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]]>In Data Analytics, when we say data, these involve numbers or texts or symbols that represent some pieces of information. More often than not, we can see the numbers. Because numbers are involved, it is easier to think that it has some values of quantitative or qualitative variables. It must be taken note of that the term “values” is broad enough to cover everything to which value can be ascribed to.
Variables have different types. It is necessary to understand these types so as to know how to make a graphic presentation that really suits its content, nature and treatment of their values. First, there is a quantitative variable. This is also called numerical in the sense that it has a significant meaning as a measurement.
For example are persons height and weight. The best way to contain this kind of variable is thinking of the basic forms of arithmetic such as adding, subtracting, multiplying and dividing. Of course, symbols may be used to denote a value of a certain number in its place and stead. This can further be classified as continuous and discrete.
A continuous variable is a specific kind of a quantitative variable that describes data in a measurable way. If your data deal with measuring a height, weight, or time, then you have a continuous variable. Here there is the interval, and within this interval, any value can be possible. A discrete variable has a finite number of possible values and does not have the inherent order.
Here, every value is not possible. For example, in grading a performance of a product, you may use qualitative values such as 1,2,3 and 4 for the rating but this does not specifically show the real value in its strictest quantitive sense. Statistically speaking, only integer values are possible. Look the example below that talks about a family size.
This is the biggest question: How are you going to present your data? Descriptive statistics now comes in. It is the idea of presenting and describing the features of your data. It can be done through various means: graphical representation, tabular representation and summary statistics. First two are called visualization technique. For better understanding the dichotomy of the presentation, it is better to tackle the overview of descriptive vs. inferential statistics.
Descriptive statistics are used to present quantitative descriptions in a manageable form. This is a way to see something meaningful of data at hand. In short, you make a statement based on, about, and derived from these data. As a limitation, you are not allowed to make conclusions beyond the data at hand. You cannot make inferences or generalizations.
On the other hand, inferential statistics go beyond the figures. By its name, one can make inferences and conclusions. In descriptive statistics, more that one variable may be involved, that says, that the point of interest is the relationship between or among different variables. One should ask: How does one variable change with respect to other variables?
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]]>The post Introduction To Descriptive and Predictive Analytics Part – 1 appeared first on StepUp Analytics.
]]>Often the aim is to address a specific problem through modelling the world in some form and then use the model to develop a better understanding of the world.
A Fundamental Operations Problem: An example
An Operations Problem: Costs
Timeline of Events
Demand is uncertain. Suppose you bought 10 items
Problem Recap
You don’t know what the demand is going to be
You have to decide on the number of units to order from the supplier before seeing the customer demand.
What could help?
The chart shows the demands (y-axis) observed in past 100 periods (x-axis).
Past Demand Data
Before you make your decision
The problem you just saw is called a Newsvendor problem.
Its characteristics are:
This is called the newsvendor problem:
Because it is similar to a vendor who sells newspapers:
In this course, we will show you how to think about and analyze this problem
A Business Application at Time Inc.
Time Magazine Supply chain:
Stores were either selling out inventories (too little inventory) Or sold only a small fraction of allocation (too much inventory). Time Magazine evaluated and adjusted for every issue:
National print order (total number of copies printed and shipped), Wholesale allotment structure (How those copies are allotted to wholesalers). Store distribution (Final distribution to stores).
Note: the above three decisions are made before the actual demand is realized
Need to analyze past data Forecast future demand. Time Magazine reports saving $3.5M annually from tackling the newsvendor problem.
Broader applications of the Newsvendor problem
Governments order flu vaccines before the flu season begins, and before the extent or the nature of the flu strain Is known
What is forecasting?
Primary Function is to predict the Future
Why are we interested?
Dictates the decisions we make today
Examples: who uses forecasting in their jobs?
What makes a good forecast?
Point forecasts are usually wrong! Why?
1. Examples: In December 2015, there will be 37cms of snow.
2. We will sell 314 umbrellas during the rains next Part.
3. Demand could be a random variable.
Therefore, a good forecast should be more than a single number
1. Mean and standard deviation
2. range (high and low) (e.g. weather forecasts).
Modeling Uncertain Future: Probability Distributions
An Example of a Model of Future Demand
Example of a Model of Future Demand: How likely is Each Scenario?
Three Scenarios and Probability Distribution
In other words, we project that the demand is not equal to a certain number with probability 1, but, rather can take one of three values with those probabilities
We have just created a probability distribution for the future demand:
Probability distributions like that one, described by a number of distinct scenarios with attached probabilities, are called discrete Note that the probabilities are:
In other words, we project that the demand is not equal to a certain number with probability 1, but, rather can take one of three values with those probabilities Three Scenarios Probability Distribution: Scenarios and Their Probabilities
Describing Probability Distribution: Mean and Standard Deviation
Three Scenarios Probability Distribution: Mean
Describing Probability Distribution: Mean and Standard Deviation
Three Scenarios Probability Distribution: Mean and Standard Deviation
Knowledge of mean and standard deviation values helps to support a general intuition about the nature of a random variable
Mean and Standard Deviation: More than three scenarios
– D1 with probability 1
– D2with probability 2
– D3with probability 3
————————–
– Dn with probability
And 1+2+3+⋯+=1
The random variable being modelled has a really large number of scenarios on any small interval of the possible interval of values and
The probability that any one scenario is realized is really small
There exist statistical formulas (also implemented in Excel) that calculate a probability that a normal random variable X with given mean and standard deviation s produces a value within a specified interval of values
[Xmin, Xmax]
Other Continuous Probability Distributions
Returning back: Characteristics of Forecasts
1. Point forecasts are usually wrong! Why?
2. Therefore, a good forecast should be more than a single number
3. Forecasts should include some distribution information
4. Aggregate forecasts are usually more accurate
5. The accuracy of forecasts erodes as we go further into the future
6. Don’t exclude known information
Subjective Forecasting Methods
How to forecast with past data, objectively?
We can leverage past data to come up with forecasts:
Two primary methods:
Causal Models
– Let D be the demand or future outcome to be predicted and assume that there
– Are n variables (or root causes) that influence the demand.
– A causal model is one in which demand D is formulated as a theoretical function of all those n causes.
– Causal models are generally intricate and complex and need advanced tools in addition to domain expertise.
– In this course, we will focus mainly on time series based models.
Time Series Methods
Next…
Forecasting with past historical data
Moving Averages
Exponential smoothing
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]]>It will launch two tabs ui.R and server.R. To launch the application click on Run App.
There are several other examples available, for reference and details refer to below shiny website
http://shiny.rstudio.com/tutorial/
Download Cheat Sheet
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