positive polarity are in a bold green font. T-statistic results with strong correlation and an negative polarity are signified with a bold red font. T-statistic results with moderate correlation and a positive polarity are in a bold blue font. And finally, t-statistic results with moderate correlation and a negative polarity are signified with a bold orange font. One final note, I used the data on the tab to model all results “BFS” except for Remedies (RE). When Remedy parameters were the y variable I used the excel file tab No Zero” data to model the “BFS results. After all, it does not make much sense to find correlation to remedies that people have not tried (a response means people did “0” not try the remedy). Hence, the data within the No Zero” tab is “BFS the same as the data on the tab except responses to “BFS” “0” Remedy questions were omitted from the data. But, it is important to keep in mind, the model results of RE parameters using the No “BFS Zero” tab will result in fewer data points (smaller sample size, n) in the model. For this reason, the results from these models may prove to be less conclusive because the data size is in some cases significantly smaller. Hence, when evaluating the data models for RE correlation pay close attention to the sample size. When Remedies (RE) are grouped together as the x variables, I use the data on the tab to “BFS”
positive polarity are in a bold green font. T-statistic results with strong correlation and an negative polarity are signified with a bold red font. T-statistic results with moderate correlation and a positive polarity are in a bold blue font. And finally, t-statistic results with moderate correlation and a negative polarity are signified with a bold orange font. One final note, I used the data on the “BFS” tab to model all results except for Remedies (RE). When Remedy parameters were the y variable I used the excel file tab “BFS No Zero” data to model the results. After all, it does not make much sense to find correlation to remedies that people have not tried (a “0” response means people did not try the remedy). Hence, the data within the “BFS No Zero” tab is the same as the data on the “BFS” tab except “0” responses to Remedy questions were omitted from the data. But, it is important to keep in mind, the model results of RE parameters using the “BFS No Zero” tab will result in fewer data points (smaller sample size, n) in the model. For this reason, the results from these models may prove to be less conclusive because the data size is in some cases significantly smaller. Hence, when evaluating the data models for RE correlation pay close attention to the sample size. When Remedies (RE) are grouped together as the x variables, I use the data on the “BFS” tab to
A_Statistical_Analysis_of_Benign_Fasciculation_Syndrome.docx