Unit VI Scholarly Activity

Unit VI Data Analysis: t-Tests and ANOVA

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VIDEO RECORDING: http://columbiasouthern.adobeconnect.com/pcyx4tmaolm5/ PROBLEM: Use the Sun Coast Remediation data set to conduct an independent samples t-Test, dependent samples (paired

samples) t-Test, and ANOVA using the independent samples tab, paired samples tab, and ANOVA tab in the

Sun Coast data file. The statistical output tables should be cut and pasted from Excel directly into the final

project document. SOLUTION: Independent Sample t-Test: Hypothesis Testing This assignment is asking you to include the following information: 1. Restate the hypotheses (both the null and alternative). This was completed in the Unit II assignment.

a. Use “DV” for dependent variable

b. Use “IV1”(Group A, Prior Training) and “IV2”(Group B, Revised Training) for the independent

variables

Note: Please follow the template guide for restating the null and alternative hypotheses.

Conduct a t-Test as follows:

a.) Go to Data Analysis and select t-Test: Two-Sample Assuming Unequal Variances. This is just a

sampling of about 34 rows of data. You must use all of the rows of data. Be sure to select Labels to

keep titles.

Results: Please note how the variances are not equal. There is a distinct difference, and because Excel

does the calculation, based on population values, we can assume the values we see here (sample variance) will reflect the same. This is why we can use this particular t-Test.

2. Provide the data tables of the independent sample t-Test, containing the two-sample assuming

unequal variances.

a. We want to see if there is a significant difference between the safety training programs of Group A

employees and Group B employees.

b. Group A employees were trained using the current program (the prior training, IV1).

c. Group B employees were trained using a new program (revised training, IV2) that the company is considering to purchase, replacing the current safety training program.

3. Interpret the data output table results:

a. Discuss the p-value in relation to alpha b. Explicitly accept or reject the null and alternative hypotheses

First, the example provided here was only a partial t-Test, with only 34 rows used, so the results from

this example can be somewhat different than the actual results. In other words, the unequal variances of this example are vastly different, where the actual results may not be as diverse.

Interpretation:

Looking at the data output, the mean values are lower for Group A (prior training), and using an alpha of

0.05, the results indicate the p-value (two-tailed) of 1.56 × 10−7 is considerably less than the 0.05 alpha.

Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted, that there is a

statistically significant difference in mean values of the DV between Group A (IV1), Prior Training and

group B (IV2), Revised Training. The company should consider replacing the prior safety training

program with the revised program, since the mean scores from the training show improvement.

Dependent Samples (Paired Samples) t-Test: Hypothesis Testing This assignment is asking you to include the following information: 1. Restate the hypotheses (both the null and alternative). This comes from the Unit II assignment.

a. Use “DV” for dependent variable, which refers to the job sites where lead remediation will be

conducted. b. Use “IV” for independent variable, which refers to the lead levels in the blood.

Do not forget to interpret

the p-value, to decide

whether to accept or

reject the null hypothesis.

Note: Please follow the template guide for restating the null and alternative hypotheses.

You will follow the same steps described in the Independent Samples t-Test, except use:

t-Test: Paired Two Sample for Means.

2. Provide the data tables of the dependent paired samples t-Test. We want to see if there is a significant

difference between the lead levels (µg/dL) in the blood prior to exposure, and post-exposure. It is

necessary to determine if blood lead levels have increased while employees were conducting lead remediation at the job sites.

Please note: This

example only has 39

rows of data

(observations). Your

results should have about 49 rows of data.

Notice, this example only has

about 39 rows of data. You

must use all the data in both

columns. If you include the

titles, be sure to click Labels.

The data is paired, because we

are observing the results of

the same employee before

and after exposure to lead.

Be sure to look at the p-value,

and discuss whether to accept

or reject the null hypothesis.

A p-value > 0.05, you must

accept the null hypothesis. If

a p-value is < 0.05, you can

reject the null hypothesis.

3. Interpret the data output table results:

a. Discuss the p-value in relation to alpha b. Explicitly accept or reject the null and alternative hypotheses

Interpretation

Using an alpha of 0.05, we know to accept the null hypothesis if the p-value > 0.05. Our results indicate a

p-value of 0.059 > 0.05. Therefore, we must reject the alternative hypothesis, and accept the null

hypothesis, that there is no statistically significant difference in the lead levels in the blood, pre-exposure and post-exposure, of employees working at job sites where lead remediation is being conducted.

ANOVA: Hypothesis Testing

This assignment is asking you to include the following information: 1. Restate the hypotheses (both the null and alternative). This comes from the Unit II assignment.

a. Use “DV” for dependent variable, which refers to the return-on-investment

b. The independent variables will be the four lines of service that Sun Coast offers their clients: air

monitoring, soil remediation, water reclamation, and health and safety training..

Note: Please follow the template guide for restating the null and alternative hypotheses.

2. Conduct an ANOVA: Single Factor Summary

a.) Follow the diagram (below), using the Anova: Single Factor from the Data Analysis toolbar in

Excel. Be sure to highlight the titles, and click on Label.

3. Provide the ANOVA data tables. We want to see if there is a significant difference between the four IVs

(air, soil, water, and training) and the return-on-investment for the company.

4. Interpret the data output table results:

a. Discuss the p-value in relation to alpha of 0.05 b. Explicitly accept or reject the null and alternative hypotheses

Interpretation:

Looking at the ANOVA p-value, we see that it is 1.01 × 10−6, which is considerably less than our 0.05

alpha. Since 1.01 × 10−6 < 0.05 (alpha), we can reject the null hypothesis, and accept the alternative

hypothesis, which states that there are statistically significant differences between the return-on-

investment and the four lines of services offered at Sun Coast.

The ANOVA test itself cannot tell the researcher which variable or variables have caused the significance,

but if we were able to perform Tukey’s HSD test, it would be able to point out which variables have

created the significant difference in the return-on-investment. The Tukey’s HSD is mentioned in your

study guide, but it exceeds the scope of this course and is not performed.

Note that this example is

only using a portion of the

data. Your results will be

somewhat different

Do not forget to discuss

the p-value. In this

example, the p-value is

< 0.05, therefore, the null

hypothesis would be

rejected, since the p-value

is about 0.00000101