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# Calculations to Write Four Essays

*label*Mathematics

*timer*Asked: Feb 16th, 2019

*account_balance_wallet*$15

### Question Description

Use the given calculations to write 4 new essays. 1 page for each.

Please note that they are 4 individual essay, you need to finish them one by one using the given calculation on each.

And also you need to submit 4 separate files to me in order.

You cant put all 4 answers into one file.

All the work must be original

Turnitin report is required

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## Tutor Answer

Hello, I'm through with the paper. It's perfect but in case of anything you can contact me. Thank you very much. Good bye

Running Head: New Call Time

1

Name

Course

University

New Call Time

2

New call time

Introduction

A two-sample hypothesis test is used to compare to means in order to retain or reject a null

hypothesis. A z-test is used when solving a two-sample hypothesis test. Based on the New Call

Center Data, two assumptions can be made. One is that the data follows a normal distribution

and that the values are independent.

Hypothesis

H0: Old time=New time

H1: Old time ≠New time

For the z-statistic to be computed from the data, there must be a sample size. Summation of the

assumption shows that the sample sizes for the old and new call are 11.066 and 8.742

respectively. We assume the sample variances for the new and old calls to be x and y

respectively. In calculation we use the statistical test formula of t= (mean x-mean y) or [s*sqrt

(1/mean y+1/mean x), where S=sqrt (mean x*s^2x + mean y*s^2y) (John, 2014).

Substituting the values to find S with the data given we have [(170*11.066

+170*8.853)/(11.066+8.853)= 11.02-2=4.04. Substituting to the rest of the formula we get

[(11.066-8.853)/[4.04*(1/170+1/170)]= 5.296.

The critical value of from the N-tables with 5% significance levels is 3.985. 5.296 is greater than

3.985, therefore, we reject Ho. We recommend to the vice president that the advancements made

in call time are significant and should continue.

New Call Time

2

According to John (2014), in a linear relationship of a dependent and independent variable, we

can estimate its strength and direction using a correlation coefficient. Correlation coefficients lie

between -1 and +1 (John, 2014). In order t...

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