what is the difference between cross sectional data and time series data?
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Time-series data is a set of observations collected at usually discrete and equally spaced time intervals. ... Cross-sectional data are observations that come from different individuals or groups at a single point in time.
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Strengths and weaknesses of cross-sectional studies Relatively quick and easy to conduct (no long periods of follow-up). Data on all variables is only collected once. Able to measure prevalence for all factors under investigation.
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Currency can measure how up-to-date data is, and whether it is correct despite the possibility of modifications or changes that impact time and date values. ... Timeliness can be measured as the time between when data is expected and when it is readily available for use.”
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Histograms are used to show distributions of variables while bar charts are used to compare variables. Histograms plot quantitative data with ranges of the data grouped into bins or intervals while bar charts plot categorical data.
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According to Wooldridge (2010), pooled OLS is employed when you select a different sample for each year/month/period of the panel data. Fixed effects or random effects are employed when you are going to observe the same sample of individuals/countries/states/cities/etc.
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Put simply, a time-domain graph shows how a signal changes over time, whereas a frequency-domain graph shows how much of the signal lies within each given frequency band over a range of frequencies.
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The main difference between attribute data and spatial data is that the attribute data describes the characteristics of a geographical feature while spatial data describes the absolute and relative location of geographic features.
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Data is a set of qualitative or quantitative variables – it can be structured or unstructured, machine readable or not, digital or analogue, personal or not. ... There are “dimensions” that distinguish data from BIG DATA, summarised as the “3 Vs” of data: Volume, Variety, Velocity. Hence, BIG DATA, is not just “more” data.
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Time series is strong stationery given that unconditional joint probability distribution does not change when shifted in time, this means that the distribution is the same through time. In weak stationary, a time series is when the mean and the variance are not constant through time.
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There are two sources of data in Statistics. Statistical sources refer to data that are collected for some official purposes and include censuses and officially conducted surveys. Non-statistical sources refer to the data that are collected for other administrative purposes or for the private sector.
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As you may have guessed, static data refers to a fixed data set—or, data that remains the same after it's collected. Dynamic data, on the other hand, continually changes after it's recorded in order to maintain its integrity. In concept, the difference between static and dynamic data is simple enough to understand.
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They follow the same structure of date parts (i.e. year, quarter, month, week, day and so on). The difference between date values and date parts is that date values imply a level of detail in a timeline. Remember continuous fields do not create headers, they create an axis.
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Primary data is a real-time data whereas secondary data is one which relates to the past. Primary data is collected for addressing the problem at hand while secondary data is collected for purposes other than the problem at hand. Primary data collection is a very involved process.
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Real Time data vs Historical data Data and trends can be accessed through smart phones, tablets. But historical data tells us the past trends. It helps us to analyze mistakes and corrective action which needs to take place to make the process more effective. But it takes time and effort to analyze and make a decision.
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A line graph uses both the x- and y-axes, while a scatter plot only uses the y-axis. ... A line graph presents continuous and linked data, while a scatter plot presents unlinked data. You just studied 10 terms!
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The benefit of a cross-sectional study design is that it allows researchers to compare many different variables at the same time. ... However, in a longitudinal study, researchers conduct several observations of the same subjects over a period of time, sometimes lasting many years.
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Time series is strong stationery given that unconditional joint probability distribution does not change when shifted in time, this means that the distribution is the same through time. In weak stationary, a time series is when the mean and the variance are not constant through time. They keep changing with time.