Running Head: STATISTIC PROJECT 1 Statistics Project Group Members : Names & IDs
STATISTIC PROJECT 2 I. Introduction : This project will execute time series method with the application of R Analysis. The time series is very important to apply for this project because it would allow us to evaluate the forecasting. Knowing that forecasting is the science of estimation. The application of time series in this project would also allow us to measure the quality of a certain process. Control procedures are of several different kinds. In quality control, the observations are plotted on the control chart and the controller takes action as a result of studying the charts. A stochastic model is fitted to the series. Future values of the series are predicted and then the input process variables are adjusted so as to keep the process on target. This is how time series would be helpful for forecasting, as this project would like to execute into with the use of the dataset of an "annual temperature anomalies (1850–2018) for the northern hemisphere". II. Materials and Methods : Description of the data and the methods used in the analysis. The name of the dataset used is "temp_nh", this is the annual temperature anomalies (1850–2018) for the northern hemisphere is the dataset that will be going to be executed in the R analysis. This dataset has two variables; "Year and the Temp-NH". These two variables have two the same count of observation "169". For the independent variable in this dataset is "Year" and the dependent variable is " Temp-NH". The dataset has collected from "[Data source: Climate Research Center, University of East Anglia, UK". Charts are used in this project (time series charts) to present the results from the tool of R software and Rstudio. This software is a good tool to execute the analysis of time series because it has a different kind of methods in running the forecasting process of the dataset used and accurately performed the outcomes through charts and obtaining the descriptive statistical results of the dataset used such as mean, standard
STATISTIC PROJECT 3 deviation, and etc. There are application graphical techniques to understand the nature of variation in the data in the dataset used. As part of the methods, descriptive statistic in the R
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