...032__________________ Reading Module #1 On Your Own This activity in Reading Module 1 gives you two reading passages to practice applying the Active Reading Strategies you have learned. Directions: Complete the activity by reading each passage and answering the questions. Do not forget to spell check and proofread your work. When you have completed this activity, please upload it to the Dropbox for this week. Reading #1: The Body’s Response to Stress Whenever we are surprised by a sudden stressor, such as someone swerving into our lane of traffic, the adrenal glands jump into action. Using the Predicting Strategy | Read the title and first sentence above. Now predict what you think the passage will be about. | Your prediction about the passage:its say about the health and breathing problem rate like over population .its should be more dangerous become a day | Now read the whole paragraph. The Body’s Response to Stress Whenever we are surprised by a sudden stressor, such as someone swerving into our lane of traffic, the adrenal glands jump into action. These two almond-shaped glands sit above the kidneys. They secrete adrenaline as well as other hormones into the bloodstream. As a result, the heart speeds up, breathing rate increases, blood pressure elevates, and the flow of blood to the muscles increases. It is believed that this sudden burst of energy and strength has helped generations of humans survive during adversity. This response is also believed...
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...definite and specific plans or budgets for a limited future period, usually the ensuing fiscal or calendar year. These plans, which take into account all phases of the budgeted operations, are given expression in financial terms. They also become standards against which to measure and evaluate actual performance as the period progresses. Budgeting is, therefore, not only a short term planning and coordinating process, but is also a means of exerting management control over budgeted operations. Budgeting becomes a management process rather than a financial one. Budgeting, as Distinguished from Forecasting Forecasting and budgeting, despite their similarities, should be clearly distinguished from each other. A forecast is a prediction of likely future events. But, unlike a budget, a forecast is not a plan for achieving those forecasted (desired?) results. And a forecast is too general to serve as a control standard against which actual progress across the period can be measured. Forecasts are the starting point in the budget planning process. Most firms and public agencies precede the preparation of their operating budgets with revenue forecasts and expectations of economic conditions within their markets or constituencies, and in the world's economy as a whole. We've just witnessed how economic conditions in Greece and Italy have affected the United States, as well as the poor financial climate since 2008. Essentially, the forecasting process analyzes the...
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...Inventory Proposal Inventory should be closely followed especially for companies that rely heavily on seasonal sales. Companies will often times use forecasting models to predict future sales and the amount of inventory needed to make their sales quotas. SSMA retail outlet will evaluate their current data from the past four years to identify any inventory issues by using indices to analyze the data and histograms to forecast future inventory cost saving models. SSMA is a small retail outlet store that sells a variety of summer clothing throughout the year. The new owners have decided to install an inventory system to keep better track of the inventory, however, a complete analysis of the previous data must be completed and a forecasting model must be completed in order to determine how much inventory will be needed in order to increase net profit. The store is located in an outlet mall that relies heavily on tourists; therefore, sales are very seasonal. The summer data shows that they have an uptick in inventory during the high summer months and the owners should determine by using indices and histogram models to assist in predicting future sales. Inventory management problem SSMA past data will help predict trends and assist in decision making processes to determine inventory loss, inventory shelf life, future sales and or clearance items. This type of information is critical and valuable for small retail stores and will need this data in order to recognize key areas...
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...Choose one of the forecasting methods and explain the rationale behind using it in real life. I would choose to use the exponential smoothing forecast method because it weighs the most recent past data more strongly than more distant past data. This makes it so that the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be in higher demand in recent times than past. Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. By looking at the data of other companies the fast food chain would be able to put together a forecast to determine if their business venture was viable. They could examine the sales data and determine through a exponential smoothing forecast if it made sense for them to enter into the market. This would show the trends and changes in the data more recently rather than in past time. What is the difference between a causal model and a time- series model? Give an example of when each would be used. The time–series model is based on using historical data to predict future behavior. This method could be used by a retail store, fast food restaurant or clothing manufacturer to predict sales for an upcoming season change. The causal model uses a mathematical correlation...
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...Unhelpful Thinking Habit Alternative more balanced thought Mental Filter Am I only noticing the bad stuff? Am I filtering out the positives? Am I wearing those ‘gloomy specs’? What would be more realistic? Mind-Reading Am I assuming I know what others are thinking? What’s the evidence? Those are my own thoughts, not theirs. Is there another, more balanced way of looking at it? Prediction Am I thinking that I can predict the future? How likely is it that that might really happen? Compare & despair Am I doing that ‘compare and despair’ thing? What would be a more balanced and helpful way of looking at it? Critical self There I go, that internal bully’s at it again. Would most people who really know me say that about me? Is this something that I am totally responsible for? Shoulds and musts Am I putting more pressure on myself, setting up expectations of myself that are almost impossible? What would be more realistic? Judgements I’m making an evaluation about the situation or person. It’s how I make sense of the world, but that doesn’t mean my judgements are always right or helpful. Is there another perspective? Emotional Reasoning Just because it feels bad, doesn’t necessary mean it is bad. My feelings are just a reaction to my thoughts – and thoughts are just automatic brain reflexes Mountains and molehills Am I exaggerating the risk of danger...
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...Using the Century National Bank data set, Learning Team A researched the usage of ATM’s by the account holders whom possessed debit cards versus those whom use services offered in some of the bank locations. Foreseeing and predicting some of the trends that are normally seen in the banking industry can be seen by using the linear regression analysis. With this research, our learning team formulated a hypothesis statement. A regression hypothesis test was used on the data set, and the results were shown. This will also determine if whether there is a positive or negative relationship between the independent and dependent variables. The results were used to create a scatter graph for a visual of the linear regression analysis. To determine if the amount of accounts served affects how much the automatic teller machine (ATM) is used versus use of the services offered by staff members, Learning Team D continued use of the bank data set from two bank locations, bank two and bank four. Learning Team D used Doane’s rule for the hypothesis statement with the number of accounts being n and the locations being k since the hypothesis statement required linear regression analysis for testing. H0, represents the null hypothesis in this research study i.e. that the ATM usage is not affected by the number of accounts serviced. As an alternative hypothesis, H1, postulates that the amount of ATM usage is not affected by the number of accounts serviced. Doane’s rule is n/k ≥ 5. Learning Team...
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...Guillermo Furniture Store Concepts Introduction Guillermo’s Furniture Manufacturing facility has been located in Sonora, Mexico successfully until the late 1990’s. Guillermo Navallez noticed a decline in profit and found that he would need to make changes to his manufacturing establishment in order to financially stay in business. Navallez used concepts from staying in the competitive economic environment and using accounting and research to establish some potential changes in Guillermo’s future endeavors. The Competitive Economic Environment Navallez saw through his loss of profit that there was competition for his manufacturing facility. Through researching his competitors he came up with future options for his manufacturing facility to stay in business and grow. Several of the concepts and principles of the financial environment were used. Navallez obtained information about the overseas competitor using high-tech machinery compared to the labor force. Profit margins were shrinking and decisions were going to be necessary. Navallez had options of consolidating into a larger organization through acquisition or merger. Navallez wanted to stay in business and be active but not take any more time away from his family. Principles Used The Principle of Self-Interested Behavior: People Act in Their Own Financial Self-Interest. (Emery, 2007, p. 20). This principle uses the theory of choosing the most financially opportunity. “In such impersonal transactions, getting the most...
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...The use of Financial Ratios for Research: Problems Associated with and Recommendations for Using Large Databases Introduction The use of financial ratio analysis for understanding and predicting the performance of privately owned business firms is gaining in importance in published research. Perhaps the major problem faced by researchers is the difficulty of obtaining an adequate sample of representative financial statements with many studies using 50 or fewer firms for analysis. However, when larger databases are used, it is important to know that they have problems as well and that adjustments to these samples must be made to permit the use of multivariate analysis techniques. Understanding how to properly use large databases for ratio analysis will become of importance now that the Kauffman Center for Entrepreneurial Leadership (KCEL) has developed a financial statement database of more than 400,000 privately owned firms with a significant number of these including a base year and three operating years of financial statements. This database is currently available to a team of scholars working closely with the KCEL on selected internal studies. It is expected that this database will become generally available to researchers and this source of financial statement information is likely to become the standard for financial performance research in the future. For the first time, scholars will have a large commonly available database of privately owned firm financial...
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...Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself, there will be identifiable patterns of behaviour that can be used to predict future behaviour. This model is useful when you have a short time requirement (eg days) to analyse products in their growth stages to predict short-term outcomes. To use this model you look at several historical periods and choose a method that minimises a chosen measure of error. Then use that method to predict the future. To do this you use detailed data by SKU's (Stock Keeping Units) which are readily available. In TSM there may be identifiable underlying behaviours to identify as well as the causes of that behaviour. The data may show causal patterns that appear to repeat themselves – the trick is to determine which are true patterns that can be used for analysis and which are merely random variations. The patterns you look for include: Trends – long term movements in either direction Cycles - wavelike variations lasting more than a year usually tied to economic or political conditions...
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...Statistical Analysis and the common perception of property crime This project will determine whether or not a relationship exists between various variables as they pertain to, or not, property crime. Property crime affects everyone in the population, directly or indirectly. The ability to understand and predict indicators (independent variables) of property crime can help focus the finite assets of society and the government to reduce, or in a utopic vision eliminate, property crime. To help determine the applicability of the variables, a multiple regression was performed on the property crime dataset (appendix A) to analyze the dependent or independent relations to the crime variable. APPX A shows the results of the regression analysis between each of the 8 variables when associated with crime. By using Minitab 16 for this procedure, tests were run to measure the relative p-values among the independent variables. Each independent variable in the regression models had tolerance values close to 0.1 and variance inflation factors (VIF) well below the target value (below 0.05, Allen tutorials cconline.net/browna/tutorials_mt16.htm). In fact, all were below 0.05 indicating the predictive value of the regression models and the rejection of Ho (null hypothesis) Ho: none of the variables predict Property Crime Ha: at least one of the independent variables is helpful to predict property crime The dependent variable is CRIMES: Property crime rate per 100k inhabitants...
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...Forecasting method. A Forecast provides a reasonable prediction for a future event. Being able to predict the future can provide a valuable asset for any organization. Predictions will not always be one hundred percent accurate, but they can be a reasonable guide to making decisions based on systematic data. Taylor, 2010, discusses two widely used forecasting methods; time series analysis and regression. This project will present information on forecasting in the form of a storyboard. This project will provide an overview of forecast methods, how forecasts are measured, and identify different types of forecasting methods. According to author Bernard W. Taylor, 2010, “a variety of forecasting exist, and their applicability is dependent on the time frame of the forecast, the existence of patterns in the forecast, and the number of variables to which the forecast is related” (p.682). Time frame classifies forecast into three different categories, short –range, medium-range, and long-range forecast. Short-range forecast are forecast such as Weather forecast in that they predict daily, or weekly short-range outcomes. Medium-range forecast are typically a month to a year, and Long-range forecast are long-term patterns that are considered trends. These Forecasts are documented and observed from one to two years (Taylor, 2010). It is very important to make reliable predictions, and using forecasting, but the further out the prediction the less accurate the...
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...TO: Senior Manager FROM: James Adams, Michael Hsu, Siyao Li, David Miller, Prerak Patel DATE: September 10th, 2012 SUBJECT: Cisco Case In this report, our group will examine what went wrong at Cisco in the early 2000’s. We will address what and how the main problem occurred for Cisco. Then, we will provide a solution on how to prevent this error from occurring in the future. The Nature of the Problem Cisco was one of the fastest growing companies in the world, on pace to size up the United States economy. They were a primary provider of switches and routers. Their success was largely credited to a computer model that helped forecast their future sales and demand. Even when the industry showed signs of struggle, Cisco still managed to maintain successful quarters. They had seen over a decade of growth until the third quarter in 2001. In an unforeseen turn, Cisco in the third quarter of 2001 dropped sales by 30%, wrote-off $2.2 billion of inventory, and laid-off nearly 8,500 employees. Cisco, a powerhouse company with increasing projections, took a big hit. Stock prices dropped by $68.37 in a 3 month span. The Real Causes Cisco's demand forecasting did not correctly predict the economic trend as it did not account for economic spending, interest rates, and debt. Cisco overlooked these macroeconomic factors, not realizing that this type of high demand was not sustainable. Also, the company was recording profits at an all-time but failed to take into consideration...
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...Nadia Daniella BUAD 304 – 14734 Discussion Assignment #1 September 19, 2012 1. Despite the difficulty in making predictions, many people make assertions about what will happen in the future. I think that this is because of the many biases and errors that are involved in decision-making. Overconfidence bias means that most people are far too optimistic in analyzing situations or making decisions. Although no one has the ability to accurately predict the future, people think they know more than they actually do, making them more confident than they should be when they try to do so. I also think that people make assertions because we tend to selectively gather information that confirms our past and use that to create an assumption for the future. However, the information that we gather by this process tends to be biased to our own perception. I believe that people are also very confident in making future predictions because we think that we have some control over our world, destiny, and therefore the course of the future. We try to make sense of random events and see things in patterns and seek out meanings when in reality, too many variables are random and not in our control. We also have the tendency to believe that things are obvious after the fact. This hindsight bias creates the illusion that we know more about the past and lets us think that we are better predictors than we are. 2. When Vice President Joe Biden falsely predicted that more people would be getting...
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...because you get to know more about your earth. The simple answer is, because it's awesome. Otherwise, Earth Science is very important in understanding how our world works. It has many different applications, from oil and mining, to waterways and seas, to understanding Earth History, and to engineering and building, as well as many others. Because you want to know about the Earth? Or because you think that the qualification will help you to get the type of job that you want. SO we can make good predictions an be prepared what can happen in the future. One example of a bad prediction is 2012. 1st. the myians never predicted that. 2nd. My dad talked to a scientist about it and he said everyone that's saying different ways that other people said the world is going to end is fake. so we will know that earth science is literacy of critical importance to our nature. SO we can make good predictions an be prepared what can happen in the future. One example of a bad prediction is 2012. 1st. the myians never predicted that. 2nd. My dad talked to a scientist about it and he said everyone that's saying different ways that other people said the world is going to end is...
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...Push Strategy: Push strategy is where companies forecast demand before ordering based on the willing buyers and also the goods doesn’t runout unexpectedly. For example winter jackets are required by retailers during the end of summer or during start of fall and winter. Companies can predict in their supply chain as they know what will be needed long before their demand actually arrives. So the main disadvantage in Push system is it’s purely based on forecast which is a guess. For example Billions are spent in US on computers and software’s but most of the time demand and forecast differed. So if forecast goes wrong there will be excess of investments and profits will decrease. Sometimes we may runout of inventory and lose customer loyalty. Pull Strategy: Pull strategy is the one which is based on demand data. This is based on the actual consumption at stores and also forecast rather than predicting sales. For example Custom computers are build by direct computer seller only when the order is received from consumer. Cloud technologies these days are providing daily consumer demand for generating forecast. Based on this strategy, a company can manufacture goods based on daily demand and reduce production of goods which are moving slowly so that inventory maintenance cost is reduced for goods which are not in demand. The disadvantage of this strategy is that sometimes we may runout of inventory if demand increases and if we are unable to increase production. Aggregate Forecast: ...
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