Week 11 project

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Null Hypothesis of Question

A null hypothesis is a statistical hypothesis that states that no variance exists between specific features of a populace or data-generating activity. A player, for illustration, may be curious about the fairness of a form of gambling. If it's fair, both opponents' anticipated profits per play will be zero. If the game isn't fair, one player's anticipated profits will be positive while the other would be unfavorable. General Social Survey Data set is our data to find all over the research assignment, which contain MANOVA analysis by using SPSS software.

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In the above screen shot, general social survey dataset is given, showing data view of the dataset. Bellow screen shot is also showing all the variables, present in the data set.

Q: Is the mostly respondents are from America? And all have guns in home? Has their own cars?

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Between-Subjects Factors
	Value Label	N
NUMBER OF CHILDREN	0	0	159
	1	1	79
	2	2	126
	3	3	63
	4	4	33
	5	5	11
	6	6	6
	7	7	2
	8	EIGHT OR MORE	2

Tests of Between-Subjects Effects
Dependent Variable: Rs occupational prestige score (2010)
Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	1816.608a	8	227.076	1.172	.314
Intercept	114151.736	1	114151.736	589.250	.000
childs	1816.608	8	227.076	1.172	.314
Error	91437.579	472	193.724
Total	1086180.000	481
Corrected Total	93254.187	480
a. R Squared = .019 (Adjusted R Squared = .003)

Multiple Comparisons
Dependent Variable: Rs occupational prestige score (2010)
Tukey HSD
(I) NUMBER OF CHILDREN	(J) NUMBER OF CHILDREN	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
					Lower Bound	Upper Bound
0	1	-.73	1.916	1.000	-6.70	5.24
	2	-1.11	1.660	.999	-6.28	4.06
	3	-1.94	2.072	.991	-8.40	4.51
	4	1.30	2.662	1.000	-7.00	9.59
	5	6.57	4.339	.849	-6.95	20.09
	6	-3.64	5.788	.999	-21.68	14.40
	7	-4.97	9.904	1.000	-35.84	25.89
	EIGHT OR MORE	20.03	9.904	.529	-10.84	50.89
1	0	.73	1.916	1.000	-5.24	6.70
	2	-.38	1.997	1.000	-6.60	5.85
	3	-1.21	2.351	1.000	-8.54	6.12
	4	2.03	2.885	.999	-6.96	11.02
	5	7.30	4.479	.787	-6.65	21.26
	6	-2.91	5.894	1.000	-21.27	15.46
	7	-4.24	9.966	1.000	-35.30	26.81
	EIGHT OR MORE	20.76	9.966	.486	-10.30	51.81
2	0	1.11	1.660	.999	-4.06	6.28
	1	.38	1.997	1.000	-5.85	6.60
	3	-.83	2.148	1.000	-7.53	5.86
	4	2.41	2.722	.994	-6.07	10.89
	5	7.68	4.376	.712	-5.96	21.32
	6	-2.53	5.816	1.000	-20.66	15.59
	7	-3.87	9.920	1.000	-34.78	27.05
	EIGHT OR MORE	21.13	9.920	.454	-9.78	52.05
3	0	1.94	2.072	.991	-4.51	8.40
	1	1.21	2.351	1.000	-6.12	8.54
	2	.83	2.148	1.000	-5.86	7.53
	4	3.24	2.991	.976	-6.08	12.56
	5	8.51	4.548	.634	-5.66	22.69
	6	-1.70	5.947	1.000	-20.23	16.83
	7	-3.03	9.997	1.000	-34.18	28.12
	EIGHT OR MORE	21.97	9.997	.409	-9.18	53.12
4	0	-1.30	2.662	1.000	-9.59	7.00
	1	-2.03	2.885	.999	-11.02	6.96
	2	-2.41	2.722	.994	-10.89	6.07
	3	-3.24	2.991	.976	-12.56	6.08
	5	5.27	4.846	.976	-9.83	20.37
	6	-4.94	6.177	.997	-24.19	14.31
	7	-6.27	10.136	1.000	-37.86	25.31
	EIGHT OR MORE	18.73	10.136	.650	-12.86	50.31
5	0	-6.57	4.339	.849	-20.09	6.95
	1	-7.30	4.479	.787	-21.26	6.65
	2	-7.68	4.376	.712	-21.32	5.96
	3	-8.51	4.548	.634	-22.69	5.66
	4	-5.27	4.846	.976	-20.37	9.83
	6	-10.21	7.064	.879	-32.22	11.80
	7	-11.55	10.699	.977	-44.89	21.80
	EIGHT OR MORE	13.45	10.699	.943	-19.89	46.80
6	0	3.64	5.788	.999	-14.40	21.68
	1	2.91	5.894	1.000	-15.46	21.27
	2	2.53	5.816	1.000	-15.59	20.66
	3	1.70	5.947	1.000	-16.83	20.23
	4	4.94	6.177	.997	-14.31	24.19
	5	10.21	7.064	.879	-11.80	32.22
	7	-1.33	11.364	1.000	-36.75	34.08
	EIGHT OR MORE	23.67	11.364	.487	-11.75	59.08
7	0	4.97	9.904	1.000	-25.89	35.84
	1	4.24	9.966	1.000	-26.81	35.30
	2	3.87	9.920	1.000	-27.05	34.78
	3	3.03	9.997	1.000	-28.12	34.18
	4	6.27	10.136	1.000	-25.31	37.86
	5	11.55	10.699	.977	-21.80	44.89
	6	1.33	11.364	1.000	-34.08	36.75
	EIGHT OR MORE	25.00	13.918	.685	-18.37	68.37
EIGHT OR MORE	0	-20.03	9.904	.529	-50.89	10.84
	1	-20.76	9.966	.486	-51.81	10.30
	2	-21.13	9.920	.454	-52.05	9.78
	3	-21.97	9.997	.409	-53.12	9.18
	4	-18.73	10.136	.650	-50.31	12.86
	5	-13.45	10.699	.943	-46.80	19.89
	6	-23.67	11.364	.487	-59.08	11.75
	7	-25.00	13.918	.685	-68.37	18.37
Based on observed means. The error term is Mean Square(Error) = 193.724.

Rs occupational prestige score (2010)
Tukey HSDa,b,c
NUMBER OF CHILDREN	N	Subset
		1	2
EIGHT OR MORE	2	25.00
5	11	38.45	38.45
4	33	43.73	43.73
0	159	45.03	45.03
1	79	45.76	45.76
2	126	46.13	46.13
3	63	46.97	46.97
6	6		48.67
7	2		50.00
Sig.		.091	.843
Means for groups in homogeneous subsets are displayed. Based on observed means. The error term is Mean Square(Error) = 193.724.
a. Uses Harmonic Mean Sample Size = 6.764.
b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.
c. Alpha = 0.05.

General Linear Model

Between-Subjects Factors
	Value Label	N
AGE OF RESPONDENT	43	43	1

Descriptive Statisticsa
	AGE OF RESPONDENT	Mean	Std. Deviation	N
R's socioeconomic index (2010)	43	70.500	.	1
	Total	70.500	.	1
Rs occupational prestige score (2010)	43	48.00	.	1
	Total	48.00	.	1
NUMBER OF COLLEGE-LEVEL SCI COURSES R HAVE TAKEN	43	2.00	.	1
	Total	2.00	.	1
FAMILY INCOME IN CONSTANT DOLLARS	43	49882.50	.	1
	Total	49882.50	.	1
RESPONDENT INCOME IN CONSTANT DOLLARS	43	40645.00	.	1
	Total	40645.00	.	1
HIGHEST YEAR OF SCHOOL COMPLETED	43	16.00	.	1
	Total	16.00	.	1
EMAIL HOURS PER WEEK	43	35.00	.	1
	Total	35.00	.	1
NUMBER OF HOURS USUALLY WORK A WEEK	43	40.00	.	1
	Total	40.00	.	1
FAMILY INCOME IN CONSTANT $	43	31927.50	.	1
	Total	31927.50	.	1
SIZE OF PLACE IN 1000S	43	14.00	.	1
	Total	14.00	.	1
HOURS PER DAY WATCHING TV	43	2.00	.	1
	Total	2.00	.	1
WWW HOURS PER WEEK	43	60.00	.	1
	Total	60.00	.	1
a. Weighted Least Squares Regression - Weighted by LABOR FORCE STATUS

Multivariate Testsa,b
Effect	Value	F	Hypothesis df	Error df	Sig.	Partial Eta Squared
Intercept	Pillai's Trace	.	.c	.	.	.	.
	Wilks' Lambda	.	.c	.	.	.	.
	Hotelling's Trace	.	.c	.	.	.	.
	Roy's Largest Root	.	.c	.	.	.	.
citizen	Pillai's Trace	.	.c	.	.	.	.
	Wilks' Lambda	.	.c	.	.	.	.
	Hotelling's Trace	.	.c	.	.	.	.
	Roy's Largest Root	.	.c	.	.	.	.
age	Pillai's Trace	.	.c	.	.	.	.
	Wilks' Lambda	.	.c	.	.	.	.
	Hotelling's Trace	.	.c	.	.	.	.
	Roy's Largest Root	.	.c	.	.	.	.
a. Design: Intercept + citizen + age
b. Weighted Least Squares Regression - Weighted by LABOR FORCE STATUS
c. Exact statistic

Transformation Coefficients (M Matrix)
Dependent Variable	R's socioeconomic index (2010)	Rs occupational prestige score (2010)	NUMBER OF COLLEGE-LEVEL SCI COURSES R HAVE TAKEN	FAMILY INCOME IN CONSTANT DOLLARS	RESPONDENT INCOME IN CONSTANT DOLLARS	HIGHEST YEAR OF SCHOOL COMPLETED	EMAIL HOURS PER WEEK	NUMBER OF HOURS USUALLY WORK A WEEK	FAMILY INCOME IN CONSTANT $	SIZE OF PLACE IN 1000S	HOURS PER DAY WATCHING TV	WWW HOURS PER WEEK
R's socioeconomic index (2010)	1	0	0	0	0	0	0	0	0	0	0	0
Rs occupational prestige score (2010)	0	1	0	0	0	0	0	0	0	0	0	0
NUMBER OF COLLEGE-LEVEL SCI COURSES R HAVE TAKEN	0	0	1	0	0	0	0	0	0	0	0	0
FAMILY INCOME IN CONSTANT DOLLARS	0	0	0	1	0	0	0	0	0	0	0	0
RESPONDENT INCOME IN CONSTANT DOLLARS	0	0	0	0	1	0	0	0	0	0	0	0
HIGHEST YEAR OF SCHOOL COMPLETED	0	0	0	0	0	1	0	0	0	0	0	0
EMAIL HOURS PER WEEK	0	0	0	0	0	0	1	0	0	0	0	0
NUMBER OF HOURS USUALLY WORK A WEEK	0	0	0	0	0	0	0	1	0	0	0	0
FAMILY INCOME IN CONSTANT $	0	0	0	0	0	0	0	0	1	0	0	0
SIZE OF PLACE IN 1000S	0	0	0	0	0	0	0	0	0	1	0	0
HOURS PER DAY WATCHING TV	0	0	0	0	0	0	0	0	0	0	1	0
WWW HOURS PER WEEK	0	0	0	0	0	0	0	0	0	0	0	1

Estimatesa
Dependent Variable	AGE OF RESPONDENT	Mean	Std. Error	95% Confidence Interval
				Lower Bound	Upper Bound
R's socioeconomic index (2010)	43	70.500b	.	.	.
Rs occupational prestige score (2010)	43	48.000b	.	.	.
NUMBER OF COLLEGE-LEVEL SCI COURSES R HAVE TAKEN	43	2.000b	.	.	.
FAMILY INCOME IN CONSTANT DOLLARS	43	49882.500b	.	.	.
RESPONDENT INCOME IN CONSTANT DOLLARS	43	40645.000b	.	.	.
HIGHEST YEAR OF SCHOOL COMPLETED	43	16.000b	.	.	.
EMAIL HOURS PER WEEK	43	35.000b	.	.	.
NUMBER OF HOURS USUALLY WORK A WEEK	43	40.000b	.	.	.
FAMILY INCOME IN CONSTANT $	43	31927.500b	.	.	.
SIZE OF PLACE IN 1000S	43	14.000b	.	.	.
HOURS PER DAY WATCHING TV	43	2.000b	.	.	.
WWW HOURS PER WEEK	43	60.000b	.	.	.
a. Weighted Least Squares Regression - Weighted by LABOR FORCE STATUS
b. Covariates appearing in the model are evaluated at the following values: ARE YOU A CITIZEN OF AMERICA? = 1.00.

Between-Subjects Factors
	Value Label	N
RS HIGHEST DEGREE	3	BACHELOR	1

Descriptive Statisticsa
	RS HIGHEST DEGREE	Mean	Std. Deviation	N
R's socioeconomic index (2010)	BACHELOR	70.500	.	1
	Total	70.500	.	1
Rs occupational prestige score (2010)	BACHELOR	48.00	.	1
	Total	48.00	.	1
NUMBER OF COLLEGE-LEVEL SCI COURSES R HAVE TAKEN	BACHELOR	2.00	.	1
	Total	2.00	.	1
FAMILY INCOME IN CONSTANT DOLLARS	BACHELOR	49882.50	.	1
	Total	49882.50	.	1
RESPONDENT INCOME IN CONSTANT DOLLARS	BACHELOR	40645.00	.	1
	Total	40645.00	.	1
HIGHEST YEAR OF SCHOOL COMPLETED	BACHELOR	16.00	.	1
	Total	16.00	.	1
EMAIL HOURS PER WEEK	BACHELOR	35.00	.	1
	Total	35.00	.	1
NUMBER OF HOURS USUALLY WORK A WEEK	BACHELOR	40.00	.	1
	Total	40.00	.	1
FAMILY INCOME IN CONSTANT $	BACHELOR	31927.50	.	1
	Total	31927.50	.	1
SIZE OF PLACE IN 1000S	BACHELOR	14.00	.	1
	Total	14.00	.	1
HOURS PER DAY WATCHING TV	BACHELOR	2.00	.	1
	Total	2.00	.	1
WWW HOURS PER WEEK	BACHELOR	60.00	.	1
	Total	60.00	.	1
a. Weighted Least Squares Regression - Weighted by LABOR FORCE STATUS

Appendix

Research Design

The research design we have get from this data is MANOVA. By using MANOVA we can have made all the tests by measuring means, descriptive statistics, etc. We can see all the test in the 1st part of the assignment. Multivariate examination of variance (MANOVA) is a statistical technique for assessing multivariate population means. Because there are multiple dependent variables, it is employed as a multivariate method, and it is often supplemented by significance tests on each regression equation individually. When your objective variables are cointegrated, use multivariate ANOVA. The strategic management practice between the outcome variable adds to the model's material, resulting in MANOVA. MANOVA may detect effects that are lesser than those found by conventional ANOVA when the reliant variables are cointegrated. Rather than affecting a single independent variable, the model's components may influence the connection between them. ANOVA tests using a single dependents variable, as shown in this article, may totally miss these correlations.

Dependent Variables Used in research from dataset

· Occupational Prestige Score

· Socioeconomics Index

· Number of college level Sci Courses or have taken

· Family Income in Constant dollars

· Respondent income in Constant Dollars

· Highest year of school Completed

· Email hours per week

· Number of hours usually work a week

· Size of place in 1000$

· Hours per day watching TV

· Hours per week

Independent Variables used in research from dataset

· Age of respondent

· Number of children’s

· Own home or Rent Home

· Marijuana made legal

· Marital Status

· Have gun in home

· Political party affiliation

· Race of respondent

· Respondent income

· Respondent Sex

· Sexual Orientation

· Spouse belongs to union

· US Citizen

· Feels discriminated

· Self-Employee

Effects of significance level on dataset

The significance level, also characterized as alpha or, is a measure of how strong the probability must be in your population before you could even reject the null hypothesis and declare that the impact is statistically relevant. Before starting the investigation, the researchers selected the significance level. Understanding the Significance Level's Purpose. The purpose of calculating a confidence interval is to respond to a query posed to the data. In situations when an unambiguous answer cannot be determined, statistical tests are employed. We don't need a quantitative test to prove that cells need breathe to survive if they consistently die when breath is taken off. Confidence interval means that there's a strong chance that this therapy will work. That will never, however, provide an unambiguous yes or no.

Answer to the research question

The answer is that, not all the respondents are not belongs to America, and some of them has their own guns in room, and mostly are all have their own cars.

Social Change According to dataset

Social change refers to the gradual alteration of behavioral patterns as well as cultural values. Recurrent monitoring over a long length of time is required for the longitudinal research. This research aids in determining an individual's life expectancy by observing them throughout the course of their lives or for a period of ten years. The findings of these research are often utilised in psychology and sociology to analyze life events across history and to effect societal change. The transformation of national specimens of adolescents may be tracked utilizing the High School longitudinal research from their high school experiences to later times when they are fully grown up. This model aids social change by attempting to identify the factors that influence a student's scholastic goal setting, and it is therefore essential in the development of professional preferences among respondents.

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