Statistics for Engineering Technology Case Study, UniKL, Malaysia What is the correlation coefficient of the linear regression equation? What does this tell you? Explain
| University | Universiti Kuala Lumpur (UniKL) |
| Subject | Statistics for Engineering Technology |
- What is the correlation coefficient of the linear regression equation? What does this tell you? Explain.
- What is the coefficient of determination? What percentage of the variation in the dependent variable is explained by the independent variable? Is that high or low? What does this tell you about the accuracy of your prediction? Explain.
- Use your model to predict two dependent values when any two independent values are chosen. Explain your finding.
- Write a short introduction that includes;
(a) why did you choose this example/ topic?
(b) what did you expect to find?
(c) have similar studies been done elsewhere? - Once you have collected the data;
(a) record in a table,
(b) state the website/ source and the date you obtained from,
(c) key in your data into statistical software such as MINITAB or EXCEL spreadsheet. - Perform data checking to make sure that the data you want to analyze can actually be analyzed using linear regression.
- Create a scatterplot of this data. Based on the scatterplot,
(a) does a linear relationship appear to exist between the two variables?
(b) If so, is it a positive or negative relationship? Would you expect the slope of the regression equation to be positive or negative? Why? - By using the statistical software MINITAB, fit the scatterplot with a regression line.
(a) Report the parameter estimates (the estimates of the intercept and slope).
(b) What is the regression equation for this data set?
(c) Does the slope match what you expected in Question 4(b)? Is the independent variable (X) useful in predicting the dependent variable (Y)? Why?
(d) Construct a 95% confidence interval for the slope. Interpret the result.
(e) Test at a 5% significance level whether the slope is not zero. Based on the result, decide if there is a useful linear relationship between the independent variable (X) and the dependent variable (Y).
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