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# Final exam, study guide MGS 3100

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This 9 page Study Guide was uploaded by Tricia Williams on Friday July 22, 2016. The Study Guide belongs to MGS 3100 at Georgia State University taught by Mark Sweatt in Summer 2016. Since its upload, it has received 47 views. For similar materials see Buisness Analysis in Managerial Science at Georgia State University.

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Date Created: 07/22/16

MGS 3100 (Finals study guide) Chapter 1 &3-(Overview& Basic Profit models) Model: - A representation of something, it can be real or imaginary. Modeling applies to both existing businesses and new businesses. Three types of model 1. Physical(tangible) 2. Analog(Tangible) 3. Symbolic(Abstract) Examples of the different types of model Physical: clay, buildings, cars etc. Analog: thermometer, speedometer etc. Symbolic: language, spreadsheets, simulations etc. Profit model: - Details an estimate of the revenues and cost of running a business. Revenue: - Price * Quantity Sunk cost: - Pass cost Profit: - Total Revenue-Total Cost Variable Cost: - Changes with the level of output Fixed Cost: - Does not change with the level of output Total Cost: - Fixed cost + Variable cost Breakeven point: - Total revenue=Total cost/ Profit=0 Contribution margin: - Price/unit – Variable cost/unit For each additional unit that is sold, you add that to your bottom line. Cross over point: - The point at which profit is the same for either option. (This is calculated by setting both options equal to each other). It can be positive, negative, or zero. It also helps one to decide which option to adopt. Influence diagram: - Is a representation of all revenues and all costs. Shows the structure of relationships among variables, but not the numeric computations. Two Types of reasoning Inductive reasoning: - Goes from specific to general. Eg. Susan has a cat that purr, and it is white, therefore, all white cat must purr. Deductive reasoning: - Goes from general to specific. Eg. All birds can fly and a dove is a bird, therefore a dove can fly. Deterministic model: - Models where the inputs are assume to be known with certainty. Probabilistic (stochastic): - A model where inputs can take on several values with varying probabilities. Relevant and Irrelevant Information Relevant: - Anything that is directly affected by the decision making process. Eg. Training cost, utility costs, and raw materials cost etc. Irrelevant: - Anything that is not directly affected by the decision making process. Eg. Consultation fees, institutional advertisement, and R&D etc. Eg. Of demand function (180-2*P) Chapter 2-(Excel) Review the basic excel formulas If Statement:- (Is use to return one value if true, and another value if false) VLOOKUP statement : - (The V stands for vertical, and is use to find things that is vertically arranged in a table) Median Averages Rand function Chapter 4-(Simulations) Simulation definition: - The initiative representation of the functioning of one system or process by means of the functioning of another. Simulation: - It is an effective way to handle model complexities. It is a mathematical model. It is probabilistic. It uses the entire range of possible values of variable in the model. Simulation requires one to know The variable to be simulated (Eg. Outcome of coin toss) The distribution of the values the variable can have (Eg. 2) (heads/tails) Benefits of simulation It saves time, money and increases safety. eg. Of discrete variable (30, 31, 32, 33 tables) Eg. Of continuous variable (5.5, 6.3, 7.8 feet) The random function in excel generates random numbers that are greater than or equal to zero and less than one. The distribution of these numbers are uniform/even. Advantages of simulation 1. One can study the variation of inputs to the model instead of just the averages. 2. One can study the effects of making changes to a model without making them in real life. 3. It is safer and less expensive than using a real system. 4. It compresses time. Chapter 5 (Forecasting) Forecasting -Making predictions of the future that is based on past and present information and trends. Two major groups of forecasting 1. Qualitative:- Uses expert judgement rather than numerical analysis 2. Quantitative:- Uses numerical analysis and are express mathematically Casual forecasting: - An estimation that is based on other variables that is considered the causes of the particular thing of interest. Time Series forecasting: - When something is forecasted base on its own past values without the use of other variables. Steps involve in forecasting: 1. We look 2. We forecast 3. We evaluate Averaging Techniques 1. Naïve forecast: Assumes consistency in the sense where actual sales from one period is forecasted for the next period 2. Moving Averages:- is forecasted by taking the average of immediate preceding sales for a certain number of periods. It can be two, three or four periods etc. 3. Simple exponential smoothing:- Looks at two things when forecasting, the actual sales and the forecast. The actual sales from the previous period are multiplied by the alpha, which is given, and one minus alpha multiplies the previous period forecast. The two amounts are then added together to give the forecast for the next period. Additionally, the second period forecast uses the naïve method of forecasting. Different types of evaluation 1. BIAS:- Tells how far off from actual sales the forecast is in either a negative or positive direction on average. If the bias is positive, it means that the forecast is underestimated, and if the bias is negative, it means that the forecast is overestimated. Additionally, a negative bias means that you stored more inventory that what actually sold and a positive bias means that you did not meet your customers demand. 2. Mean absolute deviation (MAD):- The average of absolute value (positive numbers) of the errors (the difference between actual sales and forecasted sales). 3. Mean Absolute Percent Error (MAPE):- take the average of the errors as a percentage of actual sales. Eg. Use each absolute error and find the percentage of their corresponding sales and then average of all the percentages. 4. Mean squared error (MSE):- take the average of the absolute errors after they have been squared. 5. Standard Error (SE): Taking the square root of the average MSE. Interpreting the evaluations 1. BIAS (0.50 as reference) The positive value of 0.50 means the forecast is underestimating sales on average by 0.50. If the figure were negative, sales would have been overestimated instead. 2. MAD (0.75 as reference) The mad of 0.75, means that on average, the forecast is away from the actual sales in either a positive/negative direction by 0.75. 3. MAPE(25% as reference) The MAPE value of 25% means that the error is 25% of the actual sales forecasted on average. Fitting a Trendline Regression: Tells if Y and X are related and if so, how closely related are they. Simple Regression: - Uses one variable to forecast the dependent variable Y (Y=intercept + variable (coefficient)) Multiple Regression: Uses more than one variable or multiple independent variable to forecast the dependent variable Y. Y=Intercept + variable (coefficient) + variable (coefficient) + variable (coefficient)……………. This model is use when there is a trend in the data collected about the item to be forecast. The above technique is evaluated by using the same evaluation criteria as averaging techniques plus R-squared. Regression equation= (Y=intercept + variable (coefficient)) Mean shoe size = average of the shoe size Deviation from the mean = actual shoe size-mean shoe size SS= sum of squared deviations Degrees of freedom (df) - is the variance you are allow to have from forecasting P-value: - Tells which variable is most significant in relation to what is being forecast. (The smaller the p-value, the more significant). Significant of F: - How reliable is the model, the smaller the figure, the better. If it is not less than 0.1, then the correlation is not meaningful. Some truth about forecasting 1. What happen in the past is more likely to happen in the future. 2. Forecast accuracy increases for shorter time frames 3. Combined forecasting is more accurate than forecasting done on an individual basis. 4. Forecasts are hardly ever accurate. Formulas SS= df * MS 2 R-Square= SS/SST or (multiple R) Multiple R= Square root of R-Square Standard error= Square root of MS of the (residual) Total degrees of freedom (n-1) where n is the observation total Total df for regression = the number of variables=K Total df for residual = (n-K-1) Trend and Seasonal Index/Seasonality All seasonality factors/indices must total to (4) Deseasonalized sales:- It is moving the seasonality from the data. (Actual sales/seasonalized index) Reseasonalized forecast (seasonalized) :- Making the final forecast. (deseasonalized forecast x seasonalized index (SI)) Forecasting: Decomposition of the trend and seasonality Moving average: - Take the average of the actual sales of two period above, the current period, and one period below. Centered average: - Take the average of the moving average current period and the immediate period that follow. Raw Indices: (Actual sales/centered average) Seasonalized index= averaging the raw indices for the different years (Year 1, year 2, year 3, year 4 etc.) Y-Y hat error (Bias):- Sales (Y) - Predicted sales (Y-hat) 2 Error squared (Y-Y-hat) Finding the seasonalized forecast of a specific quarter Sales trend: - This is computed by first finding the time period for the specified quarter for which you need to find the seasonalized forecast (eg. Q1, 2013) Always begin with the beginning quarter as period one. You then plug the specified time period number into the sales trend equation, which will give the deseasonalized forecast. Seasonalized forecast: To find the seasonalized forecast is (seasonalized index for the specified quarter, in this case is Q1 multiplied by the deseasonalized forecast). Chapter 6-Decision Analysis Decision: The action of choosing one alternative over the other. Analyzing a decision consist of: Decision Alternatives States of nature, and Payoffs Decision Alternatives: Are various choices that are available to choose from. (eg. Invest or not to invest, buy or not to buy) States of Nature: These are events where the decision maker cannot control (eg. The economy, the weather etc., or invests in a small, medium, or large hotel) Payoffs: Payoffs can be expressed as profits or loss and are stated as the rewards receive from the chosen decision. List of decision-making environments 1. Ignorance: Assumes equality because the decision maker has no knowledge of the probabilities at which the state of nature will occur. 2. Risk: Under this environment, the decision maker now knows the probabilities at which the states of nature will occur. 3. Certainty: The decision maker is aware of which state of nature will happen. List of Criterions under Ignorance 1. Maximax: Also known as risk seeking behavior, this is where the decision maker choses the best of the best decision payoffs. In this criterion, the decision maker choses the max value from each decision alternative, and then the maximum of those chosen. 2. Maximin: Also known as Risk adverse behavior, which means the decision maker is one who wants to avoid risk and chooses the best of the worse payoffs. For this criterion, the decision maker chooses the best of the worse payoffs from each alternatives and then the best of those chosen. 3. Laplace: In this criterion, the decision maker averages the payoffs for each alternative and then chooses the alternative with the highest average. 4. Minimax Regret/Lost opportunity: Shows the amount that can be lost from choosing other alternative. This is calculated by taking the maximum payoff of each state of nature and subtracting their corresponding alternatives. If this is done correctly, there will be a payoff of zero in each state of nature. (It is also important to note that all the numbers in this table should be positive numbers) List of criterions under risk 1. EVUII/EVwII/EV: Expected value of a decision made with perfect information. It is calculated by finding the probability of each corresponding alternative and then choosing the alternative with the max number. 2. EVUPI/EVwPI: Expected value of decision made with perfect information. It is calculated by finding the probability of the max payoff in each state of nature and then sums the weighted averages to get the EVUPI/EVwPI. 3. EVPI: Maximum price of perfect information or the maximum price the decision maker is willing to pay for the perfect information. It is calculated by subtracting EVUII from EVUPI. 4. EVSI: Maximum price the decision maker is willing to pay for sample information, it is calculated by subtracting EV from EVUSI. 5. EOL: Expected opportunity loss. This is done by calculating the weighted average of the opportunity losses for each alternative and then choosing the lowest value of the sum EOL. EOL equals to EVPI EVUPI equals to EV + EOL Probabilities should always equal to one. Prior probabilities is what is given Marginal probabilities is the odds of doing one thing Joint Probability is the probability of two events happening at the same time. It is calculated by multiplying the prior probability by each new probability. Decision tree Payouts are listed at the right end of the decision tree Probabilities are listed next to the state of natures Decision branches are the decision alternatives. Branches from event nodes are state of natures. Decision /choice node is represented by squares:This is the point where the selection made by the decision maker is recorded. It also consist of branches representing each decision alternatives Chance/event node is represented by circles: Consist of branches that represents event that may take place or branches representing the state of nature payoffs. Decision branches are the decision alternatives and branches from event nodes are state of natures. In addition to my notes, I strongly encourage everyone to attempt some of the exercises in the textbook as they are very helpful and can help one to do well in the final exam. Good luck!!!!!!

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