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Text reaction

Hey. What’s up?. What’s your Favorite Color?. Text reaction. By: Jackie, Molly & Franny. History. First text message sent in 1989 Edward Lantz Number read upside down Not popular in 1990’s For hearing impaired Increase in 2000 As addictive as cigarette smoking ?. What We Did.

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Text reaction

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  1. Hey What’s up? What’s your Favorite Color? Text reaction By: Jackie, Molly & Franny

  2. History • First text message sent in 1989 • Edward Lantz • Number read upside down • Not popular in 1990’s • For hearing impaired • Increase in 2000 • As addictive as cigarette smoking ?

  3. What We Did • Text a random sample of 90 people and recorded their reaction to the text • We sent a text asking “ What is your favorite color? This is Franny” • Taking note of the variables : gender, age, time taken to respond, response to the question • Categorized response as following: confusion, answered, no response, other • Our goal was to find out if the way people responded would be different based on their age and gender and on the time of the day we sent the text.

  4. How We Gathered the Data • Generated a list of random numbers on our calculator and use the number to contact the corresponding person in our contact list. • Used only one person’s cell phone to text from to reduce bias • We used numbers from each of our cell phones and each of our parents • We sent out a mass text and recorded how long it takes each person to respond (if it takes over a day we counted it as no response)

  5. How We Gathered the Data (con’t) • We recorded the time of the day we sent the message : morning, afternoon, evening • We recorded the gender and age of the individual • Considered anyone under the age of 35 young; and over 35 as older

  6. Chi-Square Homogeneity Test : Response vs. Age • Ho: The ages are the same throughout the different responses. • Ha: The ages are not the same throughout the different responses. Conditions: • Categorical data 1) chart shows it is categorical • SRS 2) we took an SRS • Cell counts ≥ 5 3) All expected values ≥ 5 Conditions met- X²-distribution-X² test homogeneity

  7. We fail to reject the Ho because the p-value .41 is greater than or equal to alpha = .05 We have sufficient evidence that the ages are distributed the same in each response.

  8. Chi-Square Independence Test :Gender vs. Response • Ho: There is no association between gender and response. • Ha: There is an association between gender and response. Conditions • Categorical 1) Chart shows • SRS 2) We took an SRS • Cell count ≥ 5 3) expected cell counts all ≥ 5 Conditions met- X²-distribution-X² test independence

  9. We fail to reject the Ho because p-value .77 is greater than alpha = .05. • We have sufficient evidence that there is no association between gender and type of response.

  10. Confused confused answered

  11. Chi-Square Homogeneity Test :Response vs. Daytime • Ho: There is no association between response and daytime. • Ha: There is an association between response and daytime. Conditions • Categorical data 1) chart shows • SRS 2) we took an SRS • Cell counts ≥ 5 3) all expected values ≥5 Conditions met- X²-distribution-X² test independence

  12. We fail to reject the Ho because the p-value .56 is greater than alpha .05. We have sufficient evidence that there is no association between response and daytime.

  13. Bar Graph : Difference in # of people in each gender texted Females: 56% Males: 44%

  14. Bar Graph: Frequency of sex and age group Females: Young- 48% Old- 52% Males: Young-52.5% Old- 47.5%

  15. Pie Chart: Distribution of responses of our sample

  16. Histogram: Display the time taken to respond to the text Shape : Unimodal Center: 147 Spread: (2,298)

  17. Bar Graph: Frequency of Responses at different parts of the day

  18. Sources of Error/ Bias • We only have peoples’ numbers from ourselves and family • Don’t have all ages • Texts might not go through • People may not have their phones on them • People may not have texting

  19. Conclusion/Personal Opinions • We found that the response we got from the people we texted was not dependent on time of day, age, or gender. • We did could not create a sufficient conclusion based on the data we collected. • It was boring waiting for people to respond • It was awkward texting our parent’s friends • We’re mad we didn’t come up with any conclusions

  20. Application to Population • Most people will respond to a random text message with a confused response such as “what?” or “huh?” or “why?” • People normally do not get random texts messages asking them “what’s there favorite color?” • Usually people text their friends or family • Using this example to conclude how people respond to text messages is not very adequate

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