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pochemuha
3 years ago
8

Which weighs more, 5 ounces or 150 grams?

Mathematics
2 answers:
gtnhenbr [62]3 years ago
6 0
150 grams weighs more. The number of ounces converted to grams would be about 142 grams. 
stich3 [128]3 years ago
3 0
Answer : 150 Grams. 1 ounce is 28 grams (rounded) times 5 is 140 ounces.   
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Use the graph to find the indicated function value y=f(x). Find f(-2)
spin [16.1K]

Answer:

f(-2)=3

Step-by-step explanation:

we know that

f(-2) is the value of the function f(x) for the value of x equal to -2

so

Observing the graph

For x=-2

f(-2)=3

7 0
3 years ago
A 2-column table with 9 rows. The first column is labeled year with entries 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010
ollegr [7]

Answer:

Median: 4.44

Mean: 4.11

On edge

Step-by-step explanation:

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3 years ago
Question 9 of 10
Lady_Fox [76]

Answer:

the answer is b B is the answer

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Find the regression​ equation, letting the first variable be the predictor​ (x) variable. Find the best predicted Nobel Laureate
SpyIntel [72]

The table is missing, so i have attached it.

Answer:

Regression equation is;

y = 0.2028x - 8.443

The predicted value is not at all close to the actual novel rate

Step-by-step explanation:

The internet users will be the x-values while the novel laureates will be the y-values.

Thus, sum of x values is;

Σx = 79.5 + 79.6 + 56.8 + 67.6 + 77.9 + 38.3 = 399.7

Sum of y values is;

Σy = 5.5 + 9 + 3.3 + 1.7 + 10.8 + 0.1

Σy = 30.4

Σx² = 79.5² + 79.6² + 56.8² + 67.6² + 77.9² + 38.3²

Σx² = 27987.71

Σy² = 5.5² + 9² + 3.3² + 1.7² + 10.8² + 0.1²

Σy² = 241.68

Σxy = (79.5 × 5.5) + (79.6 × 9) + (56.8 × 3.3) + (67.6 × 1.7) + (77.9 × 10.8) + (38.3 × 0.1)

Σxy = 2301.16

The regression line will be;

y = b1(x) + b0

b1 is expressed as;

b1 = [nΣxy - (Σx • Σy)]/[nΣx² - (Σx)²]

Where n is number of given data which is n = 6.

Thus;

b1 = [(6 × 2301.16) - (399.7 × 30.4)]/[(6 × 27987.71) - (399.7)²]

b1 = 1656.08/8166.17

b1 = 0.2028

b0 is expressed as;

b0 = [(Σy • Σx²) - (Σx • Σxy)]/[nΣx² - (Σx)²]

b0 = [(30.4 × 27987.71) - (399.7 × 2301.16)]/[(6 × 27987.71) - (399.7)²]

b0 = -68947.268/8166.17

b0 = -8.443

Thus, regression equation is;

y = 0.2028x + (-8.443)

y = 0.2028x - 8.443

Best predicted value is given as;

y¯ = (Σy)/n

y¯ = 30.4/6

y¯ = 5.067 per 10 million people.

The predicted value is far greater than the actual value of 2 per 10 million​ people.

Thus, we can say that the predicted value is nowhere close to the actual value

4 0
3 years ago
Which situation can be modeled using a linear function?
SVETLANKA909090 [29]

Answer:

the distance covered by a car at a constant speed

3 0
3 years ago
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