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Welcome to Free Excel Templates page. Thanks for visiting. We have 100s of free templates, tutorials and examples on Chandoo.org. In this page, I have shared our top 20 templates. Please feel free download them and use them as you see fit.

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4. You can download a free Stock Inventory Control template from Smartsheet. This is a detailed workbook that goes beyond a basic inventory spreadsheet. There are additional tabs to track vendors, stock inventory items, and track items on order.

5. Microsoft Office provides a free basic Equipment Inventory List template. This one allows you to record the model and serial numbers of equipment, vendor information, model and serial numbers, and depreciation.

8. Vertex42 offers a free Software Inventory Tracking template. This Excel template has columns to track the software item information (name, version, category, developer); purchase information (supplier, date, price); license information (type, serial number or code, expiration date, quantity); and other relevant data.

14. Microsoft Office offers a free Personal Inventory template. This simple spreadsheet has just a few columns to enter information about the item, a category, serial number, and value. There is also space to enter your insurance company and policy information.

16. Vertex42 has a Home Inventory template that is free as well. This one is primarily used to document assets for insurance coverage, but you can use it for any type of personal asset tracking. There are columns to log the location (room) of the item, description, purchase information, condition, estimated value, model and serial #, notes, and a link to a photo.

Suppose we wish to calculate seasonal factors and a trend, then calculate the forecasted sales for July in year 5. The first step in the seasonal forecast will be to compute monthly indices using the past four-year sales. For example, for January the index is: S(Jan) = D(Jan)/D = 208.6/181.84 = 1.14, where D(Jan) is the mean of all four January months, and D is the grand mean of all past four-year sales.Similar calculations are made for all other months. Indices are summarized in the last row of the above table. Notice that the mean (average value) for the monthly indices adds up to 12, which is the number of periods in a year for the monthly data. Next, a linear trend often is calculated using the annual sales: Y = 1684 + 200.4T, The main question is whether this equation represents the trend. Determination of the Annual Trend for the Numerical Example Year No: Actual Sales Linear Regression Quadratic Regression 1 1972 1884 1981 2 2016 2085 1988 3 2160 2285 2188 4 2592 2486 2583 Often fitting a straight line to the seasonal data is misleading. By constructing the scatter-diagram, we notice that a Parabola might be a better fit. Using the Polynomial Regression JavaScript, the estimated quadratic trend is:Y = 2169 - 284.6T + 97T2Predicted values using both the linear and the quadratic trends are presented in the above tables. Comparing the predicted values of the two models with the actual data indicates that the quadratic trend is a much superior fit than the linear one, as often expected.We can now forecast the next annual sales; which, corresponds to year 5, or T = 5 in the above quadratic equation: Y = 2169 - 284.6(5) + 97(5)2 = 3171 sales for the following year. The average monthly sales during next year is, therefore: 3171/12 = 264.25. Finally, the forecast for month of July is calculated by multiplying the average monthly sales forecast by the July seasonal index, which is 0.79; i.e., (264.25).(0.79) or 209. You might like to use the Seasonal Index JavaScript to check your hand computation. As always you must first use Plot of the Time Series as a tool for the initial characterization process.For testing seasonality based on seasonal index, you may like to use the Test for Seasonality JavaScript.Trend Removal and Cyclical Analysis: The cycles can be easily studied if the trend itself is removed. This is done by expressing each actual value in the time series as a percentage of the calculated trend for the same date. The resulting time series has no trend, but oscillates around a central value of 100.Decomposition Analysis: It is the pattern generated by the time series and not necessarily the individual data values that offers to the manager who is an observer, a planner, or a controller of the system. Therefore, the Decomposition Analysis is used to identify several patterns that appear simultaneously in a time series. A variety of factors are likely influencing data. It is very important in the study that these different influences or components be separated or decomposed out of the 'raw' data levels. In general, there are four types of components in time series analysis: Seasonality, Trend, Cycling and Irregularity. Xt = St . Tt. Ct . IThe first three components are deterministic which are called \"Signals\", while the last component is a random variable, which is called \"Noise\". To be able to make a proper forecast, we must know to what extent each component is present in the data. Hence, to understand and measure these components, the forecast procedure involves initially removing the component effects from the data (decomposition). After the effects are measured, making a forecast involves putting back the components on forecast estimates (recomposition). The time series decomposition process is depicted by the following flowchart: Definitions of the major components in the above flowchart:Seasonal variation: When a repetitive pattern is observed over some time horizon, the series is said to have seasonal behavior. Seasonal effects are usually associated with calendar or climatic changes. Seasonal variation is frequently tied to yearly cycles.Trend: A time series may be stationary or exhibit trend over time. Long-term trend is typically modeled as a linear, quadratic or exponential function. Cyclical variation: An upturn or downturn not tied to seasonal variation. Usually results from changes in economic conditions.Seasonalities are regular fluctuations which are repeated from year to year with about the same timing and level of intensity. The first step of a times series decomposition is to remove seasonal effects in the data. Without deseasonalizing the data, we may, for example, incorrectly infer that recent increase patterns will continue indefinitely; i.e., a growth trend is present, when actually the increase is 'just because it is that time of the year'; i.e., due to regular seasonal peaks. To measure seasonal effects, we calculate a series of seasonal indexes. A practical and widely used method to compute these indexes is the ratio-to-moving-average approach. From such indexes, we may quantitatively measure how far above or below a given period stands in comparison to the expected or 'business as usual' data period (the expected data are represented by a seasonal index of 100%, or 1.0). Trend is growth or decay that is the tendencies for data to increase or decrease fairly steadily over time. Using the deseasonalized data, we now wish to consider the growth trend as noted in our initial inspection of the time series. Measurement of the trend component is done by fitting a line or any other function. This fitted function is calculated by the method of least squares and represents the overall trend of the data over time.Cyclic oscillations are general up-and-down data changes; due to changes e.g., in the overall economic environment (not caused by seasonal effects) such as recession-and-expansion. To measure how the general cycle affects data levels, we calculate a series of cyclic indexes. Theoretically, the deseasonalized data still contains trend, cyclic, and irregular components. Also, we believe predicted data levels using the trend equation do represent pure trend effects. Thus, it stands to reason that the ratio of these respective data values should provide an index which reflects cyclic and irregular components only. As the business cycle is usually longer than the seasonal cycle, it should be understood that cyclic analysis is not expected to be as accurate as a seasonal analysis. Due to the tremendous complexity of general economic factors on long term behavior, a general approximation of the cyclic factor is the more realistic aim. Thus, the specific sharp upturns and downturns are not so much the primary interest as the general tendency of the cyclic effect to gradually move in either direction. To study the general cyclic movement rather than precise cyclic changes (which may falsely indicate more accurately than is present under this situation), we 'smooth' out the cyclic plot by replacing each index calculation often with a centered 3-period moving average. The reader should note that as the number of periods in the moving average increases, the smoother or flatter the data become. The choice of 3 periods perhaps viewed as slightly subjective may be justified as an attempt to smooth out the many up-and-down minor actions of the cycle index plot so that only the major changes remain. Irregularities (I) are any fluctuations not classified as one of the above. This component of the time series is unexplainable; therefore it is unpredictable. Estimation of I can be expected only when its variance is not too large. Otherwise, it is not possible to decompose the series. If the magnitude of variation is large, the projection for the future values will be inaccurate. The best one can do is to give a probabilistic interval for the future value given the probability of I is known.Making a Forecast: At this point of the analysis, after we have completed the study of the tim