Could you answer this within 30 mins? please To see if attractiveness is related to how often (that is, how many people help) women receive help from strangers, five women of varying levels attractiveness took turns standing next to a stalled car for one hour. Researchers then rated the attractiveness (using an interval scale which ranged from 1=extremely unattractive to 10=extremely attractive) of the women and noted how often strangers offered help to each women. Their data are below. Attractiveness (X) Z score for attractiveness Offers of help (Y) Z score for offers of help 9 +1.49 6 +1.07 7 +0.66 4 -0.27 5 -0.17 6 +1.07 4 -0.58 2 -1.6 2 -1.40 4 -0.27 Mean 5.40 4.4 Standard Deviation 2.42 1.50 1.1 Construct a scatterplot for these data 1.2 Compute the Pearson correlation coefficient between attractiveness and number of strangers offering help. 1.3 According to the calculations, what proportion of variability in Y (offers of help) can be explained by variability in X (attractiveness)? 1.4 Compute the linear equation (a regression equation) that best predicts the help (Y) based on attractiveness (X). 1.5 Plot the line (from the Problem 1.4) onto the scatterplot (from Problem 1.1). 1.6 Based on the equation found in 1.4, how many people would be predicted to help (Y') Jessica Alba whose attractiveness score (X) is 10? 1.7 Compute the standard error of prediction for help(Y) based on attractiveness (X). 1.8 Interpret Problem 1.7 above. What does the value computed tell us about our predictions for the number of people who would offer help? 1.9 Now compute a linear regression equation that predicts attractiveness (X) based on help (Y). 1) Compute the standard error of prediction for attractiveness (X) based on (Y), and 2) compare the value (the results of your computation) with the The standard error of prediction for help (Y) based on attractiveness (X), and explain why they are different when they are both computed based on the same correlation between attractiveness and help