Statistical Analysis: Quebec Referendum Vote

In summary: This suggests that the t-test is the appropriate test to use for the data.In summary, using the t-test, it can be concluded that there is a significant difference in the support for Quebec independence between francophones and anglophones.
  • #1
vj9
14
0
Hello All,

I am stuck on this statistical Analysis. I am wondering which statistical method to use, any suggestions and why?

The scenario is as follows

Since the 1960s there has been an ongoing campaign among Quebecois to separate from Canada and form an independent nation. Should Quebec separate, the ramifications for the rest of Canada, American States that border Quebec, the North American Free Trade Agreement and numerous multi-national corporations would be enormous. In the 1993 elections the pro-sovereigntist Bloc Quebecois won 54 of Quebec’s 75 seats in the House of Commons. In 1994 the separatist Parti Quebecois formed the provincial government in Quebec and promised to hold a referendum on separation. As with most political issues, polling plays an important role in trying to influence voters and to predict the outcome of the referendum vote. Shortly after the 1993 federal election, The Financial Magazine, in co-operation with several polling companies, conducted a survey of Quebecois.

A total of 641 adult Quebecois were interviewed. They were asked the following question. (Francophones were asked the question in French). The pollsters also recorded the language (English or French) in which the respondent answered.

If a referendum were held today on Quebec’s sovereignty with the following question, “Do you want Quebec to separate from Canada and become an independent country?” would you vote yes or no?

2 Yes
1 No

The responses were recorded and stored in columns 1 (planned referendum vote for Francophones) and 2(planned referendum vote for Anglophones)

Infer from the data:

a) If the referendum were held on the day of the survey, would Quebec vote to remain in Canada?

b) Estimate with 95% confidence the difference between French and English speaking Quebecers in their support for separation.
 
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  • #2
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  • #3
Hi Enuma,

Thanks for you reply i have a sample data of 641 people which include both francophones and anglophones.

..... Yes ... No

Francophones ... 229 ...286

Anglophones ... 10 ... 116

I just want to know which statistical method to calculate in part A and how to represent this data?

Your answer for b was helpful.
 
  • #4
A t-test isn't appropriate for qualitative data (and it's still qualitative data even if you code it as 0 and 1). You want to look for test and confidence interval procedures for comparing two proportions.
 
  • #5
Hello Statdad,

Can you suggest me which test is better to use?

Thanks
 
  • #6
vj9 said:
Hello Statdad,

Can you suggest me which test is better to use?

Thanks

The policy here is to give guidance, but not to solve the problem for you. Here, you want the test for a single proportion for a) [tex]1-\hat p = \hat q[/tex] and two proportions for b).
 
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  • #7
statdad said:
A t-test isn't appropriate for qualitative data (and it's still qualitative data even if you code it as 0 and 1). You want to look for test and confidence interval procedures for comparing two proportions.
For the record, t-test can be used as an approximation for testing binomial data, especially for a large sample like the OP's.

Using the frequency table above, I compute a chi-squared of 57.77. With d.f. = 1, it's significant at p = 0.001 or better ("critical" [itex]\chi^2\approx[/itex] 11 given p = 0.001).

Assuming unequal sample sizes and equal [unequal] variances, I compute a t ratio of 216 [338], which is significant at p = 0.0005 or better ("critical" t ---> 3.3 as d.f. ---> infinity, given p = 0.0005).
 
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What is statistical analysis?

Statistical analysis is a method used to collect, organize, analyze, and interpret data. It involves using mathematical and statistical techniques to identify patterns and trends in a dataset, and to make predictions or decisions based on the data.

Why is statistical analysis important?

Statistical analysis is important because it allows us to make sense of large amounts of data by identifying patterns and relationships. It also helps us to make informed decisions based on evidence rather than assumptions or biases.

What is the Quebec referendum vote?

The Quebec referendum vote was a referendum held in 1995 in the Canadian province of Quebec, in which the people were asked whether Quebec should become an independent country from Canada.

How was statistical analysis used in the Quebec referendum vote?

Statistical analysis was used in the Quebec referendum vote to analyze voting patterns and trends in different regions of Quebec. This allowed for the prediction of voting outcomes and helped to inform campaign strategies.

What were the results of the Quebec referendum vote?

The Quebec referendum vote had a very close outcome, with 49.42% voting "Yes" for Quebec to become an independent country and 50.58% voting "No" to remain a part of Canada. The "No" side won by a margin of only 54,288 votes.

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