Section 39 Simple Linear Regression Model: Exercise

39.1 Exercise 1:

Blood pressure of individuals

BP.csv
Fit a simple linear regression model of SBP with Age. Use the lm function to fit the model and discuss the anova and summary outputs. Also check the diagnostic plots for model assumptions.
Using the lm object, create your own customised R function that will return both the anova table and summary table. The summary table should include estimates of mean, SE, 95% CI along with t-statisic and corresponding two-tailed probability.
Use the formula discussed earlier to develop your own my.lm function to fit a simple regression model. The function will take the x and y vectors through its arguments, fit a simple regression model and provide anova and summary outputs.

The colony forming unit counts of bacteria grown at first 24 hours: bacteria.csv

bacteria.csv
The data include counts of colony forming unit (cfu) of a bacterium species recorded at each hour.
Fit a simple linear model of cfu data with hour.
Present the estimated mean, SE and confidence intervals.

Spending on different forms of advertisments and sales: Advertising.csv

Advertising.csv
Data show the spending on different forms of advertisments and sales for 200 companies.
Using a simple linear regression model, assess if the TV advertisement has contributed to sales. Use the lm function to fit the model and discuss the anova and summary outputs. Also check the diagnostic plots for model assumptions.

Credit card balance of individuals: Credit.csv

Credit.csv
Data include the credit card balance of 400 individuals and their personal profiles.
Using a simple linear regression model, evaluate if the balance depends on the age and income of individuals. Use the lm function to fit the model and discuss the anova and summary outputs. Also check the diagnostic plots for model assumptions.
Evaluate if the balance is different between the levels of Gender.