Pricing Products by Using Subjctively Determined Demand

Hi,

In this lesson I learnt how to get the demand of a product, when I don't know the elasticity of it, futhermore the demand is not linear or curve, it is quadratic curve, and we stimate the demand for different prices.

To know how it could reckon the figures, you only have to look at the following file.

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Pricing Products by Using Tie-Ins

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In this lesson I learnt how to maximize profit in Tie-ins products, if we know inicial demand and price, also the elasticity of the product, we could reckon the demand curve; thus, we obtain the better price of bearing on products.


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The Economic Order Quantity Inventory Model

Hi,

In this lesson I have learnt the amount of batches orders accordingly with our demand, or how we could set up our production run taking into account the cost of set up, the of holding our goods and the batch production.

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Determining Customer Value

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In this lesson I have learnt how we could improve profitability by valuing customer in the long term, we could reckon by using Net Present Value. Failure to look at the long-term value of a customer often causes a company to make poor decisions.


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Pricing Stock Options

Hi,

In this lesson I learnt how I can reckon a call and put option, how can I estimate the volatility of a stock based on historical data, how can I use Excel to implement the Black-Scholes formula... All this tools enable decisions makers to assess future invesments options, basing on volatility price projections.

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Simulating Stock Prices and Asset Allocation Modeling

Hello,

In this lesson I learnt how to allocate my investment portafolio between different set of assets, taking into account by assuming that future will be similar to the pass, using "bootstrapping" technique, which consist of simulates future investments returns by assuming that the future will be similar to the pass. And also measurign their problably perform and risk.


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Calculating an Optimal Bid

Hi,

In this lesson I learnt that bidding against competitors on a project, the two major sources of uncertainty are the number of competitors and the bids submitted by each competitor. If our bids are low, we'll work on lots of projects but make very little money on each one. The optimal bid is somewhere in the middle. Monte Carlo simulation is a useful tool for determining the bid that maximizes expected profit.


Click here to see how it could calculate and some examples.

Monte Carlo simutation: the best tool to reckon with discrete and normal random variables

Hi,

In this lesson I learnt that Monte Carlo simulation is the best tool to estimate both average return and the risk factor of new product, to determine which products came to the market, or for activities such as forecasting net income for the corporation, predicting structural and purchasing costs, or determining its susceptibility to different kinds of risk (GM is a company which use in this way).

Lilly company uses simulation to determine the optimal plant capacity for each drug.

Proctor and Gamble uses simulation to model and optimally hedge foreign exchange risk.

Sears uses simulation to determine how many units of each product line should be ordered from suppliers.

Oil and drug companies use simulation to value "real options", such as the value of an option to expand, contract, or postpone a project.

Financial planners use Monte Carlo simulation to determine optimal investment strategies for their clients retirement.


You could see one example by clicking here

Making Probability Statements from Forecast

Hi,

In this lesson I learnt, how can I accurate my forecast by correcting past bias. Whether the summa of the past bias excess by one, we must correct by multiplying our past forecast by errors mean, and then if we reckon the standard deviation of the errors of the new forecast corrected;  I will know if the accuracy of our forecast.

In my opinion, this correction is useful if there isn't any chances in the figures under we calculated our past forecast, we could correct; however we have to make changes in the levels of productions, expenditures, revenues... that it could affect the figures forecast, in this case the correction I don't know if it could be ok, because we operate in other scale and casuistic.


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The normal random Variable

Hi,


The normal random Variable is a useful tool that allows economist make predictions between confidence intervals in a distribution of data which we try to predict with a probability of some degree of truth a subset of this data.




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Forecasting in the Presence of Special Events

Hi,

In this lesson I learnt how can I determine whether specific factors influence the amount of a dependent variable, for example customers traffic in a airport or in a fun fair; how can I evaluate forecast accuracy, by determining the point of outliers, and finally how can Icheck whether my forecast errors are randon.





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Ratio-to-Moving-Average Forecast Method

Hello,

In this lesson I've learnt how we can reckon a forecast by coming up with good forecast of quarterly seasonal index. A lot of companies can have seasonal patterns which explains the behavior of the variable we are analyzing, for instance revenues, cost of supplies...; consequently, if we have a method that breaks down in different index which it allows us to understand this pattern; our forecast will be accurate, and we could make our forecast with more precision.


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Winters's Method: Smoothing allows forecasting easy

Hi,

In this leason I've learnt how to manage in other to predict future values of a time series, such as monthly cost or monthly product revenues. This is usually difficult because the characteristics of any time series are constantly changing. Smoothing or adaptive mothods are usually best suited for forecasting furere values of a times series.

The file that you can look at below, you'll find one description the most powerful smoothing method: Winters's method.

To help you understand how Winters's method works, we use several examples where you could see all details of the reckonings needed to get truethly forecast.


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Randomized Block and Two-Way ANOVA

Hi,

In this chapter I learnt to use the Excel tool Two-Way ANOVA in order to analize when two factors might influence a dependent variable, if any of the two factors have a significant influence on the dependent variable. With two-way ANOVA, you can also determine whether two factors exhibit a significance interaction.


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Analysis of Variace: One-Way ANOVA

Hello,

Data analysis often have data about several different groups of people or items and want to determine whether the data about the groups differs significantly.

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Modeling Nonlinearities and Interactions

Hi,

When we analize data with representents groups of data or variables, we find that changes in the INDEPENDANT VARIABLE often influences a DEPENDANT VARIABLE throu a nonliniear relationship. That means when we yields a shift in the dependant variable caused by a unit of change in the independant variable, the ratio of this change in not constant.


You could see more details of this calculation by clicking here.
 

Incorporating Qualitative Factors into Multiple Regression

Hi,

In this lesson I learnt: how can I predict quarterly U.S. auto sales?

Suppose we want to predict quarterly U.S. auto sales to determine whether the quarter of the year impacts auto sales. We'll use the data in the file you could click below, where sales are listed in thousands of cars, and GNP is in billions of dollars.

Click here to see how I made the analysis.

Introducing to Multiple Regression

Hi,

In this lesson I learnt the meaning of the parameters (Coeficients, p-vauleu, R2, Standard error...) that could influence in a multiple regression, how I can trust in the forecast made from the equation build with the coeficients, how much accurate we'll be our predictions...


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Using Correlations to Summarize Relationships

Hello,


In this chapter I learnt to read this statistic tool: correlation, to know the degree of relation between two variables, that means by using this took we are able to know how much is the shift behavior of each other.


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The Power Curve: a curve where takes place production and learning by doing

Hi everybody,

The Power Curve (y = Ax^b) is a shape where it fits the dates from production cost as a function of units produced (frequently b>1), or dates from sales as a function of advertising expenditures (usually -1
You could see some examples by clicking here