[Solution] : Project C Statistics, statistics homework help


QUESTION :

Project C Statistics, statistics homework help

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Question description

Using
MINITAB, perform the regression and correlation analysis for the data on income(Y),
the dependent variable, and credit balance (X), the independent
variable, by answering the following.
1.
  Generate a scatterplot for income ($1,000)
versus credit balance($), including the graph of the best fit line. Interpret.
2.
  Determine the equation of the best fit
line, which describes the relationship between income and credit balance.
3.
  Determine the coefficient of correlation.
Interpret.
4.
  Determine the coefficient of determination.
Interpret.
5.
  Test the utility of this regression model
(use a two tail test with α =.05). Interpret your results, including the
p-value.
6.
  Based on your findings in 1–5, what is
your opinion about using credit balance to predict income? Explain.
7.
  Compute the 95% confidence interval for beta-1
(the population slope). Interpret this interval.
8.
  Using an interval, estimate the average
income for customers that have credit balance of $4,000. Interpret this
interval.
9.
  Using an interval, predict the income for
a customer that has a credit balance of $4,000. Interpret this interval.
10. 
What can we say about the income for a customer that has a credit balance of
$10,000? Explain your answer.In an
attempt to improve the model, we attempt to do a multiple regression model
predicting income based on credit balance, years, and size.
11. 
Using MINITAB, run the multiple regression analysis using the variables credit balance,
years, and size to predict income. State the equation for this multiple
regression model.
12. 
Perform the global test foruUtility (F-Test). Explain your conclusion.
13. 
Perform the t-test on each independent variable. Explain your conclusions and
clearly state how you should proceed. In particular, state which independent
variables should we keep and which should be discarded.
14. 
Is this multiple regression model better than the linear model that we
generated in parts 1–10? Explain.

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