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GaryK [48]
3 years ago
12

What is 6x6x6x6x6 in exponential form

Mathematics
2 answers:
melisa1 [442]3 years ago
6 0

Answer:

6x6x6x6x6 in exponential form is, 6^5

Step-by-step explanation:

Using the exponent rules:

a^2 = a\cdot a

a^3 = a \cdot a \cdot a

a^4 = a \cdot a \cdot a \cdot a

a^5 = a\cdot a \cdot a \cdot a \cdot a and so on....

As per the statement:

6x6x6x6x6

To write this in exponential form.

By exponent rules:

6 repeats five times.

6 \times 6 \times 6 \times 6 \times 6  = 6^5

Therefore,  6x6x6x6x6 in exponential form is, 6^5

REY [17]3 years ago
5 0
\underbrace{6\cdot6\cdot6\cdot6\cdot6}_{5}=6^5
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Use Pythagoras theorem

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5 0
2 years ago
Read 2 more answers
Dinner for a family of 3 at the local Italian Eatery came to $47.83 before tax. If the family wanted to tip their server 20%, ho
EastWind [94]

Answer:

$9.57

Step-by-step explanation:

47.83 x 20% = 9.57

5 0
2 years ago
15 quarts per hour = gallons per minute
lions [1.4K]

(15 qt/hr) x (1/4 gal/qt) x (1/60 hr/min) =

            (15) / (4 x 60)  =  0.0625 gal/min
 
6 0
2 years ago
A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and
mr Goodwill [35]

Answer:

Bias for the estimator = -0.56

Mean Square Error for the estimator = 6.6311

Step-by-step explanation:

Given - A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and X2. The mean is estimated using the formula (3X1 + 4X2)/8.

To find - Determine the bias and the mean squared error for this estimator of the mean.

Proof -

Let us denote

X be a random variable such that X ~ N(mean = 4.5, SD = 7.6)

Now,

An estimate of mean, μ is suggested as

\mu = \frac{3X_{1} + 4X_{2}  }{8}

Now

Bias for the estimator = E(μ bar) - μ

                                    = E( \frac{3X_{1} + 4X_{2}  }{8}) - 4.5

                                    = \frac{3E(X_{1}) + 4E(X_{2})}{8} - 4.5

                                    = \frac{3(4.5) + 4(4.5)}{8} - 4.5

                                    = \frac{13.5 + 18}{8} - 4.5

                                    = \frac{31.5}{8} - 4.5

                                    = 3.9375 - 4.5

                                    = - 0.5625 ≈ -0.56

∴ we get

Bias for the estimator = -0.56

Now,

Mean Square Error for the estimator = E[(μ bar - μ)²]

                                                             = Var(μ bar) + [Bias(μ bar, μ)]²

                                                             = Var( \frac{3X_{1} + 4X_{2}  }{8}) + 0.3136

                                                             = \frac{1}{64} Var( {3X_{1} + 4X_{2}  }) + 0.3136

                                                             = \frac{1}{64} ( [{3Var(X_{1}) + 4Var(X_{2})]  }) + 0.3136

                                                             = \frac{1}{64} [{3(57.76) + 4(57.76)}]  } + 0.3136

                                                             = \frac{1}{64} [7(57.76)}]  } + 0.3136

                                                             = \frac{1}{64} [404.32]  } + 0.3136

                                                             = 6.3175 + 0.3136

                                                              = 6.6311

∴ we get

Mean Square Error for the estimator = 6.6311

6 0
3 years ago
4.18 repeating as a fraction
olga_2 [115]

Answer: it’s 148 also i don’t know if you mean 4.18 as a fraction?

Step-by-step explanation:

6 0
2 years ago
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