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ikadub [295]
3 years ago
5

Which are undefined terms that can be used to define other terms

Mathematics
1 answer:
Alisiya [41]3 years ago
6 0

In Geometry, we have several undefined terms: point, line and plane. From these three undefined terms, all other terms in Geometry can be defined. In Geometry, we define a point as a location and no size. Hope I helped! ☺

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you invested 1,000 in an account with interest rate at 2% compounded continuously. how long until 1400 is in your account?
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Step-by-step explanation:

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3 years ago
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Francis works at Carlos Bakery and is making cookie trays. She has 48 chocolate chip cookies, 64 rainbow cookies, and 120 oatmea
amm1812

The number of cookies and trays are illustrations of greatest common factors.

  • The number of trays is 8
  • 6 chocolate chips, 8 rainbows and 15 oatmeal cookies would fit each tray

The given parameters are:

\mathbf{Chocolate\ chip=48}

\mathbf{Rainbow=64}

\mathbf{Oatmeal=120}

<u>(a) The number of trays</u>

To do this, we simply calculate the greatest common factor of 48, 64 and 120

Factorize the numbers, as follows:

\mathbf{48 = 2 \times 2 \times 2 \times 2 \times 3}

\mathbf{64 = 2 \times 2 \times 2 \times 2 \times 2 \times 2}

\mathbf{120 = 2 \times 2 \times 2 \times 3 \times 5}

So, the GCF is:

\mathbf{GCF= 2 \times 2 \times 2}

\mathbf{GCF= 8}

Hence, the number of tray is 8

<u>(b) The number of each type of cookie</u>

We have

\mathbf{Chocolate\ chip=48}

\mathbf{Rainbow=64}

\mathbf{Oatmeal=120}

Divide each cookie by the number of trays

So, we have:

\mathbf{Chocolate\ chip = \frac{48}{8} = 6}

\mathbf{Rainbow = \frac{64}{8} = 8}

\mathbf{Oatmeal = \frac{150}{8} = 15}

Hence, 6 chocolate chips, 8 rainbows and 15 oatmeal cookies would fit each tray

Read more about greatest common factors at:

brainly.com/question/11221202

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2 years ago
What is the solution set for the inequality 3(5- x)&lt;5x-17
Nana76 [90]

Answer:

i think the answer is 7

Step-by-step explanation:

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3 years ago
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When referring to the normal probability distribution, there is not just one; there is a "family" of normal probability distribu
pishuonlain [190]
<span>A probability distribution is formed from all possible outcomes of a random process (for a random variable X) and the probability associated with each outcome. Probability distributions may either be discrete (distinct/separate outcomes, such as number of children) or continuous (a continuum of outcomes, such as height). A probability density function is defined such that the likelihood of a value of X between a and b equals the integral (area under the curve) between a and b. This probability is always positive. Further, we know that the area under the curve from negative infinity to positive infinity is one. The normal probability distribution, one of the fundamental continuous distributions of statistics, is actually a family of distributions (an infinite number of distributions with differing means (ÎĽ) and standard deviations (Ď). Because the normal distribution is a continuous distribution, we can not calculate exact probability for an outcome, but instead we calculate a probability for a range of outcomes (for example the probability that a random variable X is greater than 10). The normal distribution is symmetric and centered on the mean (same as the median and mode). While the x-axis ranges from negative infinity to positive infinity, nearly all of the X values fall within +/- three standard deviations of the mean (99.7% of values), while ~68% are within +/-1 standard deviation and ~95% are within +/- two standard deviations. This is often called the three sigma rule or the 68-95-99.7 rule. The normal density function is shown below (this formula won’t be on the diagnostic!)</span>
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3 years ago
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