We Helped With This R Language Programming Assignment: Have A Similar One?

Objective

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In this exercise we look at multiple predictor linear regression models for a continuous quantitative dependent (response) variable of interest as a function of a multiple quantitative independent (predictor, explanatory) variables. We visualize the potential linear relationship between variables using scatterplots with the dependent variable on the vertical axis and the independent variable on the horizontal axis for all combinations of dependent and independent variables. We also determine the strength and the direction of that linear relationship in the correlation model by computing a correlation between the dependent and independent variables. As in a previous unit on ANOVA we will partition the variation in the dependent variable into a regression model (explained) variation, and an error or unexplained variation. The associated ANOVA table will give us the explanatory power of the model as the coefficient of multiple determination unadjusted and adjusted for degrees of freedom, or R-squared as before. We will interpret the software computed statistical significance of the model parameters (intercept and slopes) and compare those p-values to the desired level of significance.

MUST USE R  and attach file accordingly for the following two problems

PROBLEM 1( Attached RISKFACT data in an excel spreadsheet)

Using R software perform an analysis using a sample of size 30 and an analysis using the entire dataset. Include scatterplots, correlation between all variables, regression analysis including ANOVA table. Discuss the predictive power (coefficients of multiple determination) and statistical significance of all parameters using the ANOVA table, and statistical significance of the individual model parameters (intercept and slopes) assuming a level of alpha of 0.05.

Refer to the data on cardiovascular risk factors (RISKFACT). The subjects are 1000 males

engaged in sedentary occupations. You wish to study the relationships among risk factors in this

population. The variables are

Y = oxygen consumption

X1 = systolic blood pressure (mmHg)

X2 = total cholesterol (mg/dl)

X3 =  HDL cholesterol (mg/dl)

X4 = triglycerides (mg/dl)

Select a simple random sample from this population and carry out an appropriate statistical

Analysis.

MUST provide  a narrative report of your findings in a word doc

PROBLEM 2 ( Attached NCBIRTH800 data in an excel spreadsheet)

And using the data from NCBIRTH800, perform the following analysis including your analysis:

a. Fit a multiple predictor linear regression model using variable tgrams as the dependent (response) variable and variables weeks and gained as the independent (predictor, explanatory) variables

b. Comment on the average change in tgrams per week and per gained.

c. Repeat part (a) but add qualitative (categorical) independent variables drink and smoke (chapter 11).

d. Discuss the differences on tgrams that variables drink and smoke have considering the different combinations of drinking and smoking or not.

e. Include scatterplots, correlation between all variables, regression analysis including ANOVA table. Discuss the predictive power (coefficient of multiple determination) and statistical significance of correlations and of the model parameters (intercept and slopes) assuming a level of alpha of 0.05.

MUST provide  a narrative report of your findings in a word doc