ransmission of certain disease microorganisms is a function of how well they can survive during an aerosol dispersal event (e.g. coughing and sneezing produce aerosols containing disease organisms). Scientists are interested in the relationship between the time a disease organism spends in an aerosol and the percentage of organisms that remain alive. Data for minutes spent in an aerosol and percent alive forPseudomonas aeruginosaare given below:

Mean aerosol time (minutes):22Standard deviation of aerosol time: 6.9

Mean organism viability (percent alive): 26.7

Standard deviation of organism viability: 5.29

Correlation coefficient: -0.61

(give answers to4places past decimal)

1. The least squares regression model for predicting organism viability from aerosol time is:

y =_____+_____x

2. In this regression analysis, organism viability is the:

A)intervening variable

B)independent variable

C)response variable

D)usually designated by x

3. What fraction of organism viability is not explained by least squares regression model?

4. The predicted percentage of Pseudomonas aeruginosa that will remain alive after having been suspended in an aerosol for 6 minutes is:

5. If the total sum of squares (SST) is 102204, the error sum of squares (SSE) is:

6. The data in the table above were forPseudomonas aeruginosa. The same variables were measured for a different speciesPseudomonas mendocina, and the corresponding regression model had r²= 0.62. Which regression model will yield more reliable predictions?

A) The Pseudomonas aeruginosa model, because its coefficient of determination is larger.

B)The Pseudomonas mendocina model, because its coefficient of determination is smaller.

C)The Pseudomonas mendocina model, because its coefficient of determination is larger.

D)The Pseudomonas aeruginosa model, because its coefficient of determination is smaller.