Estimate the correlation coefficient

Reference no: EM1314104

Percent of school children receiving free or reduced-fee lunches at school (variable p-RFM) and percent of bicycle riders wearing a helmet (variable p-Helm). Data for this study was recorded by field observers in October of 1994.

i	School	P-RFM	P-Helm
1	Fair Oaks	50	22.1
2	Strandwood	11	35.9
3	Walnut Acres	2	57.9
4	Discov. Bay	19	22.2
5	Belshaw	26	42.4
6	Kennedy	73	5.8
7	Cassell	81	3.6
8	Miner	51	21.4
9	Sedgewick	11	55.2
10	Sakamoto	2	33.3
11	Toyon	19	32.4
12	Leitz	25	38.4
13	Los Arboles	84	46.6

Above table liats data from a cross sectional survey of bicycle safety. The explanatory variable is a measure of neighborhood socioeconomic status (variable P-RFM). The response variable is "percent of bicycle riders wearing a helmet" (P_Helm)

a. Construct a scatter plot of P-RFM and P-HELM. If drawing the plot by hand, use graph paper to ensure accuracy. Make sure you label the axes. After you have constructed the scatter plot, consider its form and direction Identify outliers if any

b. Calculate r for all 13 data points. Describe the correlation strength

c. A good case can be made that observation 13 (Los Arboles) is an outlier. Discuss what this means in plain terms

d. In practice, the next step in the analysis would be to identify the cause of the outlier. Suppose we determine that Los Arobles had a special program in place to encourage helmet use. In this sense, it is from a different population, so we decide to exclude it from further analysis. Remove this outlier and recalculate r. To what extent did removal of the outlier improve the fit of the correlation line?

e. Test Ho: p = o (excluding outlying observation 13)

Reference no: EM1314104

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