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Multivariate, Time-Series, and Survival Analysis

Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis. Multivariate statistics is concerned with understanding the different aims and background of each of the different forms of multivariate analysis and how they relate to each other. It is also concerned with multivariate probability distribution in terms of how these can be used to represent the distribution of observed data and how they can be used as part of statistical inference, where several different quantities are of interest to the same analysis.

A time series is a sequence of data points measured at successive points in time spaced at uniform time intervals.  Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance and many more. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistic and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.

Survival analysis a branch of statistics that deals with the analysis of time to events; for example, the death in biological organisms and failure in mechanical systems. This topic is called reliability theory. Survival analysis also attempts to answer the questions of that is the proportion of a population which will survive past a certain time, or can multiple causes of death or failure be taken into account. Survival analysis involves the modeling of time to event data. Reoccurring events or repeated event models relax assumptions that only a single event occurs for each subject. 

Categories within Multivariate, Time-Series, and Survival Analysis

Time Series Analysis

Postings: 51

A time series is a sequence of data points measured at successive points in time spaced at uniform time intervals.

Forecasting: Graphing Trends

1) Plot the following time series to determine which of the trend models appears to fit better. Period 1 2 3 4 5 6 7 8 9 10 Time Series 55 57 53 49 47 39 41 33 28 20 2) The following trend line and seasonal indexes were computed from 4 weeks of daily observations. Forecast the 7 values for next week. y=120 + 2.3t t=1,2..

Reliability of a Series System Problem

An engine system consists of three main components in a series, all having the same reliability. Determine the level of reliability required for each of the components if the engine is to have a reliability of 0.998.

Seasonally Adjusted Forecast

Small Wonder, an amusement park, experiences seasoned attendance. It has collected two years of quarterly attendance data and made a forecast of annual attendance for the upcoming year. Compute the seasonal indexes for the four quarters and generate quarterly forecasts for the coming year, assuming annual attendance for the comi

Forecast: Monthly Index

Each worker's monthly caseload is determined by the number of cases received and the number of workers available during the month. There are two excel spreadsheets attached. One shows the number of cases for each month, number of staff who reported to work, and the caseload per worker for each of the past 48 months. The other

Forecast Analysis

The Bayside Fountain Hotel is adjacent to County Coliseum, a 24,000 seat arena that is home to the city's professional basketball and ice hockey team and that host a variety of concerts, trade shows and conventions throughout the year. The hotel has experienced the following occupancy rates for the past 9 years, since the colise

Exponentially Smoothed Forecast, MAD, MAPD in EXCEL

The manager fo the Petroco Service Station wants to forecast the demand for unleaded gasoline next month so that the proper number of gallons can be ordered from the distributor. The owner has accumulated the following data on demand for unleaded gasoline from sales during the past ten months: Month Gas

Development of a Forecasting Chart

I have tried several times and missing a step somewhere in the process. Rosa's Italian restaurant wants to develop forecasts of daily demand for the next week. The restaurant is closed on Mondays and experiences a seasonal pattern for the other six days of the week. Mario, the manager has collected information on the number o

Operations Management (Forecasting)

A manager receives a forecast for next year. Demand is projected to be 600 units for the first half of the year and 900 units for the second half. The monthly holding cost is $2 per unit, and it costs an estimated $55 to process an order. a. Assuming that monthly demand will be level during each of the six-month periods cover

Upper and Lower control limits

1. Which of the following situations suggest a process that appears to be operating in a state of statistical control? a. A control chart with a series of consecutive points that are above the center line and a series of consecutive points that are below the center line. b. A control chart in which no points fall outside


1. The Omni Hotel in Charleston SC wants to forecast their expected occupancy rate for the next 5 years. Using the past data below, forecast the hotel's future occupancy. Year Occupancy Rate (%) 1 82 2 77 3 76 4 80 5

Linear Trend Forecasts in Excel

The Dean of the business college at Strayer University wants to forecast the number of students who will enroll in business courses at the Charleston SC campus. Historically the enrollments have been: Year Students 1 400 2 450 3

Time-Series line chart

Use Excel or MegaStat to make an attractive, well-labeled time-series line chart. Adjust the Y-axis scale if necessary to show more detail (since Excel usually starts the scale at zero). If a fitted trend is called for, use Excel's option to display the equation and R2 statistic. Include printed copies of all relevant graphs w

Forecasting Demand Each Season

1000 tires sold on average each of the past few years. the past 2 years 200 and 250 respectively in the fall season, 300 and 350 in the winter season, 150 and 165 in the spring season, and 300 and 285 in the summer season. Projected sales next year are 1200. What will the demand be each season? I would like this in a

Time Series Problem

(See attached file for full problem description) --- Let {Z(s), s } is a stationary process. Define Suppose (Z(s1),....,Z(sn)) is observed at know locations {s1,....,sn}. Please find an optimal linear prediction of Z(s) based on the observations and function. How would you estimate function from the observations? ---

Participant solved puzzles are assessed.

This generic question is posed: You have timed the rate at which participants can solve puzzles under three conditions of noise: high, medium,low. In addition, the participants are under the influence of either marijuana, caffeine, or alcohol. What kind of design do you have? a. A 2X3 between-subjects factorial design b.