Sunday 18 December 2016
Tuesday 8 November 2016
Wednesday 2 November 2016
Friday 28 October 2016
Thursday 13 October 2016
Monday 26 September 2016
Sunday 25 September 2016
Thursday 8 September 2016
Thursday 1 September 2016
Wednesday 31 August 2016
Friday 29 July 2016
Monday 14 March 2016
Bollinger Bands for Day Trading
Bollinger Bands for Day Trading
Bollinger Bands are a pair of trading bands representing an
upper and lower trading range for a particular market price. It is said that a
particular security would trade within this trading range under normal
circumstances. The Bollinger bands consist of moving averages on either side
and are used for decisive entry and exit signals by the traders. The lines are
plotted using standard deviation on either side of the moving averages. The
volatile nature of the security changes the standard deviation values and
thereby changes the width of these bands on either side.
Bollinger Bands can be used as a decisive trading system by
investors and traders for all security classes and types. Bollinger Bands can
also be used on any time frame. Bollinger bands along with the use of other
indicators can be used to make decisive decision. Like when the price is
nearing the upper end of the trading band with the help of an indicator like
the RSI, traders can go short and when the stock is near the lower end of the
trading band traders can use it as a signal to go long.
Key Features of Bollinger Bands
- · A move originating at the upper band tends to go all the way to the lower band and vice versa. This is generally the case for most of the securities and therefore is used extensively to enter or exit a particular trade
- · Quick moves tend to happen when the Bollinger bands contract and there is less volatility in price. It is said that when prices are the least volatile, the propensity of a breakout is the highest
- · At breakout, the current trend is generally sustained
Components of the Bollinger Band
- · Moving Average: Generally taken as 14 to 20 day moving average
- · Upper Band: Generally calculated as 2 standard deviation above the closing prices of the moving average
- · Lower Band: 2 standard deviations below the moving average
Buy & Sell Signals
Whenever the stock has hit the lower end of the Bollinger
band it is a buy signal and prices generally move back towards the higher end
of the trading band once they have crossed over the simple moving average in
the middle. A sell signal is generated with the opposite
Rules for Bollinger Band Trading
·
Bollinger Bands are just a relative definition
of a high or a low
·
These relative definitions with the use of
indicators can be used to enter decisive buy and sell decisions
·
Appropriate indicators can be derived and should
be used along with Bollinger Bands. For example: MACD, RSI, OBV (volume
indicator) should all be used in conjunction with Bollinger bands
·
Volatility has already been used to calculate
the width of the Bollinger Bands and therefore should not be used as a
different indicator for buy and sell decisions
·
Use different indicators from different sets.
Don’t use two momentum indicators, use one from Momentum and the other from
volume if need be
·
Bollinger Bands are used to confirm pure price
patterns like different types of Tops and Bottoms
·
Price generally moves within the Bollinger bands
so can be up or down depending on the overall trend for long periods of time
·
Closes outside the Bollinger bands can just be a
sign of continuation and not a breakout or reversal, so traders really need to
use other indicators for confirming the trade entries
·
Bollinger Bands generally have a set default
pattern with regards to their makeup and the standard deviation is used to as
per the volatility in a particular stock
·
Non Descriptive moving averages should not be
used for the creation of Bollinger bands
Monday 1 February 2016
Tuesday 12 January 2016
Decision Trees
Decision Trees
Decision trees are, in general, a non-parametric inductive learning technique, able to produce classifiers for a given problem which can assess new, unseen situations and/or reveal the mechanisms driving a problem. They can be applied to both regression and classification problems.
Decision trees can be easy-to-understand with intuitively clear rules understandable to domain experts
Decision trees offer the ability to track and evaluate every step in the decision-making process. This is because each path through a tree consists of a combination of attributes which work together to distinguish between classes. This simplicity gives useful insights into the inner workings of the method.
Decision trees can handle both nominal and numeric input attributes and are capable of handling data sets that contain misclassified values
Decision trees can easily be programmed for use in real time systems.
They are relatively inexpensive computationally and work well on both large and small data sets
Decision trees are considered to be a non-parametric method. This means that decision trees have no assumptions about the space distribution and on the classifier structure
Decision trees are, in general, a non-parametric inductive learning technique, able to produce classifiers for a given problem which can assess new, unseen situations and/or reveal the mechanisms driving a problem. They can be applied to both regression and classification problems.
Decision trees can be easy-to-understand with intuitively clear rules understandable to domain experts
Decision trees offer the ability to track and evaluate every step in the decision-making process. This is because each path through a tree consists of a combination of attributes which work together to distinguish between classes. This simplicity gives useful insights into the inner workings of the method.
Decision trees can handle both nominal and numeric input attributes and are capable of handling data sets that contain misclassified values
Decision trees can easily be programmed for use in real time systems.
They are relatively inexpensive computationally and work well on both large and small data sets
Decision trees are considered to be a non-parametric method. This means that decision trees have no assumptions about the space distribution and on the classifier structure
Sunday 10 January 2016
Validate Data in EXCEL using Validation List
Validate Data in EXCEL using Validation List
Excel enables you to restrict the values a
user can enter in a cell. By restricting values, you ensure that your worksheet
entries are valid and that calculations based on them thereby are valid as
well. During data entry, a validation list forces anyone using your worksheet
to select a value from a drop-down menu rather than typing it and potentially
typing the wrong information. In this way, validation lists save time and
reduce errors.
To create a validation list, type the
values you want to include into adjacent cells in a column or row. You may want
to name the range. After you type your values, use the Data Validation dialog
box to assign values to your validation list. Then copy and paste your
validation list into the appropriate cells by using the Paste Special Validation
option. You may want to place your validation list in an out of the way place
on your worksheet or on a separate worksheet
Validation lists can consist of numbers, names
of regions, employees, products, and so on.
Vamsidhar
www.pacegurus.com
Saturday 9 January 2016
Teaching Statistics
Teaching Statistics
Being
able to provide solid evidence-based arguments and critically evaluate claims
based on data are important skills that all citizens should have. Hence the
study of statistics worldwide at all educational levels is gaining more
attention. The study of statistics provides students with tools, ideas and
dispositions to react intelligently to information in the world around them.
Reflecting this need to improve students’ ability to think statistically,
statistical literacy and reasoning are becoming part of the mainstream school
and university curricula in many countries. As a consequence, statistics education
is becoming a thriving field of research and curricular development.
Statistics is vigorously gaining importance and recognition in
today’s society thanks to the boom created by the buzz words Big Data and
Analytics. Statistics is a central tool in moving science, economics, politics,
schools, and universities forward. Quantitative information is omnipresent in
media and in the everyday lives of citizens worldwide. Data are increasingly
used to add credibility to advertisements, arguments, or personal and
professional advice. Therefore, there is a growing public and policy consensus
that being able to provide reliable and persuasive evidence-based arguments and
critically evaluate data-based inferences are crucial skills that all citizens
of the twenty-first century should have. All students consequently must become
statistically literate and be able to reason statistically even at an informal
level as part of their compulsory and lifelong education
Despite
the increasing awareness of the importance of statistical literacy, statistics has
been viewed by many students as difficult and unpleasant to learn. Many
university instructors find statistics and research methods courses equally
frustrating and unrewarding to teach. In schools, mathematics teachers often
view statistics as a marginal strand in the mathematics curriculum and therefore
minimize or ignore its teaching. Students equate statistics with mathematics
and expect the focus to be on numbers, computations, formulas, and one right
answer. They are uncomfortable with the messiness of data, the different
possible interpretations based on different assumptions, and the extensive use
of writing and communication skills. The dissatisfaction with students’ ability
to think and reason statistically, even after formally studying statistics at
the undergraduate and graduate level should lead to a re-examination of the field
of statistics education
Many
students still leave their course perceiving statistics as a set of tools and
techniques that are soon forgotten. Even current methods of teaching continue
to focus on the development of skills and have neglected to instill in their
courses experiences that develop the ability to think statistically
There
is a need for data driven, innovative approaches in teaching statistics keeping
in mind the objective of statistics in the real world. This subject should be comfortable
even to a students who fears mathematics because this subject is all about
working with relationships and the use of technology simplifies the process
further. All the statistics and business analytics courses designed by www.pacegurus.com keep in mind a student
with very little knowledge of statistics, but has the attitude to learn,
decipher from the data and make meaningful conclusions needed for the aspects
of life.
Bottom
Line: Statistics is much much bigger than just a few calculations. It helps you
in every walk of life. Don’t compare it with Maths and develop fear and allergy
towards it. Start enjoying it……
Vamsidhar
www.pacegurus.com
Friday 8 January 2016
Intermediate Statistics using SPSS - Workshop
Intermediate Statistics using SPSS - Workshop
www.pacegurus.com announces a 2-Day workshop on "Understanding Credit Derivatives" for the Corporate, Business Schools, any other learning institutions. Even individuals can get enrolled for one-one learning of the subject. The program would be conducted by VAMSIDHAR AMBATIPUDI at your campus. The detailed syllabus for the same can be found at
http://www.pacegurus.com/Intermediate-Statistics-SPSS.html
For further details do contact us at +91-9848012123. Request you to share the information with your friends and colleagues and do recommend PACEgurus for Finance, Investment, Actuaries, Risk Management, Statistics and Business Analytics trainings for your organization.
Integrating Strategy and Analytics
Integrating Strategy and Analytics
“The vast majority of strategic initiatives never succeed.”
“Organizations fail not because they have the wrong strategy but because they do not execute the strategy properly.”
Statements like these have been repeated so often they have become conventional wisdom. An entire industry of management consultants exists to help companies define the right strategy so that they won’t fail. Yet failure, or at least muddling through, is still much, much more common than outright success. Through analysis you can determine where execution works well and where it does not. With that information you can evaluate the solutions, determine which one(s) to implement
Organizations are complex places. Cognitively, people have a hard time focusing on too many things at once. A primary vehicle for reduced complexity is standard business processes. Unfortunately, simplification and standard processes are not enough. You have to recognize and manage the organizational complexity for successful strategy execution. The challenge for frontline and middle managers is how to deal with the competing strategic and operational objectives
Most analytics conducted today by the business and by HR are incomplete and cannot solve strategy execution problems on their own. You need a full causal model to diagnose the entire system and to understand what really drives behavior and performance. You need to know what drives performance in your organization to get strategy execution right. The problem with organizational analytics today is that they tell an incomplete story. Enterprise analytics and human capital analytics are conducted along parallel and separate tracks. Both attempt to determine why performance happens, yet each on its own can tell only part of the story. Without the complete story, we don’t really know the best ways to improve strategy execution and organizational effectiveness.
When you understand the link to the larger system, you can properly diagnose the root causes of behavior and motivation. It’s also the surest way to find changes to solve the problems instead of temporary solutions that only paper over the root causes
VAMSIDHAR AMBATIPUDI
www.pacegurus.com
www.pacegurus.com
Thursday 7 January 2016
Understanding Credit Derivatives - Workshop
Understanding Credit Derivatives - Workshop
www.pacegurus.com announces a 2-Day workshop on "Understanding Credit Derivatives" for the Corporate, Business Schools, any other learning institutions. Even individuals can get enrolled for one-one learning of the subject. The program would be conducted by VAMSIDHAR AMBATIPUDI at your campus. The detailed syllabus for the same can be found at
http://www.pacegurus.com/Understanding-Credit-Derivatives.html
For further details do contact us at +91-9848012123. Request you to share the information with your friends and colleagues and do recommend PACEgurus for Finance, Investment, Actuaries, Risk Management, Statistics and Business Analytics trainings for your organization.
www.pacegurus.com announces a 2-Day workshop on "Understanding Credit Derivatives" for the Corporate, Business Schools, any other learning institutions. Even individuals can get enrolled for one-one learning of the subject. The program would be conducted by VAMSIDHAR AMBATIPUDI at your campus. The detailed syllabus for the same can be found at
http://www.pacegurus.com/Understanding-Credit-Derivatives.html
For further details do contact us at +91-9848012123. Request you to share the information with your friends and colleagues and do recommend PACEgurus for Finance, Investment, Actuaries, Risk Management, Statistics and Business Analytics trainings for your organization.
Flexible Worksheet Consolidation
Though Excel provides built-in functionality to consolidate worksheets, it is a bit overly complex for most situations. This solution consolidate worksheets, without using Excel’s consolidation function, and also gives users more flexibility to change what is being consolidated. By using this approach to consolidate worksheets, we can instantly modify the composition of our consolidation by either adding or removing worksheets from the consolidated total
The one limitation to this approach is that each worksheet needs to be identically structured relative to the items that are going to be consolidated. Generally this requirement is not particularly onerous given the nature of worksheet consolidation. In addition to the line of business-level worksheets, we will need one worksheet that sums all the lines of business as a consolidated financial statement
Application
To illustrate, assume that we have a business consisting of three lines of business (LOB) titled LOB1, LOB2 and LOB3. Each LOB's P&L and balance sheet is additive to the consolidated financial statement. In our example, the leadership team is considering adding a fourth LOB and wants to be able to easily consolidate or exclude the fourth LOB, to better analyze LOB4’s impact on the consolidated financial statements. This will require five worksheets, one for each of the four LOBs and one for the consolidated business. We start by creating a template for our financial statement with the years going across the columns. Each of the LOBs and consolidation worksheets need to be structured the same, for all cells to be consolidated.
You could have consolidated as follows
2015 Consolidated Revenue = LOB1!F13 + LOB2!F13 + LOB3!F13 + LOB4!F13
The issue that arises is when you want to remove one of the LOBs from the consolidation, which means that every formula referencing LOB4 needs to be edited.
In order to make use of the flexible consolidation approach you will need to add two extra worksheets that establish the range. We usually name the two worksheets “Start” and “End”. We insert the individual LOB worksheets between them. So there are 6 sheets now (one for each LOB and one start and one end). The consolidation worksheet will be outside the start and end sheets.
Now the new formula in consolidation worksheet is Formula: =SUM(Start:End!F13)
Now, if we are asked to display the consolidation without LOB4, we simply drag the LOB4 worksheet beyond the “Start” and “End” bookend range. This approach will allow us to create some very sophisticated reporting and analysis spread sheets, while permitting addition and deletion of worksheets within our consolidation, with much less formula editing
The one caveat to this approach is that the bookend worksheets (Start and End) are part of the SUM function, which means those worksheets must be blank; otherwise the SUM function will include any data on those two worksheets
Vamsidhar Ambatipudi
www.pacegurus.com
The one limitation to this approach is that each worksheet needs to be identically structured relative to the items that are going to be consolidated. Generally this requirement is not particularly onerous given the nature of worksheet consolidation. In addition to the line of business-level worksheets, we will need one worksheet that sums all the lines of business as a consolidated financial statement
Application
To illustrate, assume that we have a business consisting of three lines of business (LOB) titled LOB1, LOB2 and LOB3. Each LOB's P&L and balance sheet is additive to the consolidated financial statement. In our example, the leadership team is considering adding a fourth LOB and wants to be able to easily consolidate or exclude the fourth LOB, to better analyze LOB4’s impact on the consolidated financial statements. This will require five worksheets, one for each of the four LOBs and one for the consolidated business. We start by creating a template for our financial statement with the years going across the columns. Each of the LOBs and consolidation worksheets need to be structured the same, for all cells to be consolidated.
You could have consolidated as follows
2015 Consolidated Revenue = LOB1!F13 + LOB2!F13 + LOB3!F13 + LOB4!F13
The issue that arises is when you want to remove one of the LOBs from the consolidation, which means that every formula referencing LOB4 needs to be edited.
In order to make use of the flexible consolidation approach you will need to add two extra worksheets that establish the range. We usually name the two worksheets “Start” and “End”. We insert the individual LOB worksheets between them. So there are 6 sheets now (one for each LOB and one start and one end). The consolidation worksheet will be outside the start and end sheets.
Now the new formula in consolidation worksheet is Formula: =SUM(Start:End!F13)
Now, if we are asked to display the consolidation without LOB4, we simply drag the LOB4 worksheet beyond the “Start” and “End” bookend range. This approach will allow us to create some very sophisticated reporting and analysis spread sheets, while permitting addition and deletion of worksheets within our consolidation, with much less formula editing
The one caveat to this approach is that the bookend worksheets (Start and End) are part of the SUM function, which means those worksheets must be blank; otherwise the SUM function will include any data on those two worksheets
Vamsidhar Ambatipudi
www.pacegurus.com
Wednesday 6 January 2016
Manipulating Text Strings in EXCEL
Manipulating Text Strings in EXCEL
An issue that we frequently come across with our
clients is users that are a little overwhelmed when they need to rearrange text
strings differently than from the way that the text string was imported from a
source system. As long as the text string is delimited, such as with a coma,
asterisk, blank space, or colon we can use Excel’s different text functions to
get the text string parsed as required. We can break the full name into first
name, middle name and last name or any other forms of manipulations quite
comfortably and quite DYNAMICALLY if we know different formulas which can manipulate
text data
There are five primary text functions that users need
to learn, which are LEFT(), RIGHT(), FIND(), LEN() and MID().
LEFT() – This has only two possible parameters: LEFT(source, # characters). The source is
the text cell to be parsed, and the # characters are the number of characters
we want returned beginning from the left most character
RIGHT() – This works exactly the same as the LEFT(),
just beginning from the opposite side of the text string
FIND() – Like the previous two functions, this
function only requires two parameters: FIND(character(s) to be found, source string). The first parameter
needs to be enclosed with double quotes and represents the string to be found.
The second parameter is the cell address of the text string to be searched. The
FIND() function will return the position number, within our string, of the
first occurrence of the character(s) being sought
LEN() – The LEN() function returns the length of the
referenced text string
MID() – The MID function has three required parameters:
MID(source, starting position, number
of characters). The MID() function will extract a number of characters
from within a text string
Role of Analytics in innovation
Role of Analytics in innovation
Analytics
plays a crucial role in modern corporate innovation. The outcomes from
analytical models are used to drive new sales processes, to change customer
experiences in order to avoid churn, and to identify triggers detecting fraud,
risk, or any sort of corporate threat, as well as many other business issues.
The knowledge from analytical models is commonly assigned to recognize customer
Behavior, to predict an event, or to assess the relationship between events,
impacting company actions and activities.
Analytics
has three stages.
Stage
1 provides long-term informational insight, helping organizations analyse trends
and forecast business scenarios. Data warehouse, data marts, and interactive
visual analysis usually support this stage one purpose. Focus is towards identifying
trends, historical event patterns, and business scenarios. This analysis is
concerned with presenting information about past sales by region, branches, and
products and, of course, changes that have occurred over time.
Stage
2 of Analytics maps out the internal and external environments that impact the market
considerations, the customers’ Behavior and the competitor’s actions, as well
as details about the products and services that the organization offers. How
profitable are my products/services? How well have they been adopted by the
target audience? How well do they suit the customer’s need? Statistical
analyses support these tasks, with correlations, and association statistics
methods.
Stage
3 focus is driven by to the company’s strategy. Model development is directed
by core business issues such as cross sell, churn, fraud, and risk, and models
are also deployed and used once the results are derived. Data mining models
that use artificial intelligence or statistics commonly support these types of endeavours.
Models are deployed to classify and predict some particular event and to
recognize groups of similar Behavior within the customer base for subscribed business
change
The
three layers of analytics provide a foundation for data-driven innovation, both
creating and delivering new knowledge and accessible information. In innovative
organizations, access to analytically based answers is fundamental throughout
the company. Data is seen as a corporate asset and analytical methods become intellectual
property.
Innovation
is a wonderful process. It continually evolves, allowing companies to remain
ahead of competitors, ahead in the market, and ahead of its time. However,
innovation has a price - intangible price—and maybe even a higher price than we
could imagine. Innovation demands companies stay at the pinnacle of available technology
and be on the leading edge of new business actions. But even more than this,
innovation requires people to change their minds.
Innovation
demands change. We must take a chance and address a particular situation and
put into place something that may never have been tried before. Innovation in
the context of new ideas means to try, and sometimes get it right and make
things better, and sometimes not. Therefore, innovation is a trial-and-error
process, and as such it is also a heuristic process.
Everything
changes. The market changes, the consumer changes, the technology changes, and
thus products and services must change as well. Analytical models raise the
business knowledge regarding what has changed and what needs to change. The new
knowledge delivered by analytics is about the company itself, the competitive environment,
and the market, but mostly it is about the consumers/ constituents that the
company serves
Analytics
is geared toward understanding the average, to accurately forecast for the
majority, to target most of the population at hand. What companies, analysts,
and data miners need to bear in mind is how heuristic this process can be and,
as a result, how they need to monitor and maintain all analytical models to
reflect changing conditions.
Tuesday 5 January 2016
IBM SPSS for Analytics
IBM SPSS for Analytics
IBM SPSS is a wonderful statistical analysis package. We can perform complex statistical analysis on research data in a few minutes which would have been impossible earlier without expert help and an enormous amount of time. SPSS allows us to undertake a wide range of statistical analyses relatively easily. However, we do need to know what analysis is appropriate to the data we have. So a certain amount of basic statistical knowledge is required before using SPSS.
In my experience as a teacher, statistics consultant and adviser to students, I saw many people experience some confusion when they first encounter the computer output from statistical applications. They often ask questions such as: Why so many tables? What do they mean? Which is my result? Is it significant? This is because the statistical applications print out a range of useful information with each analysis. Not all of this information is readily understandable to a new user. I have seen students on a daily basis who simply want a clear explanation of the SPSS output they have produced at a level they can understand.
There are two types of explanations: ones for the novice and ones for the expert. Often we hear a technical explanation and cannot understand it. We may even be tempted to ask: Can you say that again in English? This is one of the difficulties of learning statistics and understanding the output from statistical applications. The terminology may not be readily understandable. In many subject areas there are technical definitions that are not used in everyday communication. The same is true of statistics, with terms such as ‘general linear model’, ‘homogeneity of variance’ or ‘uni-variate analysis’. But the business meaning behind each of these terms could be very simple and understandable.
www.pacegurus.com provides training to corporate, B-Schools and research scholars on the key statistical tests, describe how to undertake them and explain the output produced by SPSS for these tests. The program will be rendered in a way that even a person with little or no statistical background can use SPSS for his/her research in a very effective manner. You can recommend me (VAMSIDHAR AMBATIPUDI) for your organization/College/Institution so that you can be trained in a simple and useful manner . You can call us on +91-9848012123 for further communication
IBM SPSS is a wonderful statistical analysis package. We can perform complex statistical analysis on research data in a few minutes which would have been impossible earlier without expert help and an enormous amount of time. SPSS allows us to undertake a wide range of statistical analyses relatively easily. However, we do need to know what analysis is appropriate to the data we have. So a certain amount of basic statistical knowledge is required before using SPSS.
In my experience as a teacher, statistics consultant and adviser to students, I saw many people experience some confusion when they first encounter the computer output from statistical applications. They often ask questions such as: Why so many tables? What do they mean? Which is my result? Is it significant? This is because the statistical applications print out a range of useful information with each analysis. Not all of this information is readily understandable to a new user. I have seen students on a daily basis who simply want a clear explanation of the SPSS output they have produced at a level they can understand.
There are two types of explanations: ones for the novice and ones for the expert. Often we hear a technical explanation and cannot understand it. We may even be tempted to ask: Can you say that again in English? This is one of the difficulties of learning statistics and understanding the output from statistical applications. The terminology may not be readily understandable. In many subject areas there are technical definitions that are not used in everyday communication. The same is true of statistics, with terms such as ‘general linear model’, ‘homogeneity of variance’ or ‘uni-variate analysis’. But the business meaning behind each of these terms could be very simple and understandable.
www.pacegurus.com provides training to corporate, B-Schools and research scholars on the key statistical tests, describe how to undertake them and explain the output produced by SPSS for these tests. The program will be rendered in a way that even a person with little or no statistical background can use SPSS for his/her research in a very effective manner. You can recommend me (VAMSIDHAR AMBATIPUDI) for your organization/College/Institution so that you can be trained in a simple and useful manner . You can call us on +91-9848012123 for further communication
Monday 4 January 2016
Analytics vs. Analysis
Analytics vs. Analysis
Analysis and analytics are
similar-sounding terms, but they are not the same thing. They do have some
differences
Analysis is to understand the
status quo that may reflect the result of their efforts to achieve certain objectives
whereas Analytics to identify specific trends or patterns in the data under analysis
so that they can predict or forecast the future outcomes or behaviours based on
the past trends. Analysis
can be defined as the process of dissecting past gathered data into pieces so
that the current (prevailing) situation can be Understood. Analytics can be
defined as a method to use the results of analysis to better predict customer or
stakeholder behaviours
Even the dictionary meanings stress that Analysis is
the separation of a whole into its component parts to learn about those parts
whereas Analytics is the method of logical analysis
Analysis presents a historical view of the project
performance as of the time of analysis. Analytics look forward to project the
future or predict an outcome based on the past performance as of the time of analysis.
BI Tools and SQL etc. are used heavily in analysis whereas Statistical, mathematical,
computer science, sophisticated predictive analytics software tools are the
base for Analytics.
Sunday 3 January 2016
Forecasting - Role in Decision Making
Forecasting vs. Decision Making
Virtually every organization, public or private, operates in an uncertain and dynamic environment with imperfect knowledge of the future. Forecasting is an integral part of the planning and control system, and organizations need a forecasting procedure that allows them to predict the future effectively and in a timely fashion. Forecasting can be used as a tool to guide business decisions, even though some level of uncertainty may still exist. It can reduce the range of uncertainty surrounding a business decision
Applications of Forecasting in Business
Forecasting is a powerful tool that can be used in every functional area of business.
1. Production managers use forecasting to guide their production strategy and inventory control
2. Trends and availability of material, labor, and plant capacity play a critical role in the production process
3. Reliable forecasts about the market size and characteristics are used in making choices on marketing strategy and advertising plans and expenditures
4. Marketers use both qualitative and quantitative approaches in making their forecasts
5. Service sector industries such as financial institutions, airlines, hotels, hospitals, sport and other entertainment organizations all can benefit from good forecasts
6. Financial forecasting allows the financial manager to anticipate events before they occur, particularly the need for raising funds externally
7. Use of forecasts in human resource departments is also critical when making decisions regarding the total number of employees a firm needs
Forecasting as a tool in planning has received a great deal of attention in recent decades. Today, firms have a wide range of forecasting methodologies at their disposal ranging from intuitive forecasting to highly sophisticated quantitative methods.
Virtually every organization, public or private, operates in an uncertain and dynamic environment with imperfect knowledge of the future. Forecasting is an integral part of the planning and control system, and organizations need a forecasting procedure that allows them to predict the future effectively and in a timely fashion. Forecasting can be used as a tool to guide business decisions, even though some level of uncertainty may still exist. It can reduce the range of uncertainty surrounding a business decision
Applications of Forecasting in Business
Forecasting is a powerful tool that can be used in every functional area of business.
1. Production managers use forecasting to guide their production strategy and inventory control
2. Trends and availability of material, labor, and plant capacity play a critical role in the production process
3. Reliable forecasts about the market size and characteristics are used in making choices on marketing strategy and advertising plans and expenditures
4. Marketers use both qualitative and quantitative approaches in making their forecasts
5. Service sector industries such as financial institutions, airlines, hotels, hospitals, sport and other entertainment organizations all can benefit from good forecasts
6. Financial forecasting allows the financial manager to anticipate events before they occur, particularly the need for raising funds externally
7. Use of forecasts in human resource departments is also critical when making decisions regarding the total number of employees a firm needs
Forecasting as a tool in planning has received a great deal of attention in recent decades. Today, firms have a wide range of forecasting methodologies at their disposal ranging from intuitive forecasting to highly sophisticated quantitative methods.
Friday 1 January 2016
Sports Analytics
Management of structured historical data, application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers (personnel executives, coaches, trainers, etc. and enable them to help their organizations in gaining a competitive advantage on the field of play. Understanding the tools of sports analytics is important to create a competitive advantage.
Goals
1. Save the decision maker time by making all of the relevant information for evaluating players or teams or prospects efficiently available
2. Provide decision makers with novel insight. Analytic models allow decision makers to gain insight into teams and players that are not possible without advanced statistical analysis. Analytic models have many uses, but their core function is to turn raw data into reliable and actionable information.
Many teams across sports use analytic models to aid in their selection of players. Differences in player performances are the result of a variety of factors, such as team mates, system, opponents, and the player’s ability to perform
Now that leagues exist for almost all sports, it is essential to understand sports analytics and use it for winning.
Management of structured historical data, application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers (personnel executives, coaches, trainers, etc. and enable them to help their organizations in gaining a competitive advantage on the field of play. Understanding the tools of sports analytics is important to create a competitive advantage.
Goals
1. Save the decision maker time by making all of the relevant information for evaluating players or teams or prospects efficiently available
2. Provide decision makers with novel insight. Analytic models allow decision makers to gain insight into teams and players that are not possible without advanced statistical analysis. Analytic models have many uses, but their core function is to turn raw data into reliable and actionable information.
Many teams across sports use analytic models to aid in their selection of players. Differences in player performances are the result of a variety of factors, such as team mates, system, opponents, and the player’s ability to perform
Now that leagues exist for almost all sports, it is essential to understand sports analytics and use it for winning.
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