# Two Way Contingency Table Analysis Statistics Hypotheses

*label*Mathematics

*timer*Asked: Jan 4th, 2019

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Hello i need to do the 5 Assignments

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## Tutor Answer

Hi, Find attached the paper for your review.Let me know if you need anything edited or changed.Looking forward to working with you again in future.Thank you.

Attached.

Outline

The paper has been completed as per the requirements.

1

Week 3 Assignment

Student’s Name

Professor’s Name

Date

2

Independent-samples t-test

The independent samples T-test is a parametric test that is used to compare the

means of two independent samples as the name suggests (Fagerland, 2012). This is to

find out if the two population means are significantly different based on the samples

used. The samples used must be random and must meet the normality, independence,

and variance homogeneity assumptions (Wonnacott & Wonnacott, 1990).

The independent variable for this case will be a categorical or nominal variable;

gender while dependent variable will be a continuous variable; Height. The independent

samples T-test will, therefore, be used to test whether there is a mean difference in the

height of males and females, which are the two categories in the categorical variable

selected; Gender.

Research Question

Is there a statistically significant gender height mean difference?

Hypotheses

H0: There is no statistically significant gender height mean difference.

H1: There is a statistically significant gender height mean difference.

Results

A two-tailed independent samples T-test was conducted at the 0.5 level of

significance to find out whether there is a statistically significant difference in the mean

height of males and females. The independent and dependent variables for the test were

gender and height consecutively. The test value (Levene’s test) for the assumption

3

assessment (p = 0.3, ˃.05) therefore not significant. The results were significant, t(103) =

0.067, p = .947. There is, therefore, a significant difference in the mean height of

individuals from the male (M = 169.982, SD = 17.5649) and female (M = 170.204, SD =

16.3171) genders at the 95% level of significance. Figure 1 is a box depicting height by

gender. The SPSS output is provided in the Appendix

Figure 1. Boxplot depicting height scores by gender

4

References

Fagerland, M. W. (2012). t-tests, non-parametric tests, and large studies—a paradox of

statistical practice?. BMC Medical Research Methodology, 12(1), 78

Wonnacott, T. H., & Wonnacott, R. J. (1990). Introductory statistics (Vol. 5). New York:

Wiley.

5

Appendix – Independent Samples t-Test SPSS Output

Gender

Height

Female

Male

Group Statistics

N

Mean

Std.

Deviation

49 170.204

16.3171

56 169.982

17.5649

Std. Error

Mean

2.3310

2.3472

Independent Samples Test

Levene's Test for Equality of

Variances

F

Sig.

t-test for Equality of Means

t

df

Sig. (2-tailed)

Mean

Difference

Std. Error

Difference

95%

L

Height

Equal variances assumed

Equal variances not

assumed

1.010

.317

.067

103

.947

.2219

3.3244

.067

102.616

.947

.2219

3.3080

1

Week 4 Assignment

Student’s Name

Professor’s Name

Date

2

One-Way Analysis of Variance (ANOVA)

One way Analysis of Variance is used to evaluate for the statistical significance in

the difference of means of two or more unrelated independent groups. The one way

ANOVA statistic is termed an omnibus statistic given that it doesn’t specifically state the

groups that are statistically different from each other (Statistics, 2013). As a consequence,

post hoc tests are conducted to establish what specific independent groups are different

from the others in order to add more meaning to the generated ANOVA results

(Frankfort-Nachmias & Leon-Guerrero, 2015).

This research will seek to find out if a statistically significant difference exists in

the GPA scores of individuals by their ethnicity. As such, the one way ANOVA is the

most appropriate statistical test to be utilized. The independent variable (Ethnicity) is

measured at the nominal level while the dependent variable (GPA) is measured at the

scale level.

Research Question

Is there a statistically significant difference in the GPA scores among Hispanic,

African American, Caucasian, Native American, Asian American, and other individuals?

Hypotheses

Null Hypothesis (H0): There is not a statistically significant difference in the GPA

scores among Hispanic, African American, Caucasian, Native American, Asian

American and other individuals.

3

Alternative Hypothesis (H1): There is a statistically significant difference in the

GPA scores among Hispanic, African American, Caucasian, Native American, Asian

American and other individuals.

Results

In this subheading, I will present descriptive statistics, discuss testing of the

assumptions, present inferential statistic results, and conclude with a concise summary.

Descriptive Statistics

A total of 105 individuals participated in the study. The assumption of equality

variances (Levene’s test, p = .484) was evaluated with no violati...

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