Obesity in children puts them at risk for several serious medical problems

Subject:MathPrice: Bought3

Share With

Obesity in children puts them at risk for several serious medical problems. Some researchers believe that obesity in children is correlated with spending too much time watching television (research hypothesis). Based o this assumption data were collected among 7 boys of the same age (see table below). Compute a Pearson's correlation coefficient and indicate whether the correlation is statistically significant.

 

TV Watching Hours (X)	Weight (lb)  (Y)	X ²	Y²	(X)(Y)
1.5	79				N = 7
5.0	105				Mean of X ( ) = SX/N =
3.5	96				Mean of Y = SY/N =
2.5	83				SX ² =
4.0	99				SY² =
1.0	78				SXY=
0.5	68
SX=	SY=	SX ² =	SY² =	S((X)(Y) =

 

 

Please make a hypothesis: 

 

 

Step 1: Compute the values for X, Y, SX, SY, SX ², SY², and SXY. Fill the numbers in the table. 

 

Step 2: Plug the values from Step 1 into Pearson's correlation formula (see formula attached):

 

Step 3: Find the degree of freedom: df = N - 2 =

 

Step 4: Find the appropriate value of Pearson's r in Table F (alpha = .05) and compare the obtained Pearson's r with table Pearson's r. Which one is greater?

 

            Table Pearson's r = 

 

Step 5: Do you accept or reject the research hypothesis. Explain why. 

 

Explaining Person's r: Correlation Coefficient in SPSS: 

 

Person's r is a measure of association ranging from 0.00, indicating no relationship, to positive or negative 1.00, indicating perfect positive or negative relationships. The output of bivariate correlations is a zero-order correlation matrix, a table that shows the correlations of all possible combinations of variables, including correlations of each variable with itself. In each cell, the value of Person's's r is in the top row, statistical significance in the second row, and the number of cases in the bottom row. The statistical test shows the probability that the variables are significantly correlated in the population. Statistical significance p equals or is less than .05 indicates a significant relationship (a low probability that this relationship occurs by chance). Person's's r applies to interval and ratio, or numerical, variables. 

 

Assignment B: Correlation in SPSS

 

Correlations
		Work Hours off campus	Satisfaction with academic program index	Satisfaction with faculty index	Satisfaction with advisement index	Satisfaction with social integration index
Work Hours off campus	Pearson Correlation	1	-.187**	-.149**	-.081*	.004
	Sig. (2-tailed)		.000	.000	.012	.891
	N	980	969	964	952	947
Satisfaction with academic program	Pearson Correlation	-.187**	1	.656**	.416**	.475**
	Sig. (2-tailed)	.000		.000	.000	.000
	N	969	1097	1087	1074	1068
Satisfaction with faculty	Pearson Correlation	-.149**	.656**	1	.493**	.553**
	Sig. (2-tailed)	.000	.000		.000	.000
	N	964	1087	1092	1077	1069
Satisfaction with advisement	Pearson Correlation	-.081*	.416**	.493**	1	.426**
	Sig. (2-tailed)	.012	.000	.000		.000
	N	952	1074	1077	1079	1066
Satisfaction with social integration	Pearson Correlation	.004	.475**	.553**	.426**	1
	Sig. (2-tailed)	.891	.000	.000	.000
	N	947	1068	1069	1066	1073
*** Correlation is significant at the 0.001 level (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

 

1. Explain the meaning of correlation, including what we mean by positive and negative correlations (direction), strength of correlation, and statistical significance. (Please refer to the PowerPoint lecture notes and the text.)

 

1. Examine the Zero-order Correlation Matrix displayed above, which correlation is the strongest positive association? Explain the meaning of this particular pair of relationship, and its direction, strength, and statistical significance.

 

1. Which correlation is the strongest negative association? Explain the meaning of this particular pair of relationships, and its direction, strength, and statistical significance.