Solve the equation for all values of x: (x+1)^2=-49

Mathematics

1 answer:

Kazeer [188]3 years ago

6 0

Answer:

Values for x = -1 + 7i and -1 - 7i

Step-by-step explanation:

we have an equation,

(x+1)^2 = -49

We need to solve this equation for all values of x:

(x+1)^2 = - 49

Taking square root both sides

√((x +1)^2) = √-49

x + 1 = ± 7i

Subtract both sides by 1

x = -1 ± 7i

Hence we get two values for x = -1 + 7i and -1 - 7i

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In statistics, the standard deviation deviation may be a measure of the quantity of variation or dispersion of a group of values. The margin of error may be a statistic expressing the number of sampling error within the results of a survey. The correlation could be a statistical measure of the strength of the connection between the relative movements of two variables.

Given nothing and that we need to explain standard deviation. margin of error, correlation coefficient .

Standard deviation

In statistics, the standard deviation may be a measure of the number of variation or dispersion of a group of values. an occasional variance indicates that the values tend to be near the mean of the set, while a high variance indicates that the values are detached over a wider range.

Formula: $\sqrt{(x-x bar)^{2}/N }$

where x bar is mean and N is size of population.

Margin of error

The margin of error may be a statistic expressing the quantity of sampling error within the results of a survey. The larger the margin of error, the less confidence one should have that a poll result would reflect the results of a survey of the complete population.

Formula for M=z*s/ $\sqrt{n}$

here z is z value of Z score , s is variance , n is that the sample size.

Correlation coefficient

In statistics, the Pearson parametric statistic ― also called Pearson's r, the Pearson product-moment parametric statistic, the bivariate correlation, or colloquially simply because the coefficient of correlation ― could be a measure of linear correlation between two sets of information.

Formula=∑ $(x_{i} -x bar)(y_{i} -y bar)/\sqrt{}$ ∑ $(x_{i} -x bar)^{2}$ ∑ $(y_{i}-ybar) ^{2}$