KIN232 Lecture Notes - Lecture 7: Scatter Plot, Linear Regression, Regression Analysis
Document Summary
Correlation strength vs statistically significant correlations: this table allows us to determine the significance of the correlation. This means that 78. 5% of the variance in weight is due to height. The remaining 21. 5% is due to individual variation and might be explained by other factors that were not taken into account in the analysis, such as eating habits, exercise, sex, or age. Example: body fat values are determined by measurement of skinfold thickness. Validity is concerned with the ability of skinfold thickness to determine body fat %. The ppmc is used to represent the strength of this relationship or the ability to accurately predict % body fat from skinfold measurements. Correlation prediction: the statistical procedure for using a relationship to predict score, y =a+bx, x = measurement of x value (iv, a = y intercept, b =slope of regression, y = predicted y value. This would produce a rxx value of 1. 0.
