How to graph confidence interval in excel,the secret rhonda byrne audio free download 64,secret life of bees movie online free - Step 2

The selection of a confidence level for an interval determines the probability that the confidence interval produced will contain the true parameter value. Theoretically, petroleum inventory levels are a measure of the balance - or imbalance - between petroleum supply (domestic production and imports) and demand, and thus provide a good market barometer for crude oil price (e.g. The level of inventories helps investors to estimate the prices for petroleum products – (e.g. Furthermore, crude oil is an important commodity in the global market and plays a crucial part of the economy, so fluctuations in its price have a direct influence on consumer prices. In this paper, we will model the dynamics in the crude oil stockpile (including SPRi) level over time, and project a forecast for the next 12 months. The general demand for petroleum products is highly seasonal and is greatest during the winter months, when countries in the Northern Hemisphere increase their use of distillated heating oils and residual fuels. During the summer months, supply exceeds demand and petroleum inventories normally build; whereas during the winter, demand exceeds supply and inventories are drawn down. For our sample data, we’ll use the EIA data from the weekly petroleum status report between January 1991 (Post- Gulf War) and August 17th, 2012. At first glance, the data does not exhibit the 12-month seasonality that we were anticipating, so let’s look closer. Now, to better distribute the values, we’ll compute the logarithmic transformation of the time series and use it primarily in our analysis. Using summary statistics, the time series is serially correlated and its probability distribution exhibits fat-tails.
For monthly time series, we’ll interpolate the reserve level on the last day of each month (similar to the EIA monthly report) using the weekly time series.
The correlogram plot suggests a seasonal ARIMAi (SARIMAi) model with a season length of 12 months.

Using the NumXL Airline wizards, we specified a 12-month Airline model and calibrated its parameter values using the log monthly levels. The calibrated parameters’ values yield a stable model and satisfy the underlying model assumptions. The model is saying that the year-over-year of the monthly change () is basically a special moving average process of order 13.
The Airline model’s functions compute the mean and confidence interval of the monthly log-level. In the graph above, the dotted line represents the Airline mean forecast and the shaded area is the 95% confidence interval.
To forecast the crude oil reserve level for any arbitrary future date (other than the end of the month), we simply interpolate the value of this date using the monthly forecast. For the current month where EIA issued one or more of its weekly reports, we use the latest report value and the projected end-of-month forecast value to interpolate the value for an intermediate date.
In this paper, we analyzed the weekly time series of crude oil reserve levels and demonstrated its key statistical properties: integration and seasonality.
Using Airline(12) we projected an 18 month forecast for the crude reserve level and constructed a 95% confidence interval. Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. I want to identify the maximum value of Y and corresponding X value is position of median in box and whisker plot. Not the answer you're looking for?Browse other questions tagged r graph boxplot confidence-interval or ask your own question. This weekly report gives an overview of the level of crude reserves held and produced by the U.S.

As such, if low reserves are reported, crude oil prices are most likely to increase and therefore drive up consumer prices as well.
Supply of crude oil, including both production and net imports, also shows a similar seasonal variation but with a smaller magnitude. The leap year phenomenon makes it difficult to pin-point a season length in number of weeks.
The EIA publishes the monthly crude reserve stockpile level, but these levels are published once a month and are generally for the previous three month period. In short, the Airline model is a reasonable candidate model for the monthly log-level process. Next, using the weekly time series, we interpolated the monthly time series and constructed an Airline (12) model. Whenever the value of Y declines by 1 point (either left or right) is the 99% confidence interval (will be inside the box) and whenever the Y declines to 2 (both left and right), the corresponding positions in x will be represented by the whisker. Just like any other goods and services, prices for petroleum products are determined by supply and demand. The Airline Model can not only capture the seasonality, but it requires only three (3) parameters to estimate, so it is typically a good starting point.

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