Introduction
Predictive analytics is an advanced analytics technique. Predective analytics uses both new and historical data to foresee the result, activity, behavior and trends.
Statistics is branch of mathematics, mainly concerns about collection, analysis, interpretation and presentation of tons of numerical facts. Statistics is used in almost every field of research.
Head to Head Comparisons Predictive Analytics Statistics
Definition Predictive analytics is branch of the data analytics to predict the future events. Statistics in simpler terms is collection of numerical facts. It is the science of collecting, classifying and representing the numerical data.
Why it matters?
Predictive analytics can identify the risks and opportunities
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Branches Predective analytics is one of the types of Data Analytics. The other analytics are descriptive and prescriptive analytics. The two main branches of statistics are descriptive statistics and inferential statistics.
Key differences:
• Predective Analytics is used to make predictions about unknown future events. Whereas statistics is the science and it’s mainly used in ‘Research’. Statistics helps in making conclusion from the data by collecting, analyzing and presenting.
• For a business to bloom, it must collect and generate facts that reflect its current status. Statistics helps these facts or data to be changed into information, in order to support rational management decision making.
How it works:
• In Predective Analytics, predictive models use known results to develop or train a model that can be used to predict values for different or new data. This modeling provides results in the form of predictions that represent a probability of the target variable based on estimated importance from a set of input variables.
• Statistics summarizes the data for public use. There are two main statistical methods: Descriptive Statistics and Inferential
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• Big companies are using predictive analytics. For example open your Amazon site and take a look around the site. A huge percentage of the screen is devoted to “recommended” products, and each recommendation area is a slightly different predictive algorithm based on different data.
These recommendations are based on the search history of the items that user browsed, based on that a model will be trained, so when user opens the site he/she will be seeing all the relent items to the previous product that he/she browsed. It could be a brand of the product or the color or the style of the product. That strategy is to attract and engage the user on the website and obviously to by the products.
Here in the above example statistics will help to develop the prediction model, using its descriptive and inferential models.
Conclusion: Predective ‘Analytics’ and ‘Statistics’ are useful to analyze current data and historical data to make predictions about future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning and artificial
subsection{Recommending Unexpected Relevant Items} Once the forgotten items have been identified, we need to distinguish relevant ones from the rest. Given user taste shifts, as well as the changes in the system as a whole, not all unexpected items remain relevant, and consequently useful for recommendation. The key concept to identify relevant items is the extbf{relevance score} of the items at each moment. We propose four strategies to define the relevance score of each unexpected item.
Nate Silver’s The Signal and the Noise: Why So Many Predictions Fails- But Some Don’t serves as a guide to ordinary citizens on the dangers and benefits of prediction and forecasting in modern society. The book criticizes many of society’s so-called “experts and authorities” for their incorrect usage of statistical methodologies. Silver illustrates the statistical problems and progress that society has made through the usage of multiple examples and professions ranging from baseball predictions to seismology.
Week 3 Practice Problems 1. The class handout cites 3 basic purposes for studying statistics: data reduction, inference, and identification of relationships. In your own words, describe these three ideas in a couple of sentences each. Why do we study statistics? We study statistics for 3 reasons below: Data Reduction:
In the meantime, here are some ways in which the stats and terms that come up often in baseball make a lot of sense in the investing world as well. Averages Since baseball is played out over a long season, the statistics that are accumulated over that period of time can give viewers somewhat of an assumption about the likelihood of, for example, a batter getting a hit or a pitcher striking that batter out. In much the same way, average stock prices are a good way for investors to get an idea of the baseline of the stock and perhaps predict if any deviations from the norm are the beginning of a trend or an aberration before a return to the norm. Sample Size Baseball pundits often throw around the term sample size to explain whether or not results that have been achieved can be predictably repeated.
Statistics is used in a variety of ways in today’s society from calculating your insurance premium, what will happen in the stock market, who will win in the next Super Bowl, the outcome of the next political campaign, and other numerous events that occur in one’s life. Not many people realize how much these events skulp their life. In The Drunkard’s Walk: How Randomness Rules Our Life, Leonard Mlodinow discusses how chance, probability, and randomness reveal an astounding amount in our daily lives, and how we happen to misinterpret the significance of these events. Mlodinow informs you on those who fathered methods in some of the basic principles of probability, and how they happen to bring them about.
Other than utilizing it to examine patterns. The quantity of associations for the client to break down the distinctive
Companies like FedEx use them to determine the effects of price change or new services, and has seen 65%-90% accuracy. This ability to determine the best solution before it happens is an incredible achievement. Making changes that aren’t well receptive can cost your business customers, reputation, and much more. Putting out the right plan the first time can save your face and more importantly, you cash flow. For the city planner, predicting which bus stops will have the
Effective prediction of ratings from a little range of examples is very important. Also, the reliability of the collaborative recommendation system depends on the provision of a crucial mass of users. For example, within the movie recommendation system, perhaps there can be several movies that are rated by solely few individuals and these movies would be recommended terribly rarely, even if those few users gave high ratings to them. For the user whose tastes are uncommon compared to the remainder of the population, there will not be the other users who are particularly similar, resulting in poor recommendations. 1.4 Objective:
Classification If you are not an only child, have you ever wondered if being the oldest or middle child ever hurts you on being smarter than the other? In Jeffery Kluger’s essay, he discusses the difference in birth order and how it plays a big factor on being successful in life. Whether you are the first, second, or third born, it all hinges on the birth order. He talks about the different orders in the essay and that is what we are going to be talking about in the essay.
In baseball, nearly everything is a statistic. There is a statistics for a players average on certain pitches in certain places in the strike zone. There are statistics on how many more wins a player gives his team more than a replacement level player. Statistics, while not always pure, have helped the game evolve through changes, to a game where small market clubs can compete with teams like the Yankees.
These reports gather statistical data of files that we have in our office. However, the mathematical formulas to come up with these statistics are simple and require basic operations like addition, subtraction and division. The experience that a student can gain through algebra allows them to analyze skillfully
Analytics has even allowed webmasters to understand what product is fascinating to their customers that to location, time,age-wise. The insights of online adult industry can be garnered by other organizations too. Basically, Sex sells and the better understanding of the sexual fantasies of the visitors of certain region or age group or any other category could be extremely helpful for optimization and promotion.
In reading, How the Confidence Interval Affects Business, I found it to be very interesting. I had no idea the relation of statistics that are being used in the business world. Stat Trek (2017) states, “Confidence intervals indicate (a) the precision estimate and (b) the uncertainty of the estimate” (para. 3). Marketing is an important tool for most business, for a business to have the ability to estimate future sales and determine if sales will decline in the near future seems to be vital knowledge for the success of a company. This knowledge is achieved through data collected from clients, previous sales, and other bases of relative information.
Therefore, I applied Union College Summer Research Fellows to do a research about analyzing the changing tendency of China’s trade policy after 1993 with a Policy Science Professor. Meanwhile, I decided to compress my two-term Seward honor project into six weeks in summer to save me more spaces learning new knowledge in coming terms. After discussing with my advisor, Professor Motahar, my honor project was to analyzing the behaviors of Chinese currency. If Econometrics taught me how to establish and analyze model, Regression Analysis taught me how to take a look at the model itself from statistical aspects. Therefore, I used predictive analytics to analyze students’ academic behaviors at Union College with Professor Dvorak during the same summer.
The revolution of statistics began with the ruler who wanted the monitor it people. Sweden had forged the development of statistics, 50 years before the Austrians, Belgian, Deans, Dutch, France, Germans, and Italian and finally the British. Modern statistic comes from the word ‘state’ three centuries ago, from the Swedish table Verket. In 1749, the Swedish government collect the first ever systematic record of births, marriages and deaths in the table Verket gather from every parish in Sweden. In northern Europe, Sweden was the greatest military power, however 1749, Sweden notice they were the only European country with a decline in their status, as other countries were growing stronger.