What are some instances of trends and seasonal patterns in time series data?
Trends in time series data show long-term changes or patterns over time, such as increasing or decreasing values. Examples include population growth, stock market trends, or climate change. Seasonality refers to recurring patterns that follow a specific time frame, like daily, weekly, or yearly cycles. Examples comprise retail sales peaking during holidays, temperature fluctuations across seasons, or website traffic spiking during certain times of the day.
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Which data set should I use to apply linear regression?
Choosing a dataset for linear regression depends on your research question and the availability of relevant variables. Datasets with continuous dependent variables and numeric independent variables are ideal. Consider datasets like housing prices with predictors such as square footage, crime rates, or location; stock prices with factors like volume traded and economic indicators; or academic performance with predictors like study hours and GPA.
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