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MATH GURUU
|
Case Processing Summary |
|||||||
|
Valid |
Total |
||||||
|
N |
Percent |
||||||
|
Suicide 1* Genders |
984 |
49.8% |
990 |
50.2% |
100.0% |
|
Chi-Square Tests |
|||||
|
|
Value |
df |
Asymptotic Significance (2-sided) |
||
|
Pearson Chi-Square |
32.411a |
16.00 |
0.01 |
||
|
Likelihood Ratio |
27.331 |
0.003 |
|||
|
Linear-by-Linear Association |
4.017 |
1.00 |
0.03 |
||
|
N of Valid Cases |
|||||
|
a. 9 cells (33.3%) have expected count less than 5. The minimum expected count is 0.48. |
Research Question:
Doesn’t a person’s social class determine their view on suicide?
Variables:
i) The dependent variable I suicide.
ii) The dependent variable is gender (sex) Variable is ordinal.
1. Write your research hypothesis (H1) and your null hypothesis (H0).
H1: gender (sex) not same on the issues related to suicide
H0: gender (sex) same on the issues related to suicide
2. Identify and record your level of significance: either .05 or .01.
Alpha values is either 0.05 or 0.01. We take the values of significance is 0.05
1. Complete the test using SPSS.
Test is shown above.
4. Identify the number under Sig. (2-tail). This will be represented by “p.” compare the numbers in steps 2 (level of significance) and 4 and apply the following rule:
If p < or = level of significance, than you reject the null hypothesis
As we can see in the above analysis that the p values (P-value=0.01) is less than 0.05 (level of significance). Therefore we reject the null hypothesis.
5. Determine what to do with your null and explain this to your reader. Be sure to go beyond the phrase “reject or fail to reject the null” and explain what that means to your research.
As I done the chi square test. My deepened variable is gender (sex) and dependent variable is suicide form the SPSS output we can easily see and conclude that the SPSS output the Chi-Square value is 32.12, it is shows our chi square statistics and the degree of freedom is 16 and the significance p-value is 0.01. the statistical test and analysis shows the significance p level is less than cut off value we are fail to accept the null hypothesis conclude that there signifies evidence to support the alternative hypothesis that gender (sex) not same on the issues related to suicide.
Now I additionally conclude that the bottom of the Chi square values shows that 33% of the cells have expected frequency that are lessor than 5.
Professors comments:
Professors comment: Hi Hussain you still need to include your crosstabs, with percentages on the columns here. Please recomplete your work with this item included.
Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Life of the person
Suicide
98
4
49.8%
99
0
.0
5
0.
2
0%
1
9
7
4.00
100.0%
0
1
2
3
4
5
6
7
EIGHT OR
MORE
TOO LITTLE
22
6.00
11
1.00
208.00
106.00
51.00
1
5.00
1
0.00
1.00
5.00
73
3.00
ABOUT
RIGHT
5
7.00
18.00
45.00
18.00
17.00
5.00
3.00
4.00
6.00
173.00
TOO MUCH
20.00
1
2.00
20.00
15.00
7.00
1.00
2.00
1.00
0.00
78.00
303.00
141.00
273.00
139.00
75.00
21.00
15.00
6.00
11.00
984
.00
NUMBER OF suicide Cross tabulation
Count
NUMBER OF Suicide
Total
IMPROVI
Suicide
|
Measures |
|||||
|
Value |
Asymptotic Standard Error |
Approximate T |
Approximate Significance |
||
|
Ordinal by Ordinal |
Gamma |
0.075 |
0.050 |
1.5020 |
0.133 |
|
N of Valid Cases |
984.00 |
||||
|
a. Not assuming the null hypothesis. |
|||||
|
b. Using the asymptotic standard error assuming the null hypothesis. |
The above-mentioned data (table) shows the relationship between the numbers of suicide) Independent variable (IV) )
Individual have and their options on increasing the life (dependent variable)
Hypothesis Research: The opinion of the people on person life affected by the number of suicide they all have.
Null hypothesis:
From the above analysis we can see that there is relationship between dependent variable and independent variable so we can conclude that a person has the right to end his or her own life if this person is tired of living and ready to die.
We used the Gamma because as my dependent variable (DV) is used because my Dependent variable “Suicide” is ordinal also that my Independent Variable Number of suicide is not nominal. The value of 0.075 it is telling us that the number of suicide an individual has affects the life of the person by 7.50%. Because of this information showed in the charts above, the relationship is weak. However, because of the positive sign of 0.075 there is an indication that both life (dependent variable) and the number of suicide Independent variable (IV) are positively strong associated? This is concluded and reflected in the way in which the variables are coded where, as one goes increase and the one as well.
PROFESSORS COMMENTS:
Good work so far on your analysis, it looks like you have a very weak relationship based on gamma value of .075. However, I am not sure what variable you are using in your independent variable and dependent variable. Could you let us know that? Then I can help you formulate your research question a bit better and your hypothesis. Because you formulate we don’t know the exact variables you are using, we also can’t tell if you have used the right measure of association.
2
StatisticalAnalysis and Conclusion
Statistics Homework
Student Name:
Professor Name:
Institution Name
Date: 19th Jan,
20
18
Introduction.
One of the most usual applications of Statistics is describing a set of data using estimation. Given a set of data, for instance, the data on the opinion of the people on person life affected by the number of suicide they all have. By analyzing and examining the raw data, we can make and draw logical conclusions or even compare, contrast or rank of the data or establishments based on the specified attribute. Evaluating the status of your business and social comparing and contrast by considering its attributes that affect customers and people is a very important aspect for the growth and development of any business establishments and for social activities. In our research the social investigation of the suicide
The use of various descriptive and inferential statistics measures is one of the most effective ways to examine properly these social attributes. To name some, one needs to employ the application of measures of central tendencies, measures of variability, and positions, estimation and even correlation, hypothesis testing. Regression model and many more. Once data are gathered and analyzed, one will be aware of the attribute given the most importance by the social act, and also those given the least importance. This paper will focus on methods of estimation. And hypothesis testing (Walpole, 1982)
a) This research paper elaborate the importance and using of elementary statistical concepts.
Statistics is the connected branch of probability theory and hypothesis which manages true issues, endeavoring to make conclusion in view of perceptions. Two noteworthy errands in probability are estimation and hypothesis testing. Statistical estimation can be recognized in parameter estimation and forecast, prediction and can be performed either on a point premise or on an interim premise. Utilizing of statistical estimation in designing applications incorporate the estimation of parameters of likelihood conveyances, for which a few strategies exist, and the estimation of quintiles of disseminations. Measurable hypothesis testing is likewise a critical apparatus in building contemplates, not only in typical decision making processes, as well as in more investigative undertakings, for example, in distinguishing connections among various geophysical, and especially hydrological, forms. This ideas are quickly talked about both in a hypothetical level, to clear up the ideas and stay away from abuses, and a more handy level to show the utilization of the ideas. (Walpole, 1982)
b) Some statistical using SPSS tools done and the output is shown below based in the provided data file of the suicide and gender relationship investigation.
The above mentioned data (table) shows the relationship between the numbers of suicide Independent variable (IV)
Individual have and their options on increasing the life (dependent variable)
Hypothesis Research: The opinion of the people on person life affected by the number of suicide they all have.
Null hypothesis:
F
rom the above analysis we can see that there is relationship between dependent variable and independent variable so we can conclude that a person has the right to end his or her own life if this person is tired of living and ready to die.
Analysis:
We used the Gamma because as my dependent variable (DV) is used because my Dependent variable “Suicide” is ordinal also that my Independent variable Number of suicide is not nominal. The value of 0.0
7
5
it is tells us that the number of suicide an individual has affects the life of the person by 7.
50
%. Because of this information showed in the charts above, the relationship is weak. However, because of the positive sign of 0.075 there is an indication that both life (dependent variable) and the number of suicide Independent variable (IV) are positively strong associated? This is concluded and reflected in the way in which the variables are coded where, as one goes increase and the one as well.
Create a crosstab table with the variables that you have chosen. If you have two IVs, you need to have two separate crosstabs. Be sure that column percentages are included in the table. Please provide a detailed overview of your bivariate analysis,
Dependent variable is suicide 1 and gender is the independent variable the output of the cross tabulation is shown underneath
|
Suicide1 * Gender Cross tabulation |
||||||
|
Count |
||||||
| Gender |
Total |
|||||
|
Male |
Female |
|||||
| Suicide1 |
Undecided |
11 |
10 |
21 |
||
|
Yes |
5 |
1 6 |
||||
|
No |
7 | 6 |
1 3 |
|||
|
29 |
50 |
Conclude that a larger part of males and females are undecided on whether critically ill patients ought to have a privilege to confer suicide or not. This conclusion is not the same as the males since a more noteworthy bit of males trusts that the in critical condition patients ought to have a privilege to confer suicide. My hypothesis is hence right.
c) Qualitative data is an absolute estimation communicated not regarding numbers, yet rather by methods for a characteristic dialect depiction. In measurements, it is regularly utilized reciprocally with “categorical” data.
In spite of the fact that we may have categories, the categories may have a structure to them. At the point when there isn’t a characteristic requesting of the classifications, we call these nominal categories. Cases may be sexual orientation, race, religion, or game.
Quantitative information or data is a numerical estimation communicated not by methods for a characteristic dialect depiction, yet rather as far as numbers. Be that as it may, not all numbers are constant and quantifiable. For instance, the social security number is a number, however not something that one can include or subtract
d) Examine the contrasts amongst descriptive and inferential statistics and their utilization in the Descriptive statistics are the fundamental statistics that portray what is happening in a population or data index. While inferential statistics reveal to us essential data about the populace or informational collection under investigation, inferential insights are delivered by more perplexing scientific calculation, and enable us to derive inclines about a bigger population in light of an investigation of an example taken from it. We utilize inferential statistics to look at the connections between factors inside an sample, and after that make speculations or forecasts about how those factors will relate inside a bigger population
e) In making a literature reviews and survey, take note of that it is regularly this third layer of information that is referred to as “true” despite the fact that it frequently has just a free relationship to the essential examinations and optional literature reviews. Given this, while writing surveys are intended to give a review and blend of apropos sources you have investigated, there are various methodologies you could embrace contingent on the kind of examination supporting your examination.
f) When it comes to statistical analysis, there are two classifications: descriptive statistics and inferential statistics. In a nutshell, descriptive statistics intend to describe a big hunk of data with summary charts and tables, but do not attempt to draw conclusions about the population from which the sample was taken.
ANOVA test based on the Mock study
Test Statistics: One Way ANOVA
|
Source of Variation |
Sum of Square |
d.f |
Mean Sum of Square |
F |
P Value |
|
Between |
80.093 |
3 |
26.698 |
12.811 |
0.000 |
|
Within |
41.679 |
20 |
2.084 |
||
|
121.773 |
23 |
Effect Size = 0.5203
Conclusion: 0.001 < 0.05, reject null hypothesis conclude that there occurs a noteworthy variance among result of evidence by the behavioral scientists as supposed by the Judges, Attorneys, Jurors and Law Enforcement officials.
PROFESSORS COMMENTS: use the GSS data set that you will download in your account.
Logins:
Power2017 Login id: keelin.leger@mycampus.apus.edu login into: https://estore.onthehub.com/WebStore/Account/OrderDetails.aspx?o=5e429e3f-1fe0-e711-80fa-000d3af41938 Product key to get in is:415899f4f4b0e0abf028
This the link for GSS data. In it it shows the spss download as well. Make sure it’s 2012. Any. Questions I’m here to help. http://gss.norc.org/get-the-data
GSS DATASET 2012 info http://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M63&product_isbn_issn=9781285458854&chapter_number=0&resource_id=21&altname=2012%20GSS%20Data%20Sets Download it and it should be a whole lot easier. Don’t forget we’re on suicide and do a person social class determine their outlook on suicide.
References
Walpole, R. (1982). Introduction to Statistics. (3rd ed.). Prentice Hall Publication.
Fink, A. (2014). Organizing Your Social Sciences Research Paper: 5. The Literature Review.
GUY, M. R. (2012). DESCRIPTIVE VS. INFERENTIAL STATISTICS: WHAT’S THE DIFFERENCE?
Koutsoyiannis, D. (2016). Elementary statistical concepts.
ThoughtCo. (2010). Understanding Descriptive vs. Inferential Statistics.
(A)
Myresearch questions
is: is to figure out what people in the particular social group think about suicide. In order for me to figure this out I had to look at social class and look.at both males and females in this claas.
(B)
1
.
5
0
random people of the area
2. The general representation of the area
3. The much recourses required I can funding.
4. On the weekend at around the mall.
5. By meeting face to face and one by one
(C)
Variables (you have expected to have only one DV and a minimum of one IV. (
10
pts)
My IV(s): if you have multiple IVs, provide information for EACH IV using the format below.
IV Variable name in SPSS: I (
Male
) ii (female)
IV Question (as asked to the respondent verbatim) _____ Are you male or female?
IV Answer categories:
i) -male & ii –
Female
IV Level of Measurement
No
minal
My DV: only ONE DV is required for your final portfolio
DV variable name in SPSS: 0-
Undecided
, 1-
Yes
and 2- No
DV Question (as asked to the respondent verbatim)
Do you think a person has a right to end gender life if this person has an incurable disease?
DV Answer categories: Undecided, 1- Yes and 2- No
DV Level of Measurement: Nominal
(D)
Gender
Frequency
Percent
Valid
Percent
Cumulative Percent
29
.0
5
6
.90
58.00
58.00
21
.0
41.2
0
4
2.0
0
100.0
0
Total
50
.0
98.0
0
100.00
Missing
System
51
|
Suicide1 |
||||||||
| Undecided | 21 | 41.2 |
42.0 |
|||||
| Yes |
16 |
31.4 |
32.0 |
7 4.0 |
||||
| No |
13 |
25.5 |
26.0 |
|||||
| 50 | 98.0 |
My respondents consisted of more males and females. Out of the 50 respondents, males were 29 while females were 21. Among all the respondents those undecided on whether people with incurable diseases should have a right to commit suicide or not were 21, those who said yes were 16 and those who said no were only 13.
]
(E)
Conclude form the graph above, plainly my respondents comprised of more male and females. Male were 58% while females were 42 % of aggregate respondents. Among every one of the respondents larger part were undecided on whether individuals with uncurable infections ought to have a privilege to confer suicide or not , this were nearly trailed by other people who said yes and the minority were the individuals who said no.
(F)
Option 1: Running measures of central tendency and dispersion
• [Running measures of central tendency and dispersion
i) Recoding
ii) Index construction
One should look over the procedures you explored about the topic you are thinking about in more greater detail. Outline your discoveries and glue all pertinent yield information here in this record.
My information that I provided is maximized and clearly coded and laid out I a way that we can enhance a conclusion by making a crossing and a arrangement to come out with data from both sides of the factors. The information that I provided here is ostensible by nature by saying that all measurements of focal inclination will have value
Be that as it may, for the variable suicide1 the modular class is undecided. This implies larger part of the respondents were undecided on whether individuals with incurable infections ought to have a privilege to confer suicide or not. For sex, the modular class was male, implying that dominant part of the respondents were male.]
(G)
Research hypothesis: My hypothesis is males and females regardless of social class differ on the topic of suicide
Null hypothesis: Are Males and females on the same page as it relates to the issues related specifically about suicide
___________________________________________________________
Your DV, Suicide1, will go into the ROW.
Your IV, Gender, , will go into the COLUMN.
[
|
Suicide1 * Gender Cross tabulation |
||
|
Count |
||
|
11 |
10 | |
| 5 | ||
| 7 | 6 | |
| 29 |
According to the research above a particular class both male and female say that they are undecided if a person should terminate because of illness. In any case, with regards to the individuals who picked yes or no, a more noteworthy extent of females feel that at deaths door patients ought not have a privilege to submit suicide. This conclusion is not the same as the males since a more noteworthy bit of males trusts that the in critical condition patients ought to have a privilege to confer suicide. My hypothesis is hence right.
Epsilon
Epsilon abridges rate distinction in rows of crosstabs. It is figured by subtracting the biggest % and littlest %. Most analysts trust that a rate contrast of over 9 % demonstrates a solid connection between the factors being cross tabulated.
| Epsilon | ||||||
|
% within Suicide1 |
52.4% |
47.6% |
||||
|
% within Gender |
37.9% |
9.7% |
||||
|
68.8% |
31.3% |
|||||
|
23.8% |
14.1% |
|||||
|
53.8% |
46.2% |
|||||
|
24.1% |
28.6% |
4.5% |
||||
|
58.0% |
42.0% |
|||||
|
100.0% |
Average difference =9.43% |
The distinction between rows is generally high and the normal contrast epsilon is high.
This implies there is a solid connection between the factors gender and Suicide1. This implies contemplations about suicide will be affected by ones gender orientation. My hypothesis is along these rows revise and is acknowledged. Conclusion: Males and females contrast on the issues related with suicide
PROFERSSORS COMMENTS: use the GSS dataset in lesson 1.
