In preparation for writing your report to senior management next week, conduct the following descriptive statistics analyses with Microsoft® Excel®. Answer the questions below in your Microsoft® Excel® sheet or in a separate Microsoft® Word document:
RESPOND TO Ricardo and David post IN A 100 WORDS
A hypothesis is a prediction that you make about something that may be true or false, like “if I launch this product, I will sell a lot.” When you test a hypothesis, you are predicting the result of a particular event (launching a product) and then you are looking at how close or far away the result is from the prediction. A hypothesis test is a statistical test that is used to determine if a population parameter falls within a specified range. It is most commonly used in hypothesis testing for the null hypothesis, but can also be used when testing a difference between two populations. A two-tailed hypothesis test is used when you are testing the difference between two populations or you want predict that the result is going to be a positive or a negative outcome. A one-tailed hypothesis test is used when you are testing the difference between a population and its hypothesized value. This can be applied to several of my daily habits like driving to different routes back and forth to all of my projects to determine the quickest paths to also figuring out which project takes the longest to schedule based on the data provided.
Hypothesis testing is a concept of measuring the result of a given population statistically. It is the outcome of various measures of likely hood in a situation. Hypothesis testing should be used when one may want to confirm the outcomes of financial or business claims, or the outcome of an idea. One-tailed testing is based solely on a one direction based hypothesis. Two-tailed testing is one that includes both directions of hypothesis testing. I work in the manufacturing field, I feel that hypothesis testing can be performed on many aspects of the business. There may be questions to a specific piece of machinery that we may be looking into the performance of depending on various operators. This may be considered as one-tailed testing in my opinion. Questions raised to what the probability of success would be depending on an operators age, build, or knowledge can play on how successful the equipment can be. There is other testing that we can use hypothesis testing with such as repeatability on a product being ran many other different pieces of equipment, which in my opinion can be considered as two-tailed testing.
RESPOND TO THERESA AND T. PRYOR BE CONSTRUCTIVE AND PROFE SSIONAL WITH RESPONSE
The differences in data analytics from statistics is that data analytics shows patterns where as statistics shows theories. An example of data analytics would be when someone is analyzing their life, they could be thinking about their current situation, their past or their future, and thinking about decisions and outcomes of that decision. An example of statistics would be when the meteorologist asses how likely it is to snow or a certain day. The first type of analytic is descriptive , this tells us what has happen , an example would be month to month growth in sales. The second type of analytic is predictive , this is where one would predict what will happen , or what could happen, example would be trying to improve customer service to increase customers satisfaction. Lastly the third type of analytic is prescriptive, this analytic shows us what should happen in the future, an example would be investments. An example of raw operational data would be when a business analyzes the work flow of their employees and determine from that how long it should take for the majority to complete the same work on a regular bases.
T. PRYOR POST
The difference with data analytics is that it’s more of an observes trends and patterns which most company uses to get the facts in order to process a method Statistics validates those theories using scientific processes , statistical methods are used to pick up the patterns and correlations generated by data analysis and attempt to confirm them using the correct formula or method to process information for example in agriculture a farmer can tell when it’s going to be drought by the formula and and method process they use to determine the facts. In a statistician, even before the theories comes in exist to be (dis)confirmed. Data is the pre-processed and indexed by data engineers , even though data stores are often still too vast or complex to be navigable to investigators it’s always good to use the statistics measure of determination it helps to give the conclusion of the problem it prevents the paper trail with the business background of the problem and help to resolve and bring the theory into life with the right data and correct information.
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