Hello Everyone,
In my study, I'm utilizing Eviews 10 software to analyze secondary time-series data. According to the manual, it includes inferential statistics and a reliability test. Could someone kindly clarify what tests go under inferential tests and what sort of tests I need to do to confirm the reliability of the collected data?
Thanks
Inferential statistics are a set of methods used to make inferences about a population based on a sample of data collected from that population. The goal of inferential statistics is to estimate population parameters (such as the mean or proportion) based on sample statistics (such as the sample mean or proportion) and to make judgments about whether the sample statistics are likely to represent the population parameters.
Reliability tests for secondary time series data are used to assess the consistency and stability of the data over time. A common reliability test for time series data is the Augmented Dickey-Fuller (ADF) test, which tests the null hypothesis that the data is non-stationary (i.e., the mean, variance, and autocorrelation are not constant over time) against the alternative hypothesis that the data is stationary.
The ADF test is a statistical test that is used to determine whether a time series is stationary. The test statistic is calculated using the data, and a p-value is obtained. The p-value is used to evaluate the null hypothesis that the data is non-stationary. If the p-value is less than the significance level (typically 0.05), then the null hypothesis is rejected, and the data is considered stationary.
A reliability test for secondary time series data is also important because it can help to identify any issues with the data, such as outliers or missing values, that may need to be addressed before further analysis is conducted.
It's important to note that when using inferential statistics and reliability tests for secondary time series data, it's important to consider the characteristics of the data, such as the frequency of the data, whether it's stationary or not, and any other relevant factors that may affect the interpretation of the results.
Inferential statistics is the most appropriate research method for analyzing data that has been gathered through observation or measurement. Inferential statistics is used to draw conclusions about a population based on the data collected
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