AP Statistics Communist Revolution By Ian Adams. Introduction Question: Is it possible, using secondary data about communist revolutions to predict when.

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Presentation transcript:

AP Statistics Communist Revolution By Ian Adams

Introduction Question: Is it possible, using secondary data about communist revolutions to predict when the next communist revolution will occur?

Data Collection The events came from Wikipedia however was followed up and fact checked and was accurate. I organized each event by their date from oldest event to most recent even. I organized the dates from after the communist ideas were designed by Karl Marx in From that I figured out the time each event happened from the pervious one.

Data Collection (Continued) From the table of events and the time from the previous event. I counted how many times each number of years since the previous revolution occurred from 0 years - 46 years.

Data analysis The X axis is represented by the dates of each event and the Y axis is represented by number of years from previous event. This gives a clear visual of how often or little communist revolutions happened

Data analysis continued The years after previous revolution is on the X axis while the number of years from previous revolution is on the Y axis. The number of times a number of years from a previous revolution occurred is tallied which is what makes the bar graph

Inference (PANIC)

Interval (From PANIC) Step 1 Find the mean Step 2 Compute standard deviation Step 3 Compute Standard error Step 4 Compute Margin of Error Step 5 Compute confidence interval

Conclusion (From PANIC) From the results of the data we are able to estimate that every to years there should be a communist revolution.

Summary In conclusion this study has been complex and interesting however happens to have limitations and parts of which the data is not accurate. Since all the assumptions were not met this would make the data collected not at all accurate. In future studies and experiments try to use procedures that could more accurately represent the data collected.