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Recall that: ⚫ Type I error is defined as rejecting the null hypothesis when in fact it should not be rejected (i.e., "false positive," "false alarm," defendant found guilty when in fact innocent). ■ Type II error is defined as not rejecting the null hypothesis when in fact it should be rejected (i.e., "false negative," defendant found not guilty when in fact guilty). Review a classmate's Main Post. In the context of their hypothesis description, discuss what the type I error and type II error would mean, using a decision table as your guide. You can find a link to an example in the discussion resource area. Hello Everyone, I am choosing Mexico as the home country and Colombia as the country to move my business. Row Labels =Colombia NA Count of Importances of friends rating Not at All Important Not Very Important Somewhat Important Very Important =Mexico NA Not at All Important Not Very Important Somewhat Important Very Important Grand Total 1512 7 556 578 223 148 2000 1 466 635 559 339 3512 The count of important of friend's ratings is displayed and I will calculate 'not at all important' and 'not very important." In Mexico-466+635=1101 1101/2000=.55 I predict that Colombia will have a higher proportion than Mexico. For Colombia-556+578=1134 1134/1512-75 HO: p = .55 HA: p > .55 This is a right, single tailed test. There is sufficient evidence to suggest that friends' ratings in Colombia is less important than in Mexico. If I were to fail to reject the null hypothesis that would mean my home country (Mexico) places more importance on friends' ratings. As for my business, the move to Colombia will mean that Friend's ratings are less important and therefore, word of mouth may not mean as much in Colombia.

Recall that: ⚫ Type I error is defined as rejecting the null hypothesis when in fact it should not be rejected (i.e., "false positive," "false alarm," defendant found guilty when in fact innocent). ■ Type II error is defined as not rejecting the null hypothesis when in fact it should be rejected (i.e., "false negative," defendant found not guilty when in fact guilty). Review a classmate's Main Post. In the context of their hypothesis description, discuss what the type I error and type II error would mean, using a decision table as your guide. You can find a link to an example in the discussion resource area. Hello Everyone, I am choosing Mexico as the home country and Colombia as the country to move my business. Row Labels =Colombia NA Count of Importances of friends rating Not at All Important Not Very Important Somewhat Important Very Important =Mexico NA Not at All Important Not Very Important Somewhat Important Very Important Grand Total 1512 7 556 578 223 148 2000 1 466 635 559 339 3512 The count of important of friend's ratings is displayed and I will calculate 'not at all important' and 'not very important." In Mexico-466+635=1101 1101/2000=.55 I predict that Colombia will have a higher proportion than Mexico. For Colombia-556+578=1134 1134/1512-75 HO: p = .55 HA: p > .55 This is a right, single tailed test. There is sufficient evidence to suggest that friends' ratings in Colombia is less important than in Mexico. If I were to fail to reject the null hypothesis that would mean my home country (Mexico) places more importance on friends' ratings. As for my business, the move to Colombia will mean that Friend's ratings are less important and therefore, word of mouth may not mean as much in Colombia.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 18HP
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Recall that: ⚫ Type I error is defined as rejecting the null hypothesis when in fact it should not be rejected (i.e., "false positive," "false alarm," defendant found guilty when in fact innocent). ■ Type II error is defined as not rejecting the null hypothesis when in fact it should be rejected (i.e., "false negative," defendant found not guilty when in fact guilty). Review a classmate's Main Post. In the context of their hypothesis description, discuss what the type I error and type II error would mean, using a decision table as your guide. You can find a link to an example in the discussion resource area.
Transcribed Image Text:Recall that: ⚫ Type I error is defined as rejecting the null hypothesis when in fact it should not be rejected (i.e., "false positive," "false alarm," defendant found guilty when in fact innocent). ■ Type II error is defined as not rejecting the null hypothesis when in fact it should be rejected (i.e., "false negative," defendant found not guilty when in fact guilty). Review a classmate's Main Post. In the context of their hypothesis description, discuss what the type I error and type II error would mean, using a decision table as your guide. You can find a link to an example in the discussion resource area.
Hello Everyone, I am choosing Mexico as the home country and Colombia as the country to move my business. Row Labels =Colombia NA Count of Importances of friends rating Not at All Important Not Very Important Somewhat Important Very Important =Mexico NA Not at All Important Not Very Important Somewhat Important Very Important Grand Total 1512 7 556 578 223 148 2000 1 466 635 559 339 3512 The count of important of friend's ratings is displayed and I will calculate 'not at all important' and 'not very important." In Mexico-466+635=1101 1101/2000=.55 I predict that Colombia will have a higher proportion than Mexico. For Colombia-556+578=1134 1134/1512-75 HO: p = .55 HA: p > .55 This is a right, single tailed test. There is sufficient evidence to suggest that friends' ratings in Colombia is less important than in Mexico. If I were to fail to reject the null hypothesis that would mean my home country (Mexico) places more importance on friends' ratings. As for my business, the move to Colombia will mean that Friend's ratings are less important and therefore, word of mouth may not mean as much in Colombia.
Transcribed Image Text:Hello Everyone, I am choosing Mexico as the home country and Colombia as the country to move my business. Row Labels =Colombia NA Count of Importances of friends rating Not at All Important Not Very Important Somewhat Important Very Important =Mexico NA Not at All Important Not Very Important Somewhat Important Very Important Grand Total 1512 7 556 578 223 148 2000 1 466 635 559 339 3512 The count of important of friend's ratings is displayed and I will calculate 'not at all important' and 'not very important." In Mexico-466+635=1101 1101/2000=.55 I predict that Colombia will have a higher proportion than Mexico. For Colombia-556+578=1134 1134/1512-75 HO: p = .55 HA: p > .55 This is a right, single tailed test. There is sufficient evidence to suggest that friends' ratings in Colombia is less important than in Mexico. If I were to fail to reject the null hypothesis that would mean my home country (Mexico) places more importance on friends' ratings. As for my business, the move to Colombia will mean that Friend's ratings are less important and therefore, word of mouth may not mean as much in Colombia.
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