DQ1
Descriptive statistics is the course of data analysis that helps in the description and summarization of data in an important manner, for instance through the usage of patterns do show data. It is simply data analysis that is not conclusively used. They are important in data presentation since they allow us to present data in a momentous way that allows for easier reading and understanding of data. Descriptive statistics allows us to describe data through graphs and statistics in an important and accurate way. Two statistics that are used in describing data are:
Measures of central tendency: these are methods of describing the mean position of a frequency division for a group of data. For instance in a students work score, the frequency distribution is basically the division and pattern of marks scored by the student ranging from the lowest to the highest score. This central position can be described using various statistics that include the mode, median, and the mean.
Measures of spread: it is a way of summarizing a set of data through describing how spread out the score pattern is. For instance, in 100 students, the mean score maybe 65 out of 100, but not all of the students will have attained the 65 marks. In contrast, their scores will spread out since some of the students would have higher scores than others students. This measure helps us to summarize the degree data spread for the scores. In order to describe this spread, we use various statistics such as quartiles, standard deviation, range, variance, and absolute deviation
For example, in my business of bakery, I normally get the number of different preferred types of cakes, and find the mode of the most preferred cake.
DQ 2
Inferential Statistics
These are statistics used to make inferential statements about a population. Inferential statistic is a technique that relies on the random variety of a sample from a population. It does not engross taking samples all the individuals of a population, but rather getting a representative sample that can be used in finding an interference of the population in general. The adoption and usage of inferential statistics is a keystone of research on population since it is tricky to review each individual in a given population. The researchers involved in inferential statistics use a sample representative of a population to uphold their basis and argument.
The intention of inferential statistics is to exhibit that an experiential difference is real and is not a coincidence of the sample population. This is achieved by creating a differing hypothesis and trying to see if it can be proved. In order for the inferential statistics to thrive, the population size should be superior, since the minor it is, the higher the risk error in sampling the representative of a population. The inferential statistics may also use the methods of parameter measurement and testing of arithmetic hypotheses. In my business of bakery, I would use inferential statistic when I want to find the sample age of my customers who prefer cakes to pie. My sample would present only a small percentage of my customer who prefers cakes to pie, thus this statistics would guide me estimate the number of my customers who loves cake to pie. The inferential statistic will direct me to produce more cakes than pies since my common customers are young adults below the age of 25.
