I have attached 15 questions on excel. I would like the answers and the problems worked out in excel.

An insurance company wants to design a control chart to monitor whether insurance claim forms are being completed correctly. The company intends to use the chart to see if improvements in the design of the form are effective. To start the process, the company collected data on the number of incorrectly completed claim forms over the past 10 days. The insurance company processes thousands of these forms each day, and due to the high cost of inspecting each form, only a small representative sample was collected each day. The data and analysis are shown in Exhibit 13.6. Write a 150-300-word paragraph comparing the simple moving average weighted moving average, exponential smoothing, and linear regression analysis time series models pg. 362 Simple moving average is basically what it says, it is average of previous demand and use the data to build a forecast. It is generally accepted when seasonal factors or trends are not involved. When a lot of changes are not expected a simple moving average can be used. Previous data is used to calculate and it builds and old data can be taken out like a monthly rolling forecast that includes 5 months. A weighted moving average is a more complex simple moving average. Now data is given a weight to it, meaning certain periods have more say in the upcoming forecast. Experience and trial and error are the simple ways to choose a weight for a given period set of data. Exponential smoothing is where you still use a weighted moving average but each period set of data that decrease exponentially. This will allow you to keep up with current data and not use data that may become obsolete because it is old. This is the most used method in forecasting. It is one of the more accurate methods too. Linear regression is where you try to predict numbers based on the relationship of two data points that help create a straight line. Past data and future data tend to fall in a straight line that is why linear regression method can be used or is why they think they need to use it. Time series models is ordered data that consists data like trends and seasonal factors. Linear regression is used in time series models. The data forms that straight line and goes from previous data to the future data based on the time. Forecast the demand for each quarter of the next year using trend and seasonal factors. Demand for the past two years is in the following table: Write a 150-300-word paragraph explaining the market research, panel consensus, historical analogy, and Delphi method qualitative forecasting techniques. pg 494 Market research allows you to determine or forecast the demand for the future by looking at previous trends and seasonal factors. You can use the seasonal data you collected to help your forecast your demand. For example Ice Cream becomes quite popular during the summer. Panel consensus technique is the idea that the more people you involve in forecasting demand the closer the number to actual it will be. This would be the idea of having finance, operations and sales people together assisting in coming up the forecast. You can involve everyone in an open meeting or keep it to senior level personnel. No time is involved in the process since the knowledge of everyone can bring numbers to the table like market research which can take a long period of time. Historical analogy is a forecasting technique that relies on previous product or model. It can be a similar product if no product historical data exists for example, newer generation iPhones. The book uses VCR and DVD in similarity. The Delphi method is a technique that is similar to panel consensus but the identity of the person speaking or person's idea is concealed. This way no one person has more say than the other based on position in the company. You can do this in a questionnaire form.