Statistical Package For Social Sciences Essay

The women in the study were separated into two cultural categories. The Western group contained 30

2. Results Analysis: contains a summary and statistical analysis of the results to each question in the survey.

Purpose of this Project,to find out the releationship between population and the square of countries is tried to be understood.

Analysis and prediction of a dichotomous outcome: regardless of whether an understudy will prevail in school, whether a youngster ought to be named learning crippled (LD), whether an adolescent is inclined to participate in hazardous practices, et cetera. Customarily, these examination questions were tended to by either common slightest squares (OLS) regression or straight segregate work investigation. The two procedures were in this way observed to be not as much as perfect for dealing with dichotomous results because of their strict measurable presumptions. Logistic regression was proposed as an option in the late 1960s and mid 1970s (Cabrera, 1994), and it turned out to be routinely accessible in measurable bundles in the mid-1980s.

Values p < 0.05 were considered significant in the analysis. The data were collected on a structured pro forma sheet and were analyzed by use of SPSS ver. 17 (SPSS Inc., Chicago, IL, USA).

Statistical data were collected by using the Statistical Package of Social Sciences using a paired variance t-test (comparing before - after). An independent t - test was also used for comparison between the two groups (equal variance). The results were reported as mean ± SD.

From a total of 249 study participants, 220(88.4%) were included in the study. Out of them 193(87.7%) were male and the mean (SD) age of the participants was 30.2(5.5) with a minimum and maximum of 22 and 56 years, respectively. Regarding marital status, 3(1.4%) study participants were divorced and they were merged with married category during analysis. Socio-demographic data of the study participants are stratified by marital status and sex in Table 1.

Primary data will be used to answer the research questions guiding this research. The population, sample and procedure will be covered in the following sections.

The strength of each predictor (independent) variable influence on the dependent variable can be investigated via standardized Beta coefficient. Hence, the regression coefficient explain the average amount of change in dependent variable that caused by a unit of change in the independent variable.

Statistical calculations were performed using Microsoft Excel 2010 (Microsoft Corporation, NY, USA) and SPSS (Statistical Package for the Social Science; SPSS Inc., Chicago, IL, USA) version 15 for Microsoft Windows. Student’s t and χ2 tests were used to compare continuous and categorical variables respectively. A probability value (p value) of less than 0.05 was considered significant.

How many unknown parameters are there? Can you name them? How many populations will be sampled? What are conceptual populations?

Logistic regression is also called Logit regression or Logit mode. It is developed by David Cox in 1958. Logistic regression is an analogous method for the multiple linear regression. Unlike multiple linear regression, here the outcome or response variable (categorical) is all or none that is the dependent variable is dichotomous or binary. Here the response is defined as an indicator (dummy)

Regression Analysis is relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. For exploring such issues, an investigator assembles data on the underlying variables of interest and employs regression to

This chapter starts by presenting the findings obtained from the research. The analysis of the findings including descriptive statistic would be delivered afterward. The data is collected and then processed in response to the problems posed in early chapter of this dissertation. Before jumping into main analysis of this study, there would be some preliminary analysis phases, such as data inspection, recoding answers, and calculating response rate.

The participation of youth with older age group (26-35 years) are slightly higher (59%) than the younger respondents (18-25 years). It may be a reason that older age youth are living in the place all the times and face the problems either personal level or community level, so they would be coming forward to solve their problems. The participation of male and female is almost same. however, Female participation is slightly higher (56%) over male (55%). There is higher variance among respondents belonging to different castes. The lowest participation is among ST (38%), followed by OBC (47%) and General (56%). The participation of SC is highest among all, which is double (76%) of ST. The awareness index of SCs are highest (58%), so their