What is -3-(-4)-(-2)+1 evaluate

Mathematics

2 answers:

Veronika [31]3 years ago

4 0

Answer:

Step-by-step explanation:

−3−(−4)−(−2)+1

=1−(−2)+1

=3+1

Vlad1618 [11]3 years ago

4 0

Answer:

Step-by-step explanation:

-3-(-4)-(-2)+1 → -3+4+2+1 → =4

You might be interested in

Show all work please will give brainliest answer❕❗️❕❗️❕❗️❕❗️❕David drops a ball from a bridge at an initial height of 100 meters

Inessa [10]

First, 9.8 (Gravity) times 3 (time) equals 29.4, which is the velocity after 3 seconds. The kinematic equation for change in position that uses the variables we have is:
delta x= (v)(t) -0.5(acceleration)(time)^2
delta x= 29.4 times (3) - 0.5 (9.8) times 9
delta x= 44.1
100 minus 44.1 equals 55.9, which is the answer for part a.

Tell me if you need any clarification

PART B:
The kinematic equation for this is:
delta x= (initial velocity) times time plus 0.5 (a)(time)^2
100=(0)times(x) plus 0.5 (a)(time)^2
100=0.5(9.8)(x)^2
100=4.9x^2
100/4.9 is approxamitely 20.4.
The squareroot of this is approxamitely 4.5.
4.5 seconds

Tell me if you need any clarification

3 0

3 years ago

The baseball coach bought 2 new baseballs for $2 each. The basketball coach bought 6 new basketballs for $13 each. How much more

amm1812

The basketball coach spent 80 dollars more than the baseball coach

3 0

3 years ago

Read 2 more answers

Please help with this

Degger [83]

All of them are wrong its 91 but if you want click or circle 90

3 0

3 years ago

The scores on the GMAT entrance exam at an MBA program in the Central Valley of California are normally distributed with a mean

Kaylis [27]

Answer:

58.32% probability that a randomly selected application will report a GMAT score of less than 600

93.51% probability that a sample of 50 randomly selected applications will report an average GMAT score of less than 600

98.38% probability that a sample of 100 randomly selected applications will report an average GMAT score of less than 600

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .