What extent did removal of the outlier improve the fit

Reference no: EM1317422

Test the significant difference between correlation coefficient.

Bicycle helmet use. Table lists data from a cross-sectional survey of bicycle safety. The explanatory variable is a measure of neighborhood socioeconomic status (variable_RFM). The response variable is percent of bicycle riders wearing a helmet (P_Helm).

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 (i)	School	P_RFM	P-HELM

1	Fair Oaks	50	22.1
2	Strandwood	11	35.9
3	Walnut Acres	2	57.9
4	Disc 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	Lietz	25	38.4
13	Los Arboles	84	46.6

In practice, the next step in the analysis would be to identify the cause of the outlier. Suppose we determine that Los Arboles 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 analyses. Remove this outlier and recalculate r. To what extent did removal of the outlier improve the fit of the Test Ho: p=0 (excluding outlying observation 13).

Reference no: EM1317422

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What extent did removal of the outlier improve the fit : Assume that Los Arboles had a special program in place to encourage helmet use. Elucidate to what extent did removal of the outlier improve the fit.

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