In all above tests and analysis, we concluded very specifically to analyze any given set of data regardless of its sample size which is a finite set of data. Initially, we generated frequency tables to closely assess the data in terms of variables: risk, gender, purpose of loan, employment and job. Through these analyses we concluded that in our sample size of 1000 how many are good risks and how many are bad risks. We came to know that more than 60 person of the population show good risk. Then we analyzed the number of credits at the bank based on the ability to pay back the loan. It is found that 63.3 percent of the loans taken are paid back duly.
Similarly in part 2 we tested hypothesis, which is a claim that a researcher makes in order to see whether the previous claim is true or the evidence provided to prove that claim was sufficient or not. We made a claim that people who have not taken any loan has a mean risk above 0.38 which was wrong. Hence, we rejected the alternate claim that we made and accepted the null hypothesis. A certain significance level is maintained, which is usually 5 percent, to see the occurrence of error in our test. This significance level is the criteria for the probability of our test to see the occurrence of error in the test. For instance, if the significant value in one sample t-test is higher than 5 percent we accept the null hypothesis and when it is lower than 5 percent we reject null hypothesis which mean we have accepted the alternate hypothesis.
Regression model, hypothesis testing, correlation are all the most essential tools of statistics to make decisions. These tools are used on a large scale in different industries and by government bodies to analyze a huge set of data such as the US bureau of Statistics, who is responsible to give a very brief overview of the whole economy, provide an industrial overview etc. similarly, if we want to see the affect of the variable rain on the variable sales, we use correlation and see the association between these variables. If the correlation is 1 or close to one, relationship is strong and direct but if it is 0 or close to zero then it is weak. The association can be indirect when the correlation is in negative. There are several other tools in SPSS which can be used for descriptive purposes as well as for analysis and decision making.
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