Explain how businesses use analytics to convert raw operational data into actionable information.

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Respond to the Discussion questions in 175 words Analytics is used in all business roles. In management, it is important to understand what type of analytics to employ depending on your position and the data available. To analyze business trends, an HR manager will look at market trends and hiring data like salaries in their local market to be competitive and attract talent. Respond to the following: How is data analytics different from statistics? Analytics tools fall into 3 categories: descriptive, predictive, and prescriptive. What are the main differences among these categories? Explain how businesses use analytics to convert raw operational data into actionable information. Provide at least 1 example. Consider your role in the organization you work for (or another organization you’re familiar with). How is data analytics important to your job and your organization? If it is not, how could you and the organization use data analytics to improve performance? As a manager, an important function to measure performance is to complete a data analysis comparing scores or metrics. While technology makes it possible to easily acquire data, only you can truly understand what it means; and only choosing one or two data points may not show you an accurate picture of what’s happening. Part of your job as an HR manager is to monitor the performance of trainees as they complete a 4-week paid training program, which includes a product knowledge test. You have to determine which trainees can complete the program, which may require remediation (additional training and re-testing), and which should not continue with the training (termination) based on their scores on the product knowledge exam. Review the mean, standard deviation, and 5-number summary of the trainees’ exam scores below. You can also review the individual exam scores and functions used to calculate the descriptive analyses in Excel, if desired. Mean: 75.5 Standard Deviation: 19.57 Minimum: 18 Quartile 1: 67.75 Median: 80.5 Quartile 3: 87 Maximum: 99 Respond to the following: Would you prefer to use the mean or the median in this dataset’s measure of central tendency? Why? Based on this training class’s scores, what scores do you think should be considered for completion, remediation, and termination? How did you come to that conclusion? Do you think that these scores should be the threshold for all training classes? Why or why not? Many business activities generate data that can be thought of as random. For example, a service manager at an auto shop needs to understand the data for cars coming in for services like oil changes. A variable of interest is the amount of time necessary to service the car, since service time will vary with each car. They can often capture the most relevant characteristics with a simple probability distribution model. The service manager can then analyze the model to make predictions and drive decisions, such as how many technicians to schedule to service demand on a Saturday afternoon. Respond to the following: How would you differentiate a discrete from a continuous random variable? Provide a specific example to illustrate the difference. Provide a scenario when you use might use one type of random sampling method in your industry. Explain why you would choose this method in this scenario, even if another random sampling method could be used? A production manager can use hypothesis testing to test assumptions and theories. These assumptions are tested against evidence provided by actual, observed production data. A statistical hypothesis is a statement about the value of a population parameter that we are interested in. Hypothesis testing is a process followed to arrive at a decision between 2 competing, mutually exclusive, collective exhaustive statements about the parameter’s value. An industrial seller of grass seeds packages its product in 50-pound bags. The production manager is responsible for ensuring the packages contain the amount of product they are supposed to. As part of routine quality assurance, the production manager randomly samples a batch of 9 bags to determine if the bags of seed are properly filled or not. Watch “Hypothesis Testing Basics” from LinkedIn Learning and “Defining Hypotheses” by Lori Seward in this week’s learning activities before responding to this discussion. Review the weight of the samples in the following table. You may also view the sample weights in Excel, if desired. Sample Weight (pounds) 1 49.5 2 45.6 3 46.7 4 47.7 5 47.6 6 48.8 7 50.5 8 48.6 9 50.2 Respond to the following: What is the null hypothesis and alternate hypothesis for this scenario? Now, share a scenario where you could use hypothesis testing in your industry or organization. Explain why you might need to test a hypothesis. What is your null hypothesis? What is alternate hypothesis? How does the critical value fit into this scenario? What information is needed to test the hypothesis? Statistical models help us describe and summarize relationships between variables. Understanding how process variables relate to each other helps businesses predict and improve performance. A marketing manager wants to understand the relationship between advertising and sales. When a new advertising campaign rolls out, they will look at the impact on total sales to determine if it is having a positive influence on sales, and if the cost is truly making a big enough difference. Review the Advertising vs. Sales chart. Note the scatter plot and the regression equation in the chart. You can review the same data and chart in Excel, if desired. Respond to the following: Do you observe a relationship between both variables? What does the slope tell us? Is the slope significant? What is the intercept? Is it meaningful? What is the value of the regression coefficient, r? What is the value of the coefficient of determination, r^2? What does r^2 tell us? Share a business scenario in which using a model could be beneficial. A retail store manager uses time series models to understand shopping trends. Time series models are particularly useful to track variables such as revenues, costs, and profits over time. Time series models help evaluate performance and make predictions. Review the scatter plot of the store’s sales from 2010 through 2021 to answer the questions. You may also review the annual sales data and chart in Excel, if desired. Respond to the following: Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components? The model can be additive or multiplicative. When do you use each? Review the scatter plot of the exponential trend of the time series data. Do you observe a trend? If so, what type of trend do you observe? What predictions might you make about the store’s annual sales over the next few years?






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