Importance of Business Analytics for Tendencies- myassignmenthelp

Question: Write about theImportance of Business Analytics for Central Tendencies. Answer: Descriptive Analysis This analysis techniques is usually categorized into two major groups Measures of Central tendencies, and Measures of Dispersion. The Measures of Central Tendencies are further branched to Mean, Median, and Mode. The Measures of Dispersion are further subdivided into Range, Variation and Standard Deviation. Descriptive Analysis Taxonomy Measures of central tendencies Technique: Mean Purpose: Determining the Central tendency of a given set of number Functionality: Sums up all set of number and divides by the count of the numbers Assumptions: The set of numbers are from a normal distribution. Method of validation: Calculate the mean of another sample and plot the numbers in a histogram to check for normality in distribution. Sample use case: Find the mean sales made by a business in a year Technique: Median Purpose: Describe the middle of a set of data that does not have an outlier. Functionality: Numerically sorts all numbers then picks the middle value for an odd data set, and sums the two middle values divided by two, for an even dataset. Assumptions: The frequency distribution of the data is skewed. Method of validation: Calculate the median of another sample and plot the numbers in a histogram to check for skewness in frequency distribution. Sample use case: Determining the value closest to the average salary of the office workers without including the CEOs salary or the cleaners salary. Technique: Mode Purpose: Describes the set of values that appears most often in a set of data. Functionality: checks for the value with the highest frequency Assumptions: The set of data doesnt have any outliers Method of validation: Plot the numbers in a boxplot to check for any outliers. Sample use case: Find the highest sales made by a business in a year. Measures of dispersion Technique: Range Purpose: Describes the set of data between the largest and the smallest value Functionality: Subtract the smallest value in the data from the highest value. Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Find the difference between the highest sale and the lowest sale. Technique: Variance Purpose: Describes how far the data deviates from the mean Functionality: Subtracts the data from the mean then squares the difference and finally find the average. Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Compare how small or big sales of a salesperson deviates from the average sales when rewarding bonuses to employees. Technique: Standard Deviation Purpose: Measures the amount of variation is a given data Functionality: Finds the square root of the average of the sum of the squared deviations from the mean. Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Find the spread of sales made across the year. Measures of position Technique: Z-score Purpose: Measures the how manystandard deviationsan element is from the mean. Functionality: subtracts mean from an observation and then divides by the standard deviation. Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Assuming the mean is 50 and standard deviation 5; we obtain the z score of an observation which is 60 as; Technique: Percentiles Purpose: refers to a measure that is used in statistics to indicate the value below which a given percentage of observations in a group of observations fall.. Functionality: data is arranged in ascending or descending order and then the required percentile is obtained. Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Assume we have 1, 3, 5, 7, 9, 11 and 13; The 50th percentile is 7 Technique: Quartiles Purpose: Is a measure that divides a list of numbers into quarters. Functionality: data is arranged in ascending or descending order and then the required quartile is obtained. Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Assume we have 1, 3, 5, 7, 9, 11 and 13; The 3rd quartile is 7 Technique: Interquartile range Purpose: Refers to a measure of variability, based on dividing a data set into quartiles. Functionality: Subtract Q1 from Q3 Assumptions: The data is from a continuous distribution Method of validation: Calculate the arithmetic average to check if the data is not categorical Sample use case: Assume we have 1, 3, 5, 7, 9, 11 and 13; The interquartile range is 10-4=6. References Bartlett, R. (2013). A Practitioners Guide to Business Analytics . Galit, S., Otto , K. (2010). Predictive vs. Explanatory Modeling in IS Research. Isson, J. P., Harriott, J. S. (2013). Win with Advanced Business Analytics. Miller, K. (2012). Big Data Analytics in Biomedical Research. Negash, S. (2004). 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