Assignment:
Part A. Multiple Linear Regression
Task: Evaluate if there is a relationship (predict) between the personal characteristics and the screening tools with weight loss. Prepare a short description of what was done and what you found. IV=independent variable, DV= dependent variable
Using the multiple linear regression procedure in excel conduct a multiple linear regression to predict weight loss using all of the personal characteristic variables (if appropriate).
Follow the guide on page 322 in Munro of how to conduct this analysis and include in your description of what you did, such as the following:
a. Define the hypothesis
b. Describe each variable using appropriate descriptive statistics; no need to recode anything but make sure dummy coding is correct; create a ‘table 1-remember analysis exercise 1'for this step
c. Run bivariate associations (why? need IV by each IV to check for _________)
d. Run the full model (DV and multiple IVs ) -show evidence that you checked assumptions, etc (for this exercise it is ok to enter the selected IVs all at once in one ‘block')
e. Summarize your findings in text.
f. Include the EXCEL output for the model
Example of how results may be written for a multiple linear regression:
Multiple regression (OLS) was used to estimate the ability of gender, head circumference and baby's weight at birth in predicting motor coordination at 2 years of age. Fifteen percent of the variance surrounding motor coordination was explained by gender, head circumference and the birth weight (R2= 0.154). Overall, the model was statistically significant in predicting motor coordination (F = 3.65, p = 0.031). Weight was not statistically significant in the model (p > 0.05); whereas head circumference was statistically significant (t = 2.68, p = 0.01). For every one cm increase in head circumference, motor coordination scores increased by 0.65 points (beta = 0.65). Males were also found to score higher than females. Males scores were .35 points higher (beta=.35, p=.04).