data on yield (y), mean temperature over the period between date of coming into hops and date o
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Answer:

x1=c(16.7,17.4,18.4,16.8,18.9,17.1,17.3,18.2,21.3,21.2,20.7,18.5)

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lm(formula = y ~ x1 + x2)

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-41.730 -12.174   0.791 12.374 40.093

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(Intercept) 415.113     82.517   5.031 0.000709 ***  

x1            -6.593      4.859 -1.357 0.207913    

x2            -4.504      1.071 -4.204 0.002292 **  

---  

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  

Residual standard error: 24.45 on 9 degrees of freedom  

Multiple R-squared: 0.768,     Adjusted R-squared: 0.7164  

F-statistic: 14.9 on 2 and 9 DF, p-value: 0.001395

a).  y=415.113 +(-6.593)x1 +(-4.504)x2

b). s=24.45

c).  y =415.113 +(-6.593)*21.3 +(-4.504)*43 =81.0101

residual =68-81.0101 = -13.0101

d). F=14.9

P=0.0014

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e).  newdata=data.frame(x1=21.3, x2=43)

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confidence interval

> predict(mod, newdata, interval="confidence",level=.95)

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f).  #prediction interval

> predict(mod, newdata, interval="predict",level=.95)

      fit      lwr      upr

1 81.03364 14.19586 147.8714

95% PI=(14.20, 147.87)

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