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Statistical Questions

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Statistical questions
1. In statistics, what is a time series?
A time series is a series of points of data that are indexed in the order of time. The data
points are spaced equi-distant and in a successive manner. A typical graphical representation of
time series is a line chart (Anderson, and Stanley, 23).
2. How are time series and forecasting related? Where does regression analysis come in?
Data that is used to make graphical representation in a time series is usually collected
over a period of time hence it is usually sequential in nature. Once the graphs are developed, they
must be analyzed to draw conclusion and one of the major goals of this time series analysis is
predicting the behavior of the series based on the past data through extrapolation of the series.
When single time series or multiple dependent time series at different points in time need to be
compared against each other, regression analysis is the tool often applied as it tests theories that
revolve around the effect of current values of one or more independent time series on the current
value of another time series (Anderson, and Stanley, 32).

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3. What is deseasonalization? When is it useful to deseasonalize data?
Deseasonalization of data entails the removal of patterns from a time series data once the
data is released from the public databases. Thus, the data that has been removes is referred to as
deseasonalized data or seasonally adjusted. It is important to deseasonalize data in the case
variations occur at regular intervals within a year (Wilks and Samuel, 56). For instance, intervals
such as quarterly, monthly or weekly and such seasonality is usually driven by factors such as
holidays, weather amongst others.
4. What is regression analysis?
A regression analysis is used in statistical modeling to estimate the relationship between
variables. Specifically, the analyses helps one the changes that occur in a typical value of a
dependent variable when one of the independent values is altered with (Anderson, Gupta and
George, 63)
5. What is the difference between simple linear regression, and multiple linear
regressions?
Linear regressions are used when modeling how a dependent variable relates with one or
more explanatory variables using a linear function. Multiple linear regressions, on the other
hand, occur when a linear relationship exists between two or more explanatory variable with the
dependent variable (Anderson, Gupta and George, 69).
6. For a simple linear regression, what is the importance of the slope?
The slope in a regression equation, together with line intercept, is known as regression
coefficients. Slope indicates the mean change in value of Y for every change in X hence it

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Surname 1 Name: Institution: Professor: Date: Statistical questions 1. In statistics, what is a time series? A time series is a series of points of data that are indexed in the order of time. The data points are spaced equi-distant and in a successive manner. A typical graphical representation of time series is a line chart (Anderson, and Stanley, 23). 2. How are time series and forecasting related? Where does regression analysis come in? Data that is used to make graphical representation in a time series is usually collected over a period of time hence it is usually sequential in nature. Once the graphs are developed, they must be analyzed to draw conclusion and one of the major goals of this time series analysis is predicting the behavior of the series based on the past data through extrapolation of the series. When single time series or multiple dependent time series at different points in time need to be compared against each other, regression analysis is the tool often applied as it tests theories that revolve around the effect of current values of one or more independent time series on the current value of another time series (Anderson, and Stanley, 32). Surname 2 3. ...
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