4a+2-3c+6+7c-3-3b+2a-5b you can combine 4a and 2a to get 6a
6a+2-3c+6+7c-3-3b-5b you can combine 5b and 3b to get -8b
6a+2-3c+6+7c-3-8b you can combine 3 and 2 and 6 to get 5
6a+5-3c+7c-8b you can combine 3c and 7c to get 4c
6a+5+4c-8b

6 0

3 years ago

Read 2 more answers

Use Word to documents your comparison of the two years and explain your findings in at least two paragraphs. Including: Explain

pickupchik [31]

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