Discrete data refers to a group of data which contain elements that are specific value that does not overlap with each other. Numeric data comprised of whole numbers is a good example of discrete data. Some qualitative data also meet the criteria of discrete data, a good example being Sex where one can be a male or female, colour of the marble (red, green, black). Continuous data refers to information which has elements that overlaps. If the weight of students attending a give course is collected the values will occupy a random range. Each student will have a specific weight but if the data is represented on number line overlapping occurs. Some continuous data can be manipulated during analysis to discrete data. If we categorize the weight of student into underweight, normal, and overweight the data that had been collected will be presented in secondary format which is in discrete format.
Random variable
Random variable refers to expected observation and is always dependent in a research. In rolling a dice experiment the number that appears on the face of the dice is the random variable. Each value has equal chance of being observed and recorded when we roll a dice.
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Results
The primary data from my research I consider it to be discrete data. The reason why I consider it discrete is because each observation has a whole number hence there would be no overlapping on observation. Secondly, if my observations as a set all elements and subsets correspond to a general rule of 0 ≥ n ≤6.
Conditions for Probabilities
Conditions for probability distribution are met. There are two conditions which must be met by probability distribution date.
The probability oft is greater than or equal to zero but less than one (0 ≥ P(X) < 1).
The summation of all P(X) must be one.
| Event (X) | F | P(X) |
| 1 | 3 | 0.15 |
| 2 | 4 | 0.2 |
| 3 | 3 | 0.15 |
| 4 | 4 | 0.2 |
| 5 | 3 | 0.15 |
| 6 | 3 | 0.15 |
| SUM | 1 |
Binomial probability distribution
My experiment was not a binomial probability distribution. For an experiment to be considered binomial it should have two possible outcome at any event. In my research any trial would give six possible outcome. Tossing a coin would give head or tail and is a good example of binomial distribution data. The second factor Probability of the event to occur if several independent trials are conducted does not follow Bernoulli trial rule shown below (Glasgow Caledonia University, 2018).
If we use the first four trials we have four outcomes yet possible outcomes are six this means some of possible observation cannot be accounted by the formula.
Measures of central tendency are statistical analysis results which indicate the area the data tend to converge. There are three analysis results on central tendency; mean, median, and mode. The mean, mode, and median give an insight on areas that need a lot of focus depending on research being done (Jones & Bartlett, 2015). Measures of central tendency can be important in health service delivery to clients since it can enable the healthcare providers to know major areas they should focus on in diagnosis, management and treatment. If a patient who is managing a health problem after consuming a certain diet health officer can record outcome. The factor aggravating the situation can be identified depending on data and be able to manage the situation better. Measures of Central Tendency can be used to manage mental health problems within the population by focusing on the most affected age group, gender, group based on reported reliable data evaluation (Jones & Bartlett, 2015).
Null hypothesis 1- mental Health problems effect individual adults aged 18-35 than those above 40 years in our country.
Alternative hypothesis 1 - mental health problems occurrence does not depend on age group.
Null Hypothesis 2- Women are more affected by mental health problems in our country than men.
Alternative hypothesis 2- there is no difference between male and females on mental health problem occurrence in our country.
References
Glasgow Caledonia University. (2018). Probability and Probability Distributions. Retrieved from Glasgow Caledonia University:
Jones & Bartlett. (2015). Measures of Central Tendency. Retrieved from Jones Bartlett Pub: http://samples.jbpub.com/9781449649227/34032_ch02_final.pdf
