Question

Correlation and Linear Regression Suppose you are conducting market research for a breakfast cereal manufacturer to understand whether individuals are likely to buy a new cereal. You conduct a survey among a sample of randomly selected individuals and using a series of questions. You produce a rating score for each participant for which higher scores reflect a higher likelihood of that participant buying the new cereal. You use a statistical computing program, such as SPSS, to compute the correlation between the rating score and other demographic variables, including gender, age, and income. Use the information from the following correlation matrix to answer the following questions. Use a significance level of a = .05. (Note: Gender is coded such that 0 - male and 1 - female.) Correlations 028 income 190 115 70 .226 rating 582 000 70 ..482 000 70 017 887 70 060 gender age gender Pearson Correlation 1 263 Sig (2-tailed) N 70 70 age Pearson Correlation 263 1 Sig (2-tailed) 028 N 70 70 income Pearson Correlation 190 226 Sig (2-tailed) 115 060 N 70 70 rating Pearson Correlation - 582 482 Sig (2-tailed 000 000 N 70 70 - Correlation is significant at the 0.05 level (2-tailed) Correlation is significant at the 0.01 level (2-tailed) Courtesy of IBM 70 1 1 70 017 897 70 70 Your sample consists of participants, The Pearson correlation between rating and gender is statistically significant (p ). The estimated correlation is suggesting that have a higher likelihood of buying the new cereal. This computed correlation coefficient between rating and gender is also known as a The Pearson correlation between age and is also statistically significant (p - estimated correlation is suggesting that older people ). The

Answer 1