Essay On Data Analysis

I intend to use a test, or a statistical hypothesis, to determine the effective relationship between the hours of human life spent for watching media and the effect on the average age of both men and women. The data that I chose to use in order to achieve the result is: the quantitative data, which deal with the time that these men and women spend watching media, and the descriptive data, which show the gender and age. Then, I will formulate the hypothesis: Null = H0 that means the test doesn’t have any effective relationship on the average age for the men and women for watching media. On the other hand, experimental hypothesis or alternative hypothesis is = H1 that means the test is workable and has an effect on my study.
My sample will cover ten men and women, who spend their time watching media. For the statistical study, I intend to collect many types of variables, depending on advantages and disadvantages. For advantages, I will determine how many hours both genders spend for watching individually. For disadvantages, I will focus on the differences between the measures of each value. Each result will depend on the total of each gender, women or men, who are watching media per day. In this case, I will try to find a relationship between the variables that I have in my study. If the genders can evaluate the time they spend for watching media per day, I should anticipate how many hours they might spend on this task. Moreover, determining genders who watch media will help to reduce the number of hours they spend on healthy living. The result for each gender can give a positive reflection for spending their time. The data analysis technique will help to collect the information of both genders on my study. I can use the trend line on the Microsoft Excel to determine and find out the relationship between the data that I have in my study on each gender, for spending time watching. It will also help to get the slope and intercept of the best fitting regression line. Also, the analysis can help to calculate correlation coefficient of each gender. Additionally, the analysis might distribute the variances between genders. The measurements taken can give gradually the result lines for both of them. By using correlation coefficient, I can to obtain the hours that both genders spend watching: calculated between -1 and +1, they represent the linear dependence of two variables or sets of data. Additionally, the linear regression is a significant smooth curve that fits to the set of paired data in regression analysis, to show the relationship between the human time spent on watching media and the genders watching media. To explain a linear regression, the curve is a straight line.
I think the T- test is one of the important methods that are appropriate to be used in statistical hypothesis. The T- test can help to determine the relationship between spending time on media and gender usage, when I collect the data. The goal of the test is to give a concrete sense of, whether a difference between two genders is meaningfully large or independent of, or whether the difference is statistically significant. By factor analysis in this test, I can I identify an underlying factor, which is on the large set on variables. Clustering a large number of gender variables to small number of gender variables, the factor is going to represent each set. Consequently, the analysis variance can use ANOVA for identifying and measuring the variance between them, within the collection of data.